594 results for “REA”

StreamClarify is a training-free framework for grounded streaming video question answering. It uses the entropy of a binary pre-answer judgment to decide whether to answer or ask for clarification, and pairs this with a temporal-pyramid KV cache and a CPU-side semantic retrieval archive. The authors build StreamClarify-QA (1,837 QA pairs from StreamBench and OVO-Bench). Macro accuracy rises by up to 8.2 points across three VLM backbones.

arxiv.orgpaperAdded 3 Nov 2026, published 31 Oct 2026🧠 AI

StripWake is a projector-only backdoor attack on large vision-language models. It stays dormant under full-token inference and activates when visual-token compression prunes the context around a trigger (context stripping). On LLaVA-1.5-7B across nine unseen compressors, it reaches 0.4% full-token attack success rate and 94.0%-99.7% compressed ASR on five primary compressors, with no CIDEr loss. Training aligns early visual attention with a frozen benign teacher.

arxiv.orgpaperAdded 3 Nov 2026, published 31 Oct 2026🛡️ Security

JADE (Joint Action DEnoiser) is a diffusion-based vision-language-action architecture in which the VLM reads the current noisy action latents together with images and instructions at every denoising step, and guides the action expert through gated cross-attention. The VLM is also trained to recover clean action latents, and an adaptive step-size schedule cuts sampling steps. On RoboDojo it averages 12.67% success, the highest among π0.5-based methods, and it adds 20.8 points over π0.5 on four real bimanual tasks.

arxiv.orgpaperAdded 3 Nov 2026, published 31 Oct 2026🧠 AI

The paper audits six situational-safety benchmarks for multimodal LLMs and proposes the Benchmark Card, seven measured fields that show whether a SAFE/UNSAFE label score comes from the scene, the request or the benchmark files. MSSBench chat leaks its label through image size, which alone reaches 82.3% accuracy. The Spot-Stop decomposition splits ranking-versus-labelling gaps across fourteen open MLLMs, and label-free recentering improves ranking on VSCBench for 13 of 14 models.

arxiv.orgpaperAdded 3 Nov 2026, published 31 Oct 2026🧠 AI

AgentCap is a runtime authorization layer for LLM agents that moves the authorize decision out of the model and mediates every tool call through an MCP wrapper. It issues short-lived, budget-bounded capability tokens checked at the tool boundary, restricts delegation to attenuating parent-child chains, and uses a declarative policy language. On AgentDojo and InjecAgent it lowers attack success in every tested setting, reaching 0% with GPT-5.4.

arxiv.orgpaperAdded 3 Nov 2026, published 31 Oct 2026🛡️ Security

Callix is a static analysis system that finds access control flaws at sensitive callsites in pre-installed Android apps, including generic APIs such as database reads that become sensitive when they touch protected data. It recovers each security check on a path as an explicit security property and uses a "restriction context" to judge whether the checks suffice. On 1,750 apps from 10 vendors it found 819 vulnerable paths, 42.1% allowing private user data access through generic database operations, with 95.1% precision on a 60% manual sample.

arxiv.orgpaperAdded 3 Nov 2026, published 31 Oct 2026🛡️ Security

Economic-State Manipulation (ESM) in DeFi is studied using 276 real-world attacks and 91 Code4rena audit findings. The authors build a cause-based taxonomy of four categories and eleven subcategories, showing these flaws go beyond price manipulation to accounting, AMM state consistency and execution-time violations. Seven detection approaches were evaluated; the best found only 50.4% of real attacks.

arxiv.orgpaperAdded 3 Nov 2026, published 31 Oct 2026🛡️ Security

The paper treats a benchmark as an empirical measure over item difficulty and formalizes two failure modes: measure mismatch (benchmark difficulty distribution differs from the intended one) and coverage gaps (uncovered difficulty regions that hide localized training progress). It proposes two diagnostic metrics with theoretical guarantees and analyzes 25 popular benchmarks across 7 domains using estimated item difficulties. It also introduces BenchFill, a difficulty-targeted item rewriting algorithm that reduces local coverage gaps.

huggingface.copaperAdded 31 Oct 2026🧠 AI

This paper introduces a grammar-lens benchmark, derived from the theory of computation, that generates novel data at levels of increasing computational power. GPT-5.5 and GPT-5.5-Pro at Medium, High and XHigh reasoning accepted 16, 40, 43 and 52% of test data. As test-time effort rose, model answers diverged more from human answers on the same data. The authors suggest this may reflect a general capability-versus-alignment conflict.

arxiv.orgpaperAdded 3 Nov 2026, published 31 Oct 2026🧠 AI

This paper evaluates fourteen LLMs from six providers on 3,076 questions from MedQA-USMLE, MedMCQA, PubMedQA and MedBullets. It tests standard multiple choice, questions with answer choices withheld, and reconstruction of hidden questions from choices alone. Mean accuracy falls from 82.3% to 50.9% without choices, smaller models trail larger ones by 8.7 points, and the strongest models reconstruct about one question in four.

arxiv.orgpaperAdded 3 Nov 2026, published 30 Oct 2026🧠 AI

ResMem is a method that distills retrieved knowledge into a parametric memory. The LLM reads retrieved text offline to produce teacher targets, and a memory sharing the same backbone learns an additive logit residual through self-distillation. On a Wikipedia subset it cuts teacher-construction storage by 94.7% versus a kNN pipeline. Across five QA datasets, mean token F1 rises from 32.83% to 34.48%, and it breaks 21% fewer correct answers than a kNN-trained memory.

arxiv.orgpaperAdded 3 Nov 2026, published 30 Oct 2026🧠 AI

The paper tests whether LLM monitors can tell reward hacking from honest failure in a sandboxed task where an agent can game a completion score. Across five pre-registered studies on 60 Llama 3.1 8B episodes, small Llama monitors reached AUC 0.64, while Gemini 3.1 Flash-Lite reached AUC 0.80 but never flagged an unnamed exploit. A two-rule deterministic check caught every known exploit. The environment, labels and logs are released.

arxiv.orgpaperAdded 3 Nov 2026, published 30 Oct 2026🧠 AI

Paper implementing four randomized last-level cache designs (CEASER-S, ScatterCache, SassCache, MIRAGE) in a multi-core out-of-order RISC-V SoC on FPGA, evaluated with SPEC CPU 2017 and GAPBS. Pipelining the index derivation function alone is insufficient: three variants lose up to 35% frequency until critical paths are redesigned, leaving them within 10% of baseline. IPC falls up to 5% (SPEC) and 11% (GAPBS); FPGA LUTs rise 10-35%, ASIC area 1-14%.

arxiv.orgpaperAdded 3 Nov 2026, published 30 Oct 2026🛡️ Security

PrivacyTrace is a benchmark of 1,000 synthetic user profiles and 4,000 agent trajectories across four domains, built with real tools from 25 MCP servers. Four LLM attackers inferred user attributes from tool-call traces alone at a 26.25% success rate, against 17.97% for prior-based guessing. Tool arguments and returned results gave the main evidence, and linking traces across sessions raised the risk.

arxiv.orgpaperAdded 3 Nov 2026, published 30 Oct 2026🛡️ Security

PrivacyTrace is a benchmark from the Shanghai AI Laboratory (AgentDoG Team, October 2026) that tests how much a passive observer can infer about a user from MCP tool-call traces alone. It has 1,000 synthetic profiles, 4,000 trajectories and 67,950 calls across 25 MCP servers. Four LLM attackers reached 26.25% attribute accuracy against a 17.97% prior baseline, and tool arguments leaked most.

arxiv.orgpaperAdded 3 Nov 2026, published 30 Oct 2026🧠 AI

LOCOGEN is a sampling-time guidance method for diffusion models that design regulatory DNA sequences. It learns an invariant motif mask through sequence-level invariance learning, protects motif positions, and applies out-of-distribution damping only to background positions, with no retraining of the frozen backbone. On HepG2, K562 and SK-N-SH it reaches a region of the property, novelty and plausibility space that no baseline reached. Code is released.

arxiv.orgpaperAdded 3 Nov 2026, published 30 Oct 2026🧠 AI

GPT-Rosalind-6 System Card Addendum is OpenAI's safety documentation for a life-sciences model that shares weights with GPT-6 Astra. It rates the model High in Biological and Chemical and Critical in Cybersecurity under the Preparedness Framework. It reports biology evaluations, trusted-access controls, biosecurity refusal pass rates, a historical cyber jailbreak regression (14.3% task success), and sabotage and monitor-evasion results.

deploymentsafety.openai.compaperPublished and added 30 Oct 2026🛡️ Security

GPT-Rosalind-6 System Card Addendum describes OpenAI's life-sciences reasoning model, which uses the same weights as GPT-6 Astra and is offered only through trusted access. It reports life-science benchmark gains over GPT-Rosalind-5.5 (MedChemBench v7 49.6% vs 28.9%), High-capability Biological and Chemical Preparedness results, and below-threshold Critical bio design evaluations. It also covers access controls, refusal evaluations, cyber jailbreak regression and alignment tables.

deploymentsafety.openai.compaperPublished and added 30 Oct 2026🧠 AI

world2vec is a method for learning mechanistic world models from observations and actions alone, without reward information. Its latent dynamics reproduce the true system over long prediction horizons using at least one order of magnitude fewer parameters than DreamerV3, TD-MPC2 and LeWM. The authors report state-of-the-art control of continuous environments from pixels and accurate system identification across a benchmark of 71 attractors.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

PhysCount is a benchmark of 546 real-world images and 4,595 human-written questions in 651 target-specific groups. It tests whether multimodal LLMs can separate visible appearances (direct view, reflections, glass) from the physical entities behind them. Across 40 MLLMs, counting appearances is much easier than linking them to entities, and the best model answers every question correctly in only 13.36% of groups.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

Glance is a RAG method in which the generator also reranks. It adds a lightweight decision adapter and pointer head that score candidate passages from one shared prefill, so a single resident 9B backbone does generation and reranking. After 39 minutes of MS MARCO fine-tuning it reaches nDCG@10 of 0.741 on SciFact and 0.345 on NFCorpus, against 0.721 and 0.326 for bge-reranker-v2-m3. On 300 HotpotQA questions it improves answer F1 by 0.045 over cross-encoder-selected context. Evaluation covers two retrieval datasets, one QA dataset and one backbone family.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

On-policy state training is a method for training chain embedding retrievers for multi-hop RAG on the states the encoder itself reaches, including those built on its own wrong retrievals. Each state is labeled with the earliest missing gold passage, other missing gold passages are masked, and updates are contrastive and mixed with teacher-forced states. It retrieves complete evidence more often than teacher-forced training and graph-based systems, and transfers to harder long-chain HotpotQA and 2WikiMultiHopQA variants the authors built.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

CEDAR (Clinical Evidence-Driven Agent for Risk-prediction) is an evidence-citing, self-auditing LLM pipeline for predicting pediatric diabetes outcomes at 24 months from 18 months of records. On 2,489 youth with type 2 diabetes and 4,018 with type 1, pooled AUROC was 0.815 versus 0.656 to 0.700 for trained ML in T2D, and 0.756 versus 0.603 to 0.631 in T1D. Only 4 of 70,246 cited values mismatched the chart. Two pediatric endocrinologists matched its predictions in about nine of ten cases.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

EpiCoach is an agentic system that treats one cross-country mountain biker as an N-of-1 experiment. Claims live in an evidence-gated hypothesis registry, and each must make a probabilistic prediction before a session. Predictions come from a digital twin fitted to heart rate, GPS and altitude. In a replay over eighteen months the twin beat standard forecasters on heart rate but not segment time, and the agent loop matched a static model. In the first three live weeks, the ledger sealed 20 predictions and scored 14, and a safety officer vetoed the agent twice. The registry, benchmark and protocol are released.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

CORAL (Cognitive Resource Self-Allocation) is an agent-callable working-memory toolset that lets an LLM agent checkpoint task progress and verified facts, then reset its cluttered context and resume from a clean state. A Multi-episode Agentic Reinforced Policy Optimization algorithm trains the checkpointing. With an 8B model it reaches 42.8% average accuracy on GAIA, uses 6-7x fewer input tokens than a ReAct agent, and adds 8.0 points to a proprietary agent on BrowseComp.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

MAServeBench is a benchmark that treats multi-agent collaboration as a configurable dimension of LLM serving workloads. Its composable stages model how agents coordinate and exchange context in documented multi-agent workflows, and Hierarchical Semantic Traces record operations and dependencies for reproducible comparisons. Task-linked measurements tie workloads to GPU/NPU behavior, showing serving efficiency does not always mean timely task completion. Code is released.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

A five-task benchmark tests multimodal LLM color judgment against CIE colorimetric ground truth: AWB pair judgment, skin-tone bias direction, CCT estimation, Delta E 2000 pair ordering, and color naming under changing illuminants. Eight endpoints were tested. On ColorChecker-24 stimuli, closed frontier models reach CCT MAE of 715-1056 K versus 1358-2133 K for open-weight models, and a non-ColorChecker control shows this gap is partly template-dependent.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

MedCBR is a two-stage alignment framework for medical vision language models. Difficulty Weighted Boundary Preference Optimization (DBPO) trains on counterfactual preference pairs at the model's capability frontier. Entropy Gated Grounded Policy Optimization (EGPO) then applies distilled clinical rubrics according to prediction entropy. It reaches 61.2% average accuracy on six medical benchmarks, with the largest gains on differential diagnosis, and the two stages combine super-additively.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

SEAL is a defense against indirect prompt injection for local LLM agents. It learns a soft prompt and scoring readout offline, then uses a frozen task model to score and delete injected tokens from tool outputs, with queries built from trusted initial context. On AgentDojo, attack success falls from 34.6% to 0.1% and Joint rises from 51.4% to 77.5%, using about 127x fewer defense tokens than PISanitizer.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🛡️ Security

HO-ASPA (Higher-Order Adjusted Spearman Coefficient Analysis) is a rank-based, non-profiling side-channel distinguisher. The paper derives its optimal form against two-share Boolean masking and inner product masking under Hamming weight and LSB leakage. It finds no significant gap between higher and lower orders for HO-ASPA, unlike HO-CPA, and reports that it beats Kruskal-Wallis-based attacks in simulated and real-world experiments.

eprint.iacr.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🛡️ Security

MetaVul is a loop that rewrites the harness around a single vulnerability-detection agent (Codex CLI, Claude Code) without touching model weights. A sandboxed optimizer reads past harnesses, metrics and labeled predictions, and a fresh detector scores each function. It reports pairwise-accuracy gains of 11.4 to 23.3 points on validation and up to 25.8 points on 430 held-out PrimeVul pairs.

arxiv.orgpaperAdded 3 Nov 2026, published 29 Oct 2026🧠 AI

MetaVul is a self-improving framework that discovers agent harnesses for LLM-based vulnerability detection. Starting from a seed structured reasoning scaffold, it iteratively refines the harness using feedback from paired vulnerable and patched functions, and returns Pareto-optimal harnesses. Tested on a 202-pair validation set and a 430-pair held-out set, it improved pairwise accuracy by up to 23.3 and 25.8 points, and harnesses transferred across models (14.7 points held-out gain).

arxiv.orgpaperAdded 3 Nov 2026, published 29 Oct 2026🛡️ Security

High Precision Noise Cleaning for CKKS is an IACR ePrint preprint (2026/2675) by Zhihao Li, Cheng Hong and Tao Wei. It composes lower-degree Hermite polynomials to reach output noise bounds such as 2^-300 for noise flooding, and uses this to build an asynchronous threshold CKKS scheme with strong simulation security in the random oracle model. In OpenFHE, binary-message cleaning falls from 9.63 s to 1.54 s.

eprint.iacr.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🛡️ Security

REALM (Required Execution Assets Linked to a Mission) is an approach to applying least privilege to LLM agents by identifying the assets a task needs, including ones discovered at runtime. It models assets as Dependency Graph nodes, separates discovering an asset from authorizing it, and exposes resource-bound Partial APIs inside a Task Container. On AgentDyn with qwen3.5-flash, attack success rate falls from 16.6% to 0.2%, with 40.0% task completion on clean tasks and 36.4% on injected cases.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🛡️ Security

PrivacyEvalClaw is a tool-augmented deliberative agent that audits privacy leakage in LLM jailbreak responses. The paper is by Krishna Kanth Nakka and co-authors, and it was accepted to AACL-IJCNLP 2026 (arXiv 2610.36711, cs.CR). The tweet gives only the title, authors and venue, so the method and results are not described.

x.compaperAdded 30 Oct 2026, published 29 Oct 2026🛡️ Security

PrivacyEvalClaw is a tool-augmented deliberative agent for auditing privacy leakage in LLM responses produced under jailbreak prompts. By Krishna Kanth Nakka and five co-authors, the paper is on arXiv (cs.CR) and accepted to AACL-IJCNLP 2026. The fetched post gives only the title, authors and venue, so method details and results are not available here.

x.compaperAdded 30 Oct 2026, published 29 Oct 2026🧠 AI

Addendum to the GPT-6 Luna system card for the model update of 29 October 2026 (the title says October 28). OpenAI keeps the High rating for cybersecurity and biological/chemical capability under its Preparedness Framework. It reports ExploitBench 46.19% and SEC-Bench Pro 51.75% (GPT-6 Astra: 85.4%), higher multiturn jailbreak defender success than the October 7 version, and GPT-Red prompt-injection results. It also reports weaker factuality and dual-use biology refusal.

deploymentsafety.openai.compaperPublished and added 29 Oct 2026🛡️ Security

This paper studies whether vision-language models can infer how long a person stays somewhere (living, staying, or leaving) from geotagged social media posts. The authors build a new corpus of geotagged tweets co-annotated by humans and LLMs. State-of-the-art models struggle even with multi-turn Chain-of-Thought prompting, often missing contextual cues and subtle hashtags while over-relying on prominent visual details.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

inkGround is a benchmark for stroke trajectory recovery from full-page, camera-captured handwriting, with 537 page images (about 18.7k words, 163k strokes) and pseudo-label references filtered by OCR agreement and human raters. Models get a page image and a target word, then output ordered stroke points. Seven VLMs were scored with DTW, Chamfer distance, readability and style similarity. Closed-source models, notably Gemini-3.1-Pro, did best, and the ranking held on IAM-OnDB.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

DeoGen is an automatic, parametrizable benchmark generator for deontic reasoning, where a model applies policy rules to facts to reach a verdict. It autoformalizes rules into first-order logic, builds decision trees, and turns each tree path into a benchmark entry. Frontier models give the correct verdict 89-92% of the time, but only 22-40% on entries lacking enough information, even when told to abstain.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

AskStream is a multi-turn benchmark for streaming video understanding that adds a third proactive action, ask, alongside wait and answer, for cases where video or user information is incomplete. It has 438 videos, 2,714 manually annotated frame-level time windows (wait, ask, answer), four task categories and eight subtasks. The strongest model reaches only 52.18% on the proposed Ask-F1 metric, in both multiple-choice and open-ended settings.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

Secure Transaction Verification and Authorization (STVA) is a draft Standards Track ERC (created October 2026) by @MATOBOYCRYPTO65. It proposes structured human-readable metadata and semantic intent for transactions and UserOperations, an independent simulation and verification interface, and scoped, revocable capability tokens for privileged operations. It targets blind signing and frontend supply-chain attacks, citing the February 2025 Bybit Safe multisig theft. The page gives only the abstract, motivation and core concepts.

ethresear.chpaperPublished and added 28 Oct 2026🛡️ Security

Repairs for Whom? studies single-step repairs of failed LLM agent trajectories and asks whether common evaluators (greedy replay, stronger models, oracles, log-reading judges) pick repairs that help the deployed agent. Using independent rollouts in ALFWorld and WebShop, greedy replay loses 0.06-0.13 in expected gain, and Qwen2.5-14B rollouts misselect repairs for Qwen2.5-7B. The authors recommend verifying repairs with the executor that will run them.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

This paper studies data scaling laws in stereo matching and releases XStereo, a realistic synthetic dataset of 200 thousand samples. In-domain training on five large datasets shows two regimes: a standard power law, then saturation, with a transition point roughly fixed for a given model. Cross-domain performance depends only weakly on source data scale, and distribution and coverage diversity reduce domain gaps more.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

AgentAnchor is a session-keyed behavioral watermark that lets an agent owner prove ownership of a trajectory log. Each step key comes from a secret key, a fresh public session identifier and semantic states of recent observations. Parity evidence is embedded through a KL-bounded exponential tilt of action choices. A trusted verifier checks disputed logs without rerunning the agent, using wrong-key calibration and abstention. The paper also formulates a learned cross-session forgery threat.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

CALM is a conflict-aware lifecycle memory governance framework for long-horizon language agents. It turns records into evidence-grounded memory cells, updates their validity and lifecycle states from support, conflict and dependency signals, and admits evidence at query time through training-free or learned gating based on temporal applicability. It beats baselines on LongMemEval-S and LoCoMo with Qwen and GPT backbones, and keeping superseded records helps earlier-state reasoning.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

This paper measures job-based doubt in LLM fact-checkers: judging a claim false more often when the poster has a lower-status job. It compares base and post-trained checkpoints of six model families on fact-checked claims with randomised poster profiles. Only the open Tülu-3 recipe (OLMo-2 32B, Tülu-3 70B) creates the effect, under one percentage point but reliable. In OLMo-2 32B the flip comes from one residual-stream direction and appears at supervised fine-tuning, confirmed on 400 new claims. None of seven commercial models doubts lower-status posters more.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

This preprint benchmarks 13 decision models against 15 generative LLM configurations, including different reasoning settings, on 1,273 text-only four-option MedQA questions. Accuracy ran 67.71% to 89.08% for decision models and 78.32% to 96.86% for generative configurations. Decision models were cheaper and faster, but answers with probability of at least 0.90 still had error rates of 1.71% to 12.77%, and probability repeatability varied across three runs.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

TCC-RAG is a retrieval-augmented generation method for temporal knowledge graph question answering (TKGQA). It turns the temporal knowledge graph into a multi-view causal cue graph, builds a query-specific temporal causal chain to guide retrieval, and applies a causal evidence filter to the results. The authors report gains of 10.72% and 8.40% on two TKGQA benchmarks and 62.97% on multi-step temporal reasoning.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

FlatCake is a KV cache storage architecture for LLM serving that treats host DRAM and NVMe SSD as peer media in one 64-bit address space, instead of a DRAM-over-SSD tier. Blocks are placed once, in session-contiguous, layer-major order, and streamed via GPUDirect Storage. On a Claude Code trace with DeepSeek-V4-Flash it holds a 94% hit rate (Mooncake: 64%) and cuts peak-load mean TTFT 1.6x.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

The paper studies detection and temporal localization of fraudulent inducements (prompts to follow, join chat groups, click links) in internet finance short videos. It compares OCR+LLM, OCR+MLLM and a zero-shot MLLM, and builds a dataset of 2,419 videos (66.49 hours, 3,194 events, four categories). OCR+MLLM reaches mIoU 26.52 versus 14.49 for keyframe OCR+LLM, with 67.0 percent macro recall, using a recall-weighted F2 localization reward.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

This paper treats LLM reasoning effort as a credence good and measures the counterfactual effect of effort levels on proprietary and open-weight models across four benchmarks, including long-horizon agentic tasks. Higher effort often fails on tasks lower effort solved, and on easy tasks the top level costs up to 42x the lowest. Starting at the lowest effort and escalating only after a verified failure matches or beats fixed top effort, usually at lower cost for proprietary models.

huggingface.copaperAdded 28 Oct 2026🧠 AI

VulnLens is a framework for binary C/C++ function-level vulnerability detection. A label-blind LLM Explainer describes each function from seven fixed perspectives (data flow, memory access, object lifetime, arithmetic and type behavior, control flow, shared state, external-resource access). A supervised Detector fuses code and explanations by cross-attention. It reaches 75.5% accuracy and 77.2% F1 on a 526-function PrimeVul test set, and 66.4% accuracy on SecVulEval without retraining.

arxiv.orgpaperAdded 30 Oct 2026, published 27 Oct 2026🛡️ Security

Locating Sycophancy Across Training Stages in Open Language Models measures sycophancy in 13 open checkpoints from 5 lineages, from base model through supervised fine-tuning and preference optimization to release. It finds two behaviors with different origins: deference to a claim in the prompt exists in every base model, while capitulation under user objection is largely absent from base models and present in every post-trained checkpoint. Post-trained models often drop firmly held answers as readily as loosely held ones when the objection names an alternative.

arxiv.orgpaperAdded 30 Oct 2026, published 27 Oct 2026🧠 AI

Heads of Security (HoS) is a prompt injection defense that enforces instruction-data separation with learned per-head attention gates, redistributing attention between trusted and untrusted tokens. Evaluated on Llama-3.3-70B-Instruct and GPT-OSS-20B against SecAlign++ and ASIDE. The black-box RL-Hammer attack reaches 97% ASR on GPT-OSS-20B and 85% on SecAlign but 10% on HoS. The white-box Claudini attack needs 3 times more FLOPs on HoS. HoS keeps base-model utility in few-shot settings, where SecAlign and ASIDE degrade.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🛡️ Security

Heads of Security (HoS) is a prompt injection defense that trains a small set of per-head attention gates to shift attention between trusted and untrusted tokens, separating instructions from data. Tested on Llama-3.3-70B-Instruct and GPT-OSS-20B, the black-box RL-Hammer attack reaches 10% ASR on HoS versus 97% on base GPT-OSS-20B and 85% on SecAlign. HoS keeps base utility in few-shot settings, where SecAlign and ASIDE degrade.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

LongChang is an automated framework that builds simulated worlds with altered physical laws, such as heavier objects falling more slowly, to test whether LLMs can find the law by free exploration. Explorer LLMs manipulate objects and build tools without tasks or hints, while separate LLM agents generate scenes, review tool designs and grade reports. Across 12 LLMs, GPT-6 Astra reached a 16.7% discovery rate and the other 11 scored 0%.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

SynthAPI is a pipeline that generates synthetic tool-use environments with explicit control over diversity of tools, tasks and underlying databases. Across 458,752 tasks and 849 trained models of four sizes, the authors fit a scaling law predicting downstream agent generalization. At larger data budgets, scaling tool and database diversity beats adding more tasks, and higher complexity helps more. Agents trained this way beat prior synthetic-data baselines on four benchmarks.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

This paper presents an annotated egocentric tennis dataset (egocentric video synced with five allocentric views, global positions, contact times, stroke types) and evaluates image encoders, video encoders and vision-language models on seven prediction tasks. Best models reach 0.14 m player and 0.54 m ball localization error and 0.14 s contact-time error. Depth input helps 3D ball localization, and longer histories do not help forecasting. A conditional diffusion model generates whole-body tennis motion in real time.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

VideoLVR is a method that lets video multimodal LLMs build latent visual evidence from local regions at relevant moments and reason over it interleaved with text tokens. A visual token prediction objective, defined over a vocabulary derived from the input video, supervises the latents. It extends to reinforcement learning with sampled visual tokens as actions and final-answer rewards, and improves fine-grained video benchmarks without much context growth.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

NEXUS is a hierarchical reinforcement learning framework that pairs neural skills with symbolic meta-policies, so transparent reasoning sits at the level of high-level skills rather than atomic actions. It uses object-centric observations and LLM interaction to design reward functions and meta-policies. Agents learn disentangled skills, align better with goals in the game manual, resist reward hacking more, and cope better with game simplifications than deep RL. The paper also compares neural, symbolic and neuro-symbolic meta-policy architectures.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

FeatureArc is a benchmark for coding agents on evolving features, built by a pipeline that mines repository histories into multi-stage tasks across four programming languages. It has 73 manually reviewed tasks of 2 to 10 stages, validated with fail-to-pass and pass-to-pass tests. Across 10 agents, pooled success drops from 62.7% at the first stage to 28.5% for the whole feature. Inherited defects appear in 72.9% of failed later stages.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

AdaRoll-GRPO is a method that allocates GRPO rollout budgets per question by difficulty instead of uniformly. It frames allocation as a sequential decision problem and learns an allocation policy with a Bayes-adaptive MDP, computed offline so training adds no online optimization cost. Experiments on several reasoning datasets and LLM architectures show gains over standard GRPO at the same total rollout budget.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

Preprint on using nitrogen-vacancy (NV) center quantum sensing for electromagnetic side-channel analysis of ICs. The authors build an NV-based RF spectrometer with 1.1 GHz real-time bandwidth and adjustable center frequency, which reads the spectrum in the time domain without Fourier transforms or frequency sweeps. They use it on RSA decryption emanations and for leakage detection on a software Number Theoretic Transform running on a Cortex-M4 microcontroller.

eprint.iacr.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🛡️ Security

OpenGray is a benchmark that adds tasks with known correct decisions to the OpenKBP-Opt head-and-neck dataset, including two overlap contradictions with provably incompatible dose goals and a feasible control. Four planning systems, from a rule-based script to a full LLM agent, were tested on 30 patients with Terra and Ministral 3 8B. Decision accuracy reached 95.4% and 92.1%, yet only 50% and 25% of overlap contradictions were escalated. SAGE logs of 39 radiosurgery runs showed an agent that lacked an action for stopping.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

Tessera is a training-free evidence delivery layer for web retrieval-augmented generation. It rebuilds retrieved pages' HTML into fine-grained evidence units, each completed with page title, section path and table headers, then packs the relevant units into the reader context at the same token budget. Tracing evidence over full English Wikipedia shows chunking loses table and infobox content. Tessera adds 5 to 6 accuracy points on three QA benchmarks and carries over to agentic search.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

This paper tests the delta-learning hypothesis for Direct Preference Optimization (DPO), where the larger model's response in a single model pair is treated as preferred. It shows, in theory and experiments, that fixed pairs add generator-specific biases that predict the preference label regardless of quality. DPO on single-pair data underperforms multi-model data, consistent with reward hacking, and adding more generators partly fixes it without an external judge.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

DCKV is a differentiable codec for the prompt KV cache of hybrid language models, which keep a KV cache only in their few full-attention layers. Transform, quantizer and entropy models (conditioned on prompt tokens and already decoded neighbouring positions) are trained end to end against a frozen model. On Qwen3.5-2B it stores a token in over 55 times fewer bytes than bf16 with at most 1.8 points lost on three long-context benchmarks, and over 100 times fewer with at most 3.6 points lost.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

AlgOssa is a decentralized secure aggregation protocol built on Ossa, a generic committee-aided, single-server construction for one-shot clients (following OPA and Willow, CRYPTO 2025). Ossa realizes secure aggregation with non-selective abort against a malicious server. AlgOssa adds Algorand-style sortition to rotate leaders and committees, bounds the chance of an honest server, and gives eventual delivery. Classical and post-quantum routes are evaluated.

eprint.iacr.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🛡️ Security

What Victims Take Away is an October 2026 arXiv paper analysing 563,349 Tensor Trust attack records to see how players reuse prompt injections they received. Within a week, targets reused 10.0% of attacks that broke their defense, versus 0.03% of matched attacks. Breaches were followed by defense changes that cut same-family attack success by 24.5 points. Received texts made up 23.2% of successful hijacks. The authors propose that AI security tools describe techniques without disclosing full attack text.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🛡️ Security

AK_QMUL's system for the ArGuard-2026 shared tasks fine-tunes JAIS-6.7B with 4-bit QLoRA and a linear head on the last-token representation. For binary safe/unsafe detection (B1) it uses a four-way head over dialect register and AraSafe MSA augmentation, reaching macro-F1 0.984. For seven-way harm domain (B2) it averages five seeds, reaching 0.790. Both ranked first. Bully is the weakest class.

arxiv.orgpaperAdded 28 Oct 2026, published 26 Oct 2026🧠 AI

CTGREP is a compiler-based framework that applies differential analysis across the whole Clang/LLVM compilation chain to find where, how and under which configurations compiler optimizations break constant-time cryptographic code. Applied to seven libraries, three Clang versions, six targets and thousands of configurations, it led to 48 cases fixed with developers. It also identified seven new problematic LLVM passes and bypassed existing compiler-flag mitigations.

eprint.iacr.orgpaperAdded 27 Oct 2026, published 26 Oct 2026🛡️ Security

Log-To-Leak is a prompt-injection attack on tool-using LLM agents that abuses Model Context Protocol tool metadata to trigger an attacker-controlled logging call after the last legitimate call, leaking the user query, server name, server response and agent response. Evaluated on five MCP servers and four agents, it reaches 62.64-100% attack success with 76.55-95.64% logging accuracy. Three defenses leave over 55% residual ASR on GitHub MCP with GPT-4o.

arxiv.orgpaperAdded 28 Oct 2026, published 26 Oct 2026🛡️ Security

REVA (REal-time Vulnerability Analysis) is a vulnerability detector that puts a module threat model, lightweight static analysis findings and call-graph information into one LLM forward pass. A trained probe reads the model's internal representations of the new function. On PrimeVul's held-out test set, REVA on GLM-5.3-Flash reaches 60.0% accuracy in about one second per function, matching the agentic VulTriage (59.2%) at over 250 times the speed and under a tenth of the cost. The probe is 14% to 17% more accurate than the LLM's own verdict.

arxiv.orgpaperAdded 28 Oct 2026, published 26 Oct 2026🧠 AI

REVA (REal-time Vulnerability Analysis) is an LLM-based vulnerability detector that screens each new function in one forward pass. It feeds the model a pre-computed module threat model, lightweight static analysis findings and call-graph data, then reads the model's internal representations with a trained probe. On PrimeVul's held-out test set with GLM-5.3-Flash it reaches 60.0% accuracy in about one second per function, matching the agentic VulTriage (59.2%) while over 250 times faster.

arxiv.orgpaperAdded 28 Oct 2026, published 26 Oct 2026🛡️ Security

LINE-Break is a security analysis of Letter Sealing v2, the custom end-to-end encryption protocol in the LINE messenger. A TLS man-in-the-middle attacker or malicious server can replay, reorder and block messages undetected, and colluding users can forge message authorship in one-to-one and group chats. Stickers and URL previews leak plaintext. The attacks were verified on the real iOS app and an independent implementation.

eprint.iacr.orgpaperPublished and added 26 Oct 2026🛡️ Security

Addendum to the GPT-6 Astra system card for OpenAI's GPT-6.1 Terra, rated Critical in cybersecurity and High in biological and chemical capability under the Preparedness Framework. It reports multiturn jailbreak and prompt-injection results, alignment tests (auto-review circumvention, ExploitGym honeypot, monitor evasion), and cyber scores: 97.10% ExploitBench, 24.5% on the internal recent-vulnerability port, 5 of 22 Sandbox Bench targets.

deploymentsafety.openai.compaperPublished and added 26 Oct 2026🛡️ Security

SentryBench is a benchmark for tool-using LLM agents in public-safety deployments. It scores attack success, over-refusal (missing a real emergency) and over-reaction (unwarranted action) on a cost-sensitive frontier, using real emergency-call transcripts, disaster messages and injection payloads. Across 840 episodes on three frontier models and three defenses, refuse-more caution pushed over-refusal to 100 percent on two models, while a trust-gating wrapper gave the lowest harm.

arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026🧠 AI

SentryBench is a benchmark for tool-using LLM agents in public-safety settings (public alerts, responder dispatch, citizen-record queries). It scores attack success, over-refusal and over-reaction on a cost-sensitive frontier, using real emergency-call transcripts and real injection/jailbreak payloads. Across 840 episodes on three frontier models and three defenses, refuse-more caution raised over-refusal to 100 percent on two models, while a trust-gating wrapper gave the lowest harm on every model.

arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026🛡️ Security

Anis Bkakria's ePrint 2026/2638 gives polynomial-time decision attacks on Known-Covariance Ring-LWE (KCov-RLWE), the assumption Micciancio and Suhl introduced for simulation-secure threshold encryption. The public sample and covariance hint form a bordered Gram matrix whose determinant leaks a residual that differs between real and uniform samples. The attacks cover broad prime-modulus families at ring rank one and transfer to composite moduli with an odd prime divisor. Power-of-two moduli are not covered, and the paper does not break the full threshold-encryption scheme.

eprint.iacr.orgpaperAdded 26 Oct 2026, published 25 Oct 2026🛡️ Security

SymLift is a compile-free vulnerability detection framework for context-incomplete code fragments. A fine-tuned LLM lifts control-flow, data-flow, memory and resource relations into a constrained LLVM-like IR. A parser turns the IR into facts, and Datalog rules derive findings with derivation traces. On Juliet function-level fragments Clang compilation success falls to 12.00%, while SymLift's completion rate drops only 1.17 points. SymLift-Qwen-7B reaches Target-F1 94.41% on FormAI and 91.66% micro-F1 on MegaVul.

arxiv.orgpaperAdded 28 Oct 2026, published 25 Oct 2026🛡️ Security

SymLift is a compile-free vulnerability detection framework for code fragments that lack project context. A fine-tuned LLM reconstructs control-flow, data-flow, memory and resource relations as a constrained LLVM-like IR, and a deterministic parser turns it into facts for Datalog rules that give findings with derivation traces. SymLift-Qwen-7B reaches 94.41% Target-F1 on FormAI and 91.66% micro-averaged Target-F1 on MegaVul.

arxiv.orgpaperAdded 28 Oct 2026, published 25 Oct 2026🧠 AI

Open bilingual (Russian and UK English) benchmark of five open pseudonymisation tools and a regex baseline, plus a new engine built on the two strongest tools. On a held-out set of 1,000 synthetic documents per language, the best existing tools leave a whole personal value visible in 61% (Russian) and 58% (UK) of documents. The engine does so in 0.3% and 0.2%. Russian name restoration reaches only 94.6%, below the 99% threshold. Real documents were not tested.

arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026🛡️ Security

This paper presents an open benchmark of five existing pseudonymisation tools and a regex baseline on Russian and UK English documents, plus an open engine built on the two strongest tools. On a held-out set of 1,000 synthetic documents per language, the best existing tools leave a whole value visible in 58 to 61% of documents. The engine does so in 0.2 to 0.3%. Russian name restoration reaches only 94.6%, below the 99% threshold. Real documents were not tested.

arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026🧠 AI

InteractiveWorld is an environment for closed-loop interaction between an AI assistant and a simulated human agent who sees the scene, follows instructions and acts independently. InteractiveBench, built on it, has 120 multi-turn tasks in six categories scored by verifiable, state-based rewards. Across 12 frontier MLLMs, the best model, Claude Fable 5.1, reaches at most 20% on long-horizon Guidance and 25% on Planning tasks.

arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026🧠 AI

HerHealthGPT is a conversational AI system for women's reproductive health that combines retrieval-augmented generation, context-aware clarification and source validation in a web interface. Evidence retrieval raises concern-category accuracy from 54.1% to 59.3% over fine-tuning alone on 540 messages from 90 cases. The clarification router reaches 85.6% recall and 86.2% specificity. In a 30-adult study, 83.3% preferred the retrieval-enabled version. The authors say clinical validation is still needed.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

DRILS is an LLM-based agentic framework for requirements change impact analysis. It uses multiple agents to generate candidate impact sets, with an LLM-as-a-judge promoting diversity in selection. It adds RE-RAG, a tuned retrieval technique, and a requirement dependency graph as a knowledge source. On three curated datasets it reaches 87.1% recall, 76.3% precision and 81% F1 at lower cost than baselines. The authors release the datasets.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

Intervention-Certified Amplification (ICA) is a validity gate for sigma-weighted hybrid SFT/RL post-training. The paper shows that DIAA, a per-sample sigma-weighted hybrid, emits unexecuted tool-integrated reasoning on 29% of MATH-500 prompts (three times vanilla GRPO) even with Python fences removed from its anchor. ICA gates sigma with paired-rollout evidence, cutting TIR to 13.8% (frozen) and 11.2% (online) and raising accuracy from 47.5% to 48.7% and 49.3%. It transfers to GSM8K.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

Sliding Networks is a family of residual network structures with a joint width and depth scaling recipe that keeps both locally maximal feature learning and feature diversity in the residual stream. The key quantity is the ratio N_flat/(N_sharp L) tending to a constant alpha, and Half Copy correlated initialization can relax the shape constraints. Residual-MLP experiments support an SDE limit, but language-model experiments show performance differences under regrouping at fixed parameter count.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

This paper compares LoRA and full fine-tuning for prompt-safety classification using a matched 400M encoder-decoder pair (Ettin) across three task formats, with up to 15 seeds per cell and pre-specified analyses. Full fine-tuning leads LoRA by 2-3 pp at the pre-specified learning rates, and the encoder gap closes when both use the same learning-rate search. Training LoRA to write a reason after its verdict adds 4.0 pp, while a content-free sentence of equal length does not. Trained models beat a prompted 8B in domain but trail it by 8-23 pp on adversarial jailbreaks and over-flag benign prompts (27-66% against 2-3%).

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

FNO-MoE is a Fourier neural operator with a mixture of experts that predicts the full near-explicit chemical state (Master Chemical Mechanism, thousands of species) over 30 minutes in one call. Expert disagreement selects the most informative box-model simulations, so active sampling beats random or evenly spread sampling at equal cost. Fine-tuning on longer trajectories keeps multi-hour runs stable, and the emulator runs orders of magnitude faster than the numerical solver.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

RALE (Representation-Aware Logical Ensemble) is a method for detecting vulnerabilities in C source code with LLMs trained on different code representations: raw source, Abstract Syntax Trees and Control Flow Graphs. The authors show the representations suit different vulnerability classes, and combine the models with explicit logical rules and majority voting. Single-representation models reach up to 73% F1, while the ensemble reaches 78%.

arxiv.orgpaperAdded 28 Oct 2026, published 24 Oct 2026🛡️ Security

ZipFusion is a lossless KV cache compression scheme whose attention kernel reads the compressed cache directly and rebuilds the original values in registers with simple bit operations, so no separate decompression pass is needed. It also reassigns the memory freed by compression across layers to hold more KV cache. Built on FlashInfer and integrated with SGLang, it gives about 1.25x decode attention speedup on an H100 and up to 26.3% higher decode throughput at 8K context than uncompressed SGLang. It reaches up to 2.76x the throughput of the best lossless KV compression baseline.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

VulDec is a dataset and framework for detecting vulnerabilities in binaries by first decompiling them with fine-tuned LLMs. The dataset aligns real-world Assembly-C function pairs across several architectures with CWE labels. Tuned neural decompilers reach about 56% Edit Similarity and 43% BLEU-4, and tuned detectors reach a macro F1 of 0.61 on a realistic test set with a 3% vulnerability rate.

arxiv.orgpaperAdded 28 Oct 2026, published 24 Oct 2026🧠 AI

VulDec is an arXiv paper (October 2026) presenting a dataset of real-world Assembly-C function pairs with CWE labels across multiple architectures, plus an end-to-end framework that uses neural decompilation for binary vulnerability detection. Tuned LLM decompilers reach about 56% Edit Similarity and 43% BLEU-4. Tuned LLM detectors reach macro F1 of 0.61 on a realistic test set with a 3% vulnerability rate.

arxiv.orgpaperAdded 28 Oct 2026, published 24 Oct 2026🛡️ Security

Extraction meets Distillation is an IACR ePrint paper (2026/2628, CSCML 2026) that combines cryptanalytic weight-direction extraction with label-based distillation to reconstruct fully connected neural networks. It adapts a ReLU extraction implementation to Leaky ReLU and proves conditional bounds for direction recovery and sign selection. On CIFAR-10 victims, reconstructed models reach 56.40% and 58.29% agreement. It separates label requests from privileged access and notes that prediction agreement does not identify parameters.

eprint.iacr.orgpaperAdded 26 Oct 2026, published 24 Oct 2026🛡️ Security

RAQ-MLA (Role-Aware Quantization for Multi-Head Latent Attention Caches) is a training-free method for quantizing the MLA KV cache. It fits a separate invertible transform to the compressed latent (tuned for attention-weighted value error) and to the positional key (tuned for score perturbation). It reaches about 2.5 bits per element, a 6.5x gain in theoretical KV-cache capacity, with near-lossless reasoning and coding results on four MLA models.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

DualPace is an LLM serving runtime that treats the host as an independently scheduled decode engine. A GPU engine runs prefills and GPU-resident decodes, while an AMX-enabled CPU engine batches and advances CPU-resident decodes, optionally calling GPU Dense Assist for dense stages. In decode-oriented scenarios it raises throughput 6.9 to 43.2% over NEO and 11.5 to 90.6% over vLLM at similar latency.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

Gao and Hu's IACR ePrint 2026/2622 shows that if the matrix multiplication exponent satisfies ω ≤ 9/4 − η, heuristic lattice sieving runs in (3/2 − δ)^(n/2+o(n)) time under the spherical heuristic from the BDGL analysis. It builds on OpenAI's recent result that ω ≤ 9/4. With known matrix multiplication algorithms, the method already gives subquadratic search inside BDGL buckets. Only the abstract is available, so concrete parameters are not shown.

eprint.iacr.orgpaperAdded 26 Oct 2026, published 24 Oct 2026🛡️ Security

OWLS (Open-world Language-conditioned Speed Fields) plans directly over a vision-language model's dense feature map. A text prompt describing traversable terrain scores every pixel of one camera image. The scores are projected onto the ground, adapted with two morphological operations, and used as the speed field of a minimum-time planner. It needs no training, terrain labels, map or extra sensors. It was tested on a physical rover, on simulated Mars and Moon rovers over 200 procedural terrains, and on real planetary imagery.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

PHYRD is a living benchmark for theoretical-physics reasoning with 63 Reproduce problems (derive published results) and 43 Discover problems (consequences of modified theoretical conditions). Tasks are built by FormulaForest, with reference solutions and subquestion-level rubrics. Across five models, whole-problem accuracy is 49.21% to 68.25% on Reproduce and 18.60% to 74.42% on closed-book Discover. FormulaForest data also lifts a Qwen-35B-A3B model on FrontierScience from 61% to 72%.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

DeepFaune-UK is a fine-tune of the open DeepFaune camera-trap classifier on 28 UK classes, paired with MegaDetector v6. On 20,212 MammalWeb images it reached 88.8% strict accuracy against 56.5% (DeepFaune v1.5.0) and 59.0% (SpeciesNet). Re-applying the recipe to the DINOv3-based classifier gave 90.6%. Fine-tuning on about 150 crops per class matched full-data out-of-distribution accuracy, and a DINOv2 run took about 22 minutes on under 4 GB of GPU memory.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

UAV-SwarmAgent is a hierarchical multi-agent system where an operator gives one natural-language sentence and a ground-station agent writes and runs code that produces per-UAV routes. A vision-language model grounds targets in an aerial map, and worker agents verify incidents with a compact onboard model. In simulation it located the incident in 244 of 255 missions (95.7%), and flight time followed an Amdahl-type law with speedup bounded at 1.52x for one to five UAVs.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

MCP-Injector is a protocol-aware fault-injection middleware for Model Context Protocol tool calls, with permanent, transient and intermittent faults on seedable schedules. Its fault model comes from 57,240 issue and PR threads across 1,774 open-source MCP repositories. In a 1,350-cell experiment, faults cut task-completion score by 60% (8.08 to 3.23 of 10), and the 120B gpt-oss model was no more resilient than the 20B. Agent stacks differed in how well they absorbed faults.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

ProfileSim is a profile-driven user simulator for financial marketing outbound calls. It models user profiles (demographics, Big Five traits) and business profiles (intents reverse-engineered from real call logs). The base model is fine-tuned on real voice dialogues, then refined with GRPO using dialogue-level and utterance-level rewards. Against other user generators it scores highest on colloquial naturalness and profile adherence, and lowest agent score on long-tail intents, with shorter utterances at comparable business compliance.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

MAST-VLA is a vision-language-action framework for multi-skill robot manipulation that conditions on both the skill to execute and where and how to contact objects. It adds skill-conditioned LoRA Mixture-of-Experts adapters to the LLM backbone and turns a predicted affordance heatmap into a patch-level spatial prior for the action head. It reaches an Avg. Len of 4.65 on CALVIN ABC→D and was tested on a real robot.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

MPQ (Message-Passing Quantization) is a post-training quantization method that treats row-wise PTQ as posterior inference over discrete codebooks. It keeps soft posteriors across weight blocks using vector approximate message passing, then refines a hard-decision initialization with exact block coordinate descent. Tested at 2-bit lattice and 3- and 4-bit scalar quantization on Llama, Qwen and Mistral models, it improves perplexity and zero-shot accuracy over GPTQ-style baselines.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

Tampered ML is a benchmark of 1,858 item-pairs, each pairing an experiment proposal reconstructed from a published ML paper with a minimally edited version that adds a targeted methodological flaw. Models must pick the sounder proposal in both presentation orders. A human study estimates 86% of items are valid (95% CI 81-91%). Opus 5 reaches 77.9% accuracy, and models are sensitive to order and favor flawed designs that look more specific, transparent, or conventional.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

This paper measures how hints such as "[Source] thinks the answer is (X)" shift LLM answers on MMLU questions, varying the source between a professor, a friend and a dog across Qwen, Gemma and SmolLM models. The shift fits an exponential tilt: a roughly constant mean change in the hinted answer's log-odds, with susceptibility largely shared across sources. The shift persists when the source is declared inaccurate or the prompt warns against trusting it. Synthetic experiments show how KL-regularized reward training could produce this pattern.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

Decode-time provenance binding is a defense for Model Context Protocol agent pipelines against cross-modal trust laundering, where instructions hidden in an email-signature image via LSB, DCT or metadata steganography get decoded by a tool. It combines HMAC-authenticated trust labels, a provenance ledger and context-taint tracking to block critical tool calls. On a real MCP testbed with 24 adaptive payloads, attack success fell from 100% undefended (62.5% for keyword and regex filters) to 0%.

arxiv.orgpaperAdded 26 Oct 2026, published 22 Oct 2026🛡️ Security

Sensorimotor Alignment (S^2) is a training-free offline metric that measures how well an embodied model's sensory representation aligns with the geometry of physical motion. It extends the Platonic Representation Hypothesis to motion trajectories. S^2 correlates positively with downstream robot task success across embodied model families, helps pick promising VLM backbones before fine-tuning into VLAs, and explains behaviours seen in VLAs and world action models.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

CausalCoT is an inference framework that splits a problem into a directed acyclic graph of atomic sub-questions and evaluates each node in an isolated context holding only its direct causal parents. This removes attention paths to non-parent content, so intermediate steps drive the answer rather than leaking from global context. It keeps benchmark accuracy competitive and raises causal sensitivity of reasoning steps by 12 points on average, up to 21, over the strongest baseline. The graph also allows test-time corrections that propagate downstream.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

F2G-Pose is a direct, single-pass framework that predicts category-level object pose, metric size and camera-frame completed shape from one RGB-D image. It lifts frozen RADIO foundation features onto partial point clouds, tokenizes local geometry with DGCNN, and fuses it with a Mixture-of-Experts Transformer. It trains only on synthetic SOPE data, leads on SOPE and ROPE at 28 FPS, and is tested on HouseCat6D, HANDAL and real manipulation.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

Progress Probes (P^2) trains linear probes on a vision-language-action model's own activations, using demonstrations, to estimate task progress, then flags executions that deviate from expected progress. Tested on the open-source pi0.5 across simulated and real-world manipulation tasks, it matches or beats VLM-based monitors and failure-data-trained methods at failure detection, without an external foundation model.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

This paper describes an evidence-aware retrieval evaluation protocol for a clinical RAG system over breast lesion management literature. Relevance is defined by evidence spans mapped to all containing chunks. A hybrid retriever (dense, BM25, cross-encoder reranking) with 384-token chunks and 20% overlap reached 0.94 Recall@3 on 18 queries. Expanding ground truth from 25 to 37 spans did not change rankings. The authors note the small set needs independent validation.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

TTR-Compressor is a sequence compression method that treats input tokens as a test-time regression problem. The regression is learned end-to-end by backpropagation, and its coefficients are solved in closed form in the forward pass to give the compressed representation. As a block-wise sparse attention compressor it improves RULER over attention-based compressors by a large margin. As a visual token merger for multimodal LLMs it beats the MLP merger and matches it with 55.6% fewer visual tokens.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

DuoCook is an Overcooked-inspired benchmark in which one vision-language model controls two cooks from top-down views in Unreal Engine environments built with coding agents. It has 20 maps in five categories covering equipment contention, changing terrain and handoff dependencies. Four VLMs were compared; the best, gpt-6-astra, reached 26.2% mean order completion. A notebook memory ablation had mixed effects: positive for Claude and Qwen, negative for GPT and Gemini.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

This October 2026 arXiv paper evaluates four LLM inference optimization families on Apple Silicon: KV-cache pruning, speculative decoding, ported GPU kernels and post-training quantization. Tests run on four Apple machines with a single-request decode workload, plus matched RTX 4090 runs. KV-cache pruning reaches 1.75x throughput on Apple, all tested speculative decoding methods reduce throughput, and ported kernels give up to 1.20x.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

This paper studies evidence-grounded health summarization for menstrual health monitoring. It combines structured evidence conditioning with RAG in a prompt-based constrained generation framework, and scores 4,500 summaries from three LLMs with NLI, atomic factuality metrics and readability measures. Grounding constraints improved uncertainty preservation and generally kept or improved faithfulness against retrieved evidence, while omission effects varied by model. Clinical alignment came with harder reading and more medical terminology.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

Linear cryptanalysis of FRAST, a TFHE-friendly stream cipher, in the average key setting. It fixes the false-positive problem left open in the earlier attack by Bak et al., showing key differences fall into five indistinguishable classes per round. The Contract algorithm recovers the master key for 10 rounds and, on weak keys of density 2^-11.8 to 2^-5.4, reaches full FRAST within the claimed security.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🛡️ Security

SARIA (Selective Admission and Reload for Inference Acceleration) is a KV caching system that jointly manages DRAM and SSD for multi-turn LLM agents. It persists only blocks likely to be reused beyond DRAM residency, promotes SSD-hit blocks into DRAM for same-session reuse, and paces DRAM reads with a transfer coordinator. Built on LMCache MP, it cuts SSD writes by 57.1 to 58.9% versus full duplicate caching and mean TTFT by 42.3 to 79.7% versus DRAM-only caching.

arxiv.orgpaperAdded 23 Oct 2026, published 22 Oct 2026🧠 AI

Chunk Recomputation under Real Document Revisions measures how much text must be re-embedded after edits for three stateless chunkers (non-overlapping fixed-size, recursive paragraph-first, content-defined) on 120 pairs of adjacent Chinese and English Wikipedia revisions. Fixed-size recomputes 57-68% of the new-version workload, recursive 38-56%, content-defined 43-54%, while evidence recall at a 2048-token budget differs by under two points. The authors argue update cost should be reported alongside retrieval quality.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

SecLog-Bench is a benchmark for LLM reasoning over cybersecurity logs, built on an extensible simulation platform covering 29 attack scenarios. It combines audit logs, application logs and network traffic, with attack-stage labels and step-by-step evidence chains. Eight LLMs were tested on post-detection attack-progress reasoning: the best reached 44.44% accuracy, 45.07% Macro-F1 and 54.87% Stage Reasoning F1.

arxiv.orgpaperAdded 23 Oct 2026, published 22 Oct 2026🛡️ Security

SecLog-Bench is a benchmark for LLM reasoning over cybersecurity logs. A simulation platform with 29 attack scenarios generates audit logs, application logs and network traffic, with attack-stage labels and step-by-step evidence chains. Eight mainstream LLMs were tested on attack-progress reasoning: the best reached 44.44% accuracy, 45.07% Macro-F1 and 54.87% Stage Reasoning F1. Correct predictions often came with flawed reasoning.

arxiv.orgpaperAdded 23 Oct 2026, published 22 Oct 2026🧠 AI

Gandalf is an SSD KV cache offloading system for LLM serving that is aware of SSD lifetime. It prioritizes admitting full-attention KV cache to SSD over cache from other attention types, using application-level information rather than access history alone. The authors report a 7.3x average SSD lifetime extension with negligible cache hit rate loss across several workload traces and hardware capacities. The design may extend to High Bandwidth Flash.

arxiv.orgpaperAdded 23 Oct 2026, published 22 Oct 2026🧠 AI

The SC-matrix is an evaluation framework for multilingual LLM sycophancy. It crosses a model's competence (correct answer with no pressure) with its sycophancy (keeping that answer under pressure), so agreeing with a false premise can be told apart from not knowing the answer in that language. Results: equal aggregate sycophancy rates hide different failure mixes, high competence does not always protect, and reasoning-oriented mitigations beat directive ones.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

Matrix-Game 4.0 is a system that separates game logic from visual rendering for playable generated worlds. A lightweight engine holds world state and rules, a multimodal coding agent writes game code and scene captions, and a distilled autoregressive video model turns low-poly proxies into detailed frames. It runs 480p at 60 FPS on one RTX 5090 with 112 ms key-press-to-display latency. Code, weights and assets are released.

huggingface.copaperAdded 22 Oct 2026🧠 AI

Every Part Has Its Role studies concept augmentation in LLM post-training, where a target word is replaced by related alternatives in training sentences. It extends prior work from nouns to verbs and adjectives. Concept augmentation improves perplexity most consistently on news regardless of part of speech, while verbs give the most reliable downstream gains. CKA and linear probing show the largest gains do not match the largest representation changes.

arxiv.orgpaperAdded 23 Oct 2026, published 21 Oct 2026🧠 AI

Paper on prompt injection delivered through forensic evidence, where the seized device's contents are attacker-controlled and the examiner must read them. It extends the anti-forensic model to cover manipulation of the reasoning layer and gives a taxonomy (objective, technique, payload location, concealment). Three proof-of-concept attacks on an LLM-assisted analysis pipeline change a reported conclusion, write attacker-chosen content to the analyst workstation, and trigger outbound network traffic. Mitigations are proposed.

arxiv.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

Monitorability Is Trainable proposes an unsupervised post-training objective that maximizes mutual information between a model's chain of thought and its output. On three model organisms with degraded CoT (from compression, monitor optimization, and synthetic-document generalization), it recovers most or all lost monitorability. On undegraded policies it improves CoT monitor and activation probe prediction of unsafe behavior and raises consequential reasoning.

arxiv.orgpaperAdded 23 Oct 2026, published 21 Oct 2026🧠 AI

MPIDetector is a detection framework for multi-modal prompt injection that uses few-shot in-context learning so an LLM can reason about the semantic intent of text and image inputs, without fine-tuning. The authors built a benchmark of eight textual and seven visual attack categories plus benign sets. They report up to 90.91% accuracy on text, 99.28% on images, and up to 99.80% detection on 500 obfuscated WebInject web-agent injections, beating baselines.

arxiv.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

Active and Passive Attacks against TPM 2.0-based Full Disk Encryption is an ePrint paper by Freud, Hein and Klein (BSI). It shows SPI bus sniffing and interception against discrete TPMs on a mini PC and a notebook, plus a prototype UEFI firmware attack in QEMU that affects firmware TPMs. The attacks break BitLocker and all current Linux FDE implementations, including those using TPM parameter encryption.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

Paper on memory injection, where prompt injection persists across sessions through the long-term memory of LLM-as-a-service platforms. It presents a general threat model and shows all NIST attack goals are achievable on ChatGPT Instant 5.3, Claude Sonnet 4.6 and Gemini 3 Flash. Vectors include personal memories, projects, skills, organization instructions and MCP resources. On Claude, shared memory lets an attacker compromise multiple users in an organization and exfiltrate prompts. Nearly half of the derived mitigations are unimplemented.

arxiv.orgpaperAdded 24 Oct 2026, published 20 Oct 2026🛡️ Security

UniWALM is a unified world-action-language model that learns video, action and language in one mixture-of-transformers backbone with per-modality diffusion heads that co-denoise together. Generated subtask and textual memory are fed back on the next step, so no external planner or memory bank is needed. It adds Flow Matching with Correction (FMC) for recovery from perturbed actions. It reaches 34.5% on the memory-dependent RMBench subset, above Mem-0's 28.5%, and lifts real-world Find-the-Item success from 11.1% to 61.1%.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

DAR-MGSM (Dialectal Arabic Multilingual Grade School Math) is a manually curated benchmark for mathematical reasoning in Dialectal Arabic. It covers five major Arabic dialects plus Modern Standard Arabic, in a translated-only version and a translated and culturally localized version. Eleven Arabic-centric and multilingual LLMs were tested: accuracy generally drops on the localized version, and a mechanistic interpretability analysis shows the two variants induce distinct internal reasoning trajectories.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

AnomalyAgent is a post-training fusion framework for visual anomaly detection that ensembles pretrained detectors. It aligns scores and anomaly maps, summarizes disagreement, and treats fusion as a choice among score actions and map actions. A constrained vision-language model inspects high-conflict samples and picks the actions. It beats equal-weight fusion and gets the best mean image AUROC and pixel AP among compared fusion methods on MVTec AD and VisA.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

Membership Inference in Privacy-Preserving Vector Search is an IACR ePrint paper (2026/2570) that attacks backend-compatible protections for embedding databases using one snapshot and known plaintexts. It breaks the Scale-and-Perturb scheme of Fuchsbauer et al. (advantage 0.997 on MS-MARCO) and analyzes Rotate-and-Perturb, ideal DCRE and an encrypted HNSW index. It also tests shuffling and secret projection as mitigations.

eprint.iacr.orgpaperAdded 21 Oct 2026, published 20 Oct 2026🛡️ Security

DIVE is a benchmark for full-duplex interaction with visual engagement in continuous audio/video streams. It has 525 human-annotated samples across six tasks in three scenarios: proactive intervention, continuous engagement and interaction coordination. Evaluated real-time models reach sub-second average latency, but window response rates stay at or below 53.77%, and adapting ongoing responses to new information is hardest.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

nokast-secureRAG is a roughly 0.5B-parameter small language model that works as a semantic firewall between retrieval and generation in local RAG systems. It classifies each query and context pair as safe, suspicious or malicious. It reaches 0.994 recall at a 0.026 false-positive rate in-distribution, beating Llama Guard 3 1B and Prompt-Guard-86M (0.900 recall, 0.690 FPR). Recall on PoisonedRAG falls to near zero, and single-request latency is 293-404 ms.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🛡️ Security

Systematic evaluation of fault attacks on ML-DSA (FIPS 204) rejection sampling and of proposed countermeasures. It proves the "third rejection test" of patent WO2025261784A1 re-verifies what check 2 already guarantees, and shows that 30,000 fault injections never detect a skipped z-norm check. The author fully recovers s1 against a third-check-protected implementation under randomized signing. Third check and sign-then-verify are complementary; overhead is 0.04–0.05% on x86-64 and 0.83% on Cortex-M4 for the third check.

eprint.iacr.orgpaperAdded 21 Oct 2026, published 20 Oct 2026🛡️ Security

This paper shows that reward misspecification alone degrades chain-of-thought (CoT) monitorability in language models. Using a testbed of paired true and misspecified rewards across three models and three tasks, CoTs keep reasoning about task intent while outputs hack the reward. The proposed reward description training injects an LM-inferred reward description into the input and raises monitorability by an average of 24% or 42% absolute, depending on the metric.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

Further Elaborations on the Provable Dual Attack is an October 2026 ePrint paper by Kaveh Bashiri presenting a provable dual attack on LWE that recovers a subvector of the secret, with an explicit success-probability bound and no heuristic assumptions. It unifies the analyses of Pouly and Shen (EUROCRYPT 2024) and Bashiri and Wiemers (2025). Applied to ML-KEM it improves the first provable dual attack by 22 to 100 bits; it is also applied to ML-DSA and tested on small instances. The author states ML-KEM and ML-DSA are not threatened.

eprint.iacr.orgpaperAdded 21 Oct 2026, published 20 Oct 2026🛡️ Security

RAISE (Risk-Aware Intensive Supplemental Execution Framework) is a just-in-time framework that predicts which project-days introduce a vulnerability, to decide where to spend extra fuzzing. The dataset joins OSV vulnerability-introducing commits with OSS-Fuzz coverage data for 15 C/C++ projects (15,210 project-days, 347 positive). A Random Forest using VCCFinder static features plus fuzzing features reaches ROC-AUC 0.74. In a simulation over 968 vulnerabilities, Simple-Regression needed 91.60 extra fuzzing sessions per detected vulnerability, against 171.89 for the Median heuristic.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🛡️ Security

OpenVLHarness is a multimodal reasoning harness for MLLMs built on a capability layer (task-level tool interfaces plus evidence rendering) and persistent multimodal memory that stores images, structured data and web references as addressable artifacts. A coding interface runs generated programs over stored data. Across 22 benchmarks and eight backbones it adds 9.7-12.6 points over no-tool baselines, and GPT-6 Luna at medium effort beats Codex and Visual Sketchpad at maximum effort using at most 37% of their tokens.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

This paper studies how keeping long tool output out of the context window, and reading it through a section index instead, costs LLM agents accuracy. Using the PHREEQC geochemistry simulator on 158 execution-verified multiple-choice items and 11,007 logged calls, GPT-5.1 and GPT-5.2 lose 11.3 and 7.9 points, while Claude Opus 4.6 and Sonnet 4.6 show no detectable cost. The authors name the failure evidence substitution: agents skip the report and misround from their own output files.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

Mise-en-Video (MeV) is a multi-shot video generation framework where creators stage shots in 3D space and then generate the matching video. It combines hierarchical attention for multi-level narrative consistency with 3D-aware layout guidance for per-shot spatial control. The authors built a 160K-video dataset with hierarchical multi-shot texts and oriented 3D bounding boxes, and report gains over baselines in quality, consistency and subject controllability.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

Tandem is a runtime for serving hybrid linear-attention GUI agents on mobile NPUs. It saves recurrent state where consecutive prompts diverge, keeps most prefill on the matrix unit, uses a verification kernel that reads each weight block once, and releases NPU power votes between bursts. On a Snapdragon 8 Elite it cuts per-step latency 30% for text screen descriptions and 16% for screenshots, cuts energy per text step by 33%, and lowers loaded-model standby power from 0.76 to 0.22 W.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

This arXiv paper tests eight jailbreak attacks against three open-weight language models under three evaluation procedures. Low-resource language attacks score 96.0% mean ASR under keyword matching but 2.7% under a Qwen3 judge using StrongREJECT instructions. At Ollama's default temperature of 0.8, 100 samples of an unchanged request give 100% ASR by keywords and 50% by the judge. The authors recommend explicit success definitions, matched response budgets, validated evaluators and cumulative-failure reporting.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🛡️ Security

Concerns in Benchmarking Jailbreaks is an October 2026 arXiv study of eight jailbreak attacks on three open-weight models under three evaluation procedures. Low-resource language attacks score 96.0% ASR by keyword matching but 2.7% under a Qwen3 evaluator using StrongREJECT instructions. Attack rankings flip with attempt budgets, and 100 samples at Ollama's default temperature 0.8 give 100% ASR by keywords, 50% by the evaluator.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

HAF-VLA is a hierarchical action-flow framework that adapts a pretrained flow-matching vision-language-action model to humanoid whole-body loco-manipulation. It denoises a shared action space in stages: locomotion and head commands first, then waist refinement, then full-body manipulation. Learnable stage embeddings and cross-stage KV-cache conditioning carry dependencies between stages. The authors report gains over existing models on six real-world tasks.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

Dynalize is a method that reduces reward hacking during online RL training against preference reward models. Periodically and asynchronously, a language model reads training rollouts and writes penalties for undesirable behaviors the reward model seems to reward, and a judge applies them to every rollout. In clinical counseling and creative writing it sustains longer training and improves external benchmarks, and the gains hold in multi-turn training and after reward model retraining.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

WSR-Tune is a fine-tuning framework that reparameterizes LLM weight matrices with a safety-conditioned basis, so safety-relevant information concentrates in a small set of directions. Those safety-critical directions are frozen, and the remaining complementary directions are updated for the downstream task. The authors report a better trade-off between downstream performance and safety retention. The page gives no benchmark names or numbers.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

Fake News, Real Crashes is an October 2026 arXiv paper presenting a benchmark for information injection attacks on LLM financial trading agents. It spans five injection vectors, seven event categories and about 10,000 decisions on 15 U.S. stocks. Fabricated news reached 62% attack success versus 14% for prompt injection, and two 30-word templates hit 100%. Only hierarchical aggregation reduced success among the defenses tested.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🛡️ Security

E-Layout is an ML-driven framework for post-placement logic restructuring in IC physical design. It builds equivalent logic alternatives with e-graph equality saturation, links logic choice to physical realization through a graph-based RAG model, and refines the choices with reinforcement learning on design-level timing feedback. On 9 designs it gives about 6% TNS and 2% WNS improvement and 1% less area than a commercial physical design flow.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

Concrete LDVR Attacks with Fewer Signers is an ePrint paper by Renas Bacho on the low-dimensional vector representation (LDVR) problem behind adaptive security of threshold Schnorr. It combines a Chinese remainder theorem construction with a generalized birthday search to attack 488 signers with 173 corruptions over the Curve25519 scalar field, down from 5,020 signers. For adversarial evaluation points, it reaches optimal success probability and breaks 228 signers with 114 corruptions in 168.60 seconds.

eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026🛡️ Security

VibeTraining-Bench is a benchmark for cross-component co-design, where research agents iteratively refine training data, trainers and inference harnesses for a fixed starting agent system. Tasks cover terminal operation, software engineering, multi-turn tool use and math reasoning, with safeguards against reward hacking. On SWE-bench Verified, GPT-6 Astra raises the target system from 32.9% to 53.7%, against 55.4% for the human-developed instruct model.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

Evasive Inner Product Functional Encryption Is Broken Too is a 2026 ePrint paper by Agrawal, Modi, Yadav and Yamada. It gives a counterexample showing the evasive IPFE assumption of Hsieh, Lin and Luo (Eurocrypt 2024) is false in full generality. The attack extends the 2025 PRFE counterexample and needs only the weak inner product functionality, not pseudorandom function computation. The authors conclude that letting the adversary choose unrestricted private coins breaks the Evasive LWE family.

eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026🛡️ Security

New IND-CPA-D Attacks on BFV and BGV is an ACM CCS 2026 paper by Choe, Kang and Stehlé presenting three attacks on FHE schemes whose parameters rely on heuristic noise bounds. Two attacks recover the secret key and one breaks the IND-CPA-D game. Experiments use HElib for BGV and OpenFHE for BFV, where full key recovery takes minutes to hours.

eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026🛡️ Security

The paper presents a fault attack on deterministic CSIDH and OSIDH-LD. Forcing the lane-selection mask to zero (for example by skipping one instruction) makes output changes reveal whether a secret exponent reaches a threshold. Binary search then recovers the full key in at most 297 queries for OSIDH-LD and 222 for deterministic CSIDH with CSIDH-512-sized keys. Full OSIDH-LD keys were recovered in software fault simulation, and a ChipWhisperer-Lite skipped the target instruction with probability at least 0.94. The authors argue fault resistance requires giving up determinism and propose randomization countermeasures.

eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026🛡️ Security

RAGLeak is a supply-chain privacy backdoor attack on retrieval-augmented generation. When triggered, the backdoored generator reversibly obfuscates retrieved private documents, and an auxiliary LLM recovers them. Tested on three model families and three domains, it reaches up to 100% attack success rate, loses at most 10 points under detection-based defenses, and survives clean fine-tuning and changes of model scale and version. Source code is released.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🛡️ Security

This paper presents an end-to-end GPU implementation of the HQC code-based key encapsulation mechanism. It speeds up GF(2) polynomial multiplication with a parallel Frobenius additive FFT and a Toeplitz matrix-vector mapping onto INT1 tensor cores. On an RTX 4090 the multiplier reaches 3,828k polymul/s, about 2.9x the earlier HIGH implementation. Full KEM throughput is 1.56x, 1.55x and 2.03x higher for keygen, encaps and decaps. Five thread-to-KEM mapping variants are tested on RTX 3090 and Jetson AGX Orin.

eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026🛡️ Security

The paper is a large-scale study of 37 security skills run through Claude Code with four LLMs on 100 real Linux kernel vulnerabilities, 14,800 runs in all. Only 24.4 percent of runs found the correct function and 15.7 percent classified the vulnerability correctly. It proposes SKILL-SIF, which evolves skills from failure patterns and raised fully correct detection on 100 unseen vulnerabilities from 2 to 16 percent.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

Empirical study of 37 security skills run through Claude Code with four LLMs on 100 real Linux kernel vulnerabilities across ten classes (14,800 runs). 24.4% of runs found the correct function, 18.3% the function and statement, and 15.7% the vulnerability class. The authors propose SKILL-SIF, which evolves skills from failures and raised fully correct detection on 100 unseen vulnerabilities from 2% to 16%.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🛡️ Security

Off-Key is a privacy attack on SPDZ-family maliciously secure MPC protocols that exploits inconsistent MAC keys. Depending on the MAC check protocol, a corrupted party recovers a shared secret from one authentication check or tests guesses through selective failure. The authors show it in MP-SPDZ, SCALE-MAMBA and FANNG-MPC, where a corrupted party can use different keys across batches, threads or runs, and describe the preprocessing state that enforces key consistency.

eprint.iacr.orgpaperPublished and added 19 Oct 2026🛡️ Security

Hybrid Reasoning Agents (HybridAgents) are LLM agents that switch between latent reasoning and text-based interaction in one generation process. A lightweight HybridLink handles mode transitions, continuous working memory keeps hybrid context across turns, and HybridSFT plus HybridRL train when to think latently and when to act in text. Across 3 model scales and 11 benchmarks, the paper reports 10.4% higher performance, 35.5%-54.7% fewer tokens, and 1.8x-2.6x faster inference.

arxiv.orgpaperAdded 19 Oct 2026, published 18 Oct 2026🧠 AI

ePrint 2026/2527 by Paola de Perthuis and Filip Trenkić analyses module-BKZ attacks on NTRU+, the South Korean KEM standard. It introduces relative canonical and coefficient embeddings and shows they agree up to scaling exactly when rad(c)/rad(ℓ) is 1 or 2. Simulation-based estimates show the best module attack saves 4 to 10 blocksizes, costing NTRU+ keys at most three bits of Core-SVP security. NTRU+ parameters stay safe in the overstretched regime.

eprint.iacr.orgpaperAdded 19 Oct 2026, published 18 Oct 2026🛡️ Security

"How employment is changing at Anthropic" is an October 2026 technical report by Nathan Wilmers, Regina Catipon, Maxim Massenkoff and Peter McCrory. It uses internal employment, agent telemetry, output, survey, calendar and hiring data plus 17 interviews. Junior technical hiring fell from 27% to 9%, agent hours reached six times employee hours by August 2026, and adopters' output rose. Employees increasingly do meta-work managing agents.

anthropic.compaperAdded 19 Oct 2026, published 18 Oct 2026🧠 AI

This ePrint paper (2026/2525, October 2026) by Brendan Funk and Jiafeng Xie presents the first hardware accelerator for FAEST, an MPC-in-the-head post-quantum signature in NIST's Round 3 additional-signature process. It analyses FAEST's compute-heavy operations, builds them into hardware blocks, and adds an L1-EM-fast variant. The authors report at least 98% lower equivalent area-delay product than the recent complete SDitH design at the same security level.

eprint.iacr.orgpaperAdded 19 Oct 2026, published 18 Oct 2026🛡️ Security

ToFu is a tool-aware fuzzing framework for LLM tools, including MCP servers. It maps each tool argument, using its metadata, to one of 74 CWE-guided semantics-aware sanitizers (path, URL, SQL and similar). It then generates schema-valid Atheris harnesses and semantic oracles, using runtime tracers for in-process tools and outcome oracles for opaque ones. The LLM (gpt-4.1) only maps sanitizers and writes seeds; it is never the security judge. The paper also shows a "Repeat-After-Me" trick, where an LLM told to repeat attacker-crafted text emits a tool call the orchestrator executes, reproduced on LangGraph, MAF, ADK 2.0, AutoGen, Strands and CrewAI. Across 273 runnable MCP servers (3,405 tools) it reports 253 verified bugs in 343 tools across 118 servers, such as path traversal, log injection and exfiltration. These are failures a crash-based fuzzer would miss. The result matters because it shows LLM-generated tool arguments cannot be treated as trusted.

arxiv.orgpaperAdded 18 Oct 2026🧠 AI

The One-Pixel of Reasoning introduces the one-relation attack (ORA), which finds a critical implicit warrant linking evidence to a conclusion and inserts one source-grounded sentence that qualifies it, steering an LLM agent to a wrong belief. Across eighteen models on biomedical inference and vulnerability detection, best-of-10 attacks flip initially correct answers 54.9% of the time for proprietary models and 80.0% for open-weight models. Attacks transfer without victim feedback, can pass source-support checks, and multi-agent debate raises team error from 50.0% to 59.5%.

arxiv.orgpaperAdded 21 Oct 2026, published 17 Oct 2026🛡️ Security

Forum announcement of a Zenodo preprint on post-quantum migration for censorship-resistant P2P steganographic tunnels. It argues that ML-KEM's prime modulus (q=3329) makes naive bit-packed ciphertexts statistically distinguishable from uniform noise, which gives a cheap passive DPI distinguisher. It also argues that composite PQ 0-RTT envelopes over 4.4 KB force fragmentation, which requires stateful reassembly before AEAD verification and reopens memory-exhaustion DoS in eBPF/XDP filters. Proposed fixes are algebraic uniformization (LWR lattices or inverse rejection sampling), sub-MTU hybrid or pure-PQ envelopes (Falcon-512, signcryption), and Fujisaki–Okamoto implicit rejection for constant-time silent drops. Aimed at Ethereum validator, libp2p and RPC transports facing harvest-now-decrypt-later threats.

ethereum-magicians.orgpaperPublished and added 17 Oct 2026🛡️ Security

Presents the first DPA-style power side-channel attack framework against VOLE-in-the-Head signature schemes, targeting the secret witness whose recovery enables forging signatures on arbitrary messages. It analyzes leakage from sensitive intermediate values in VOLE correlations and proposes six attack classes (two vertical, four horizontal) on FAEST and SDitH, both in NIST's third round of additional signatures. Attacks are validated with simulated leakage under varying noise and real measurements on three ARM Cortex-M4 devices.

eprint.iacr.orgpaperPublished and added 17 Oct 2026🛡️ Security

Cryptanalysis of the Eevee forkcipher-based AEAD modes Umbreon and Espeon, which claim online AE security under nonce reuse. Associated-data correlations give probability-one non-empty-message forgeries with three encryption queries and one verification query. The paper adds an affine treatment of partial final blocks (n-r+3 queries, or n-r+2 with adaptive choices). It also checks the published Umbreon attack with the official ForkSkinny primitive and reports a two-query attack accepted by the unmodified public C modes.

eprint.iacr.orgpaperPublished and added 17 Oct 2026🛡️ Security

OpenGCUA, from Alibaba's MAI-UI team, is an open stack for generalist computer-use agents (GCUAs), where the model chooses how to observe, act and organize work across GUI, accessibility-tree, API and shell interfaces. Its design combines code execution (GUI actions written as programs with loops and branches) with harness-to-model decision transfer. On OSWorld-v2 it raises mean partial score by +19.6 points across eight models, including GPT-6 Astra and Claude Opus 5, and cuts reported steps per task by 53%. The release includes the harness, prompts, evaluation trajectories, a macOS real-device harness, a cross-device SDK and an interactive lab. It also shows large efficiency gaps between models: Qwen3.8-Max used 19.4x the reasoning tokens of GPT-6 Astra on real macOS tasks.

huggingface.copaperAdded 17 Oct 2026🧠 AI

This paper shows that training-free relaxed (lossy) verification in speculative decoding causes length inflation: completions get longer, mostly through extended reasoning traces, which offsets gains in accepted tokens per round. Across twelve target-drafter pairs, relaxed rules accept 1.34x as many tokens per round but produce 1.54x longer completions. Rules that cut verifier rounds usually lose substantial accuracy, so the authors argue completion length and verifier rounds must be reported.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

DLite is a history-free speculative decoding framework for agentic LLM workloads. It reuses the target model's contextual hidden states and predicts locally without keeping the draft model's historical KV cache, which avoids the up to 80% acceptance-length drop seen with long, multi-turn contexts. A lightweight sequential head restores intra-block causal dependencies, and a two-stage training strategy is used. It reaches 2-6 acceptance length and beats DSpark by up to 70%.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

FedRAPP is a personalized aggregation framework for Source-Free Federated Test-Time Prompt Tuning (SF-FTTPT), where clients adapt CLIP soft prompts on unlabeled, non-IID test streams without source data. It combines reliability-aware client assessment, a server-side exponential moving-average prompt memory and similarity-guided peer aggregation. Across nine CLIP benchmark datasets and ImageNet variants it beats standard federated aggregation baselines while keeping prompt-only efficiency.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

OwlSight is a chain-of-thought monitor that uses "Factor", a monitoring requirement written into the prompt, so one monitor can cover many tasks across capability, safety and trustworthiness. Factors come at fine granularity (a specific leaked email address) and coarse granularity (privacy leakage). On 15,971 reasoning trajectories it beats general-purpose LLMs by 1.32-30.57 points (fine) and 11.71-27.81 points (coarse), and reaches state-of-the-art on OpenAI's CoT monitorability benchmark. It generalizes to unseen models and resists Factor rephrasing, CoT compression and obfuscation.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

The paper revisits the multi-key optimization for SPECK, a code-based signature scheme that uses the Permutation Code Equivalence Problem (PEP) as a trapdoor for the Permuted Kernel Problem (PKP). Each SPECK round has soundness error 1/2, which inflates signatures, and the SPECK authors had warned that naively using multiple public keys creates security concerns. The authors analyse those concerns, give conditions and parameter constraints under which multiple keys can be integrated securely, and show that conservative parameters perform poorly but leave room for improvement. They also build a ring signature scheme on the same construction.

eprint.iacr.orgpaperAdded 17 Oct 2026, published 16 Oct 2026🛡️ Security

This paper builds a benchmark of 500 debatable questions across ten domains to measure how user narratives steer LLM agreement. Each question has 11 matched conditions (one neutral, five mirrored narrative levels per opposing stance), giving 5,500 instances across seven LLMs. Causal framing and worldview synthesis raise agreement, personal experience can lower it, and GPT-5.1 ablations show bullet formatting also increases agreement.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

ConQUESTA is a post-training pruning framework for quantum neural networks that combines task saliency with compiler cost, screens candidate entanglers, and searches whole pruning masks under an accuracy constraint. Each mask is transpiled across several coupling topologies. At 55% pruning on 4-, 6- and 8-qubit QNNs it cuts resources by 61.6% (Breast Cancer), 72.0% (Digits01) and 82.2% (Iris), and fixed-mask recovery raises feasible runs from 16/24 to 22/24.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

MobileInject is a benchmark for testing MLLM-based smartphone agents against indirect multimodal injection. It has an interactive evaluation framework with 11 scenarios, 9 security-risk categories and 108 executable risk instances. It adds Cross-Modal Consistency Injection, Multi-Role Collaborative Deception, Reasoning-Camouflage Injection and a Reasoning Security Diagnosis Framework. Experiments find broad vulnerability, with many failures from early reasoning hijacking.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🛡️ Security

LexPilot is a benchmark linking proactive legal interaction to claim-level verdict prediction, built from 432 real Chinese civil judgments across 36 causes of action. It pairs expert-reviewed complete and underspecified consultation histories with court verdicts. Current LLMs score on average 23.84% lower on judgment prediction in underspecified consultations than in fully specified ones, and the paper analyses the bottlenecks.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

Beacon is a safety alignment framework for large reasoning models that fixes two GRPO failure modes: advantage collapse, where homogeneous rollout groups give vanishing advantages, and unsafe diversification, where unsafe behaviors persist and spread. It combines Safety Signal Recovery (advantage-guided trajectory filtering plus safe reference supervision) with Safety-Utility Decoupled Alignment (category-aware rewards, benign-only KL anchoring). Tested on four Qwen3 and DeepSeek-R1-Distill variants across eight benchmarks, it cuts average safety error by up to 32.83 percentage points with low over-refusal.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

SpikeQ is a spike-guided post-training quantization framework for spiking neural networks on neuromorphic chips. It picks each weight level by simulating downstream neurons to minimize spike errors, and uses a reset-aware grid for pre-fire membrane potentials. With 4-bit weights and potentials, models stay within 2.59 points of full precision, with 7.8 to 7.9 times smaller memory and 6.4 to 6.9 times less energy.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

MUB (Music Understanding Benchmark) is a set of 500 identity-masked recordings with existing annotations for tempo, key, time signature and structural intervals. It covers all 24 keys, tempos of 47 to 180 BPM and 4,676 intervals, and strips titles, artists and platform IDs from inputs. Baselines reach 53.6% tempo accuracy (DSP) and 98.4% time-signature accuracy (constant 4/4). No multimodal LLM has been tested yet, and rights clearance is unresolved.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

Many Biases, One Circuit is a October 2026 arXiv paper that traces attention heads behind cue-following (for example "I think the answer is B") through two post-training pipelines. Four cue types share a few heads already present in pretrained models. About 200 generic assistant examples activate them in chat format, and the final RL stage strengthens cue effects. Swapping attention patterns of six middle layers between Tulu-3-8B and OLMo-2-7B checkpoints moves cue effects most of the way.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

EDIC is a decomposed reward framework for per-instruction compliance in LLMs that read external data, where one document or tool return mixes legitimate directives with injected ones. A generative judge, trained with SFT then GRPO, attributes compliance per instruction and produces RL rewards for beneficial and harmful compliance. On Qwen3-4B it raises harmful rejection by up to 7.7% and Arena-Hard-Auto utility by 12.5% across five benchmarks.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🛡️ Security

R^2ECAP (Reused Reasoning-Cache Poisoning) is a black-box attack that plants poisoned reasoning traces through a chat interface, exploiting LLM systems that reuse KV caches of reasoning. A preceding trace steers the model, and the retained trace keeps residual adversarial influence in its cache after the preceding trace is discarded. On three open-weight LLMs and three datasets it reaches up to 79% ASR, beats the white-box HijackKV in 8 of 9 settings, and reaches 46% ASR in a reasoning-trace retrieval system. Only 1% of retained responses are flagged by a popular prompt injection detector.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🛡️ Security

The paper proposes a traceable and aggregatable tagged witness signature (TA-TWS) and builds SwiftAuth on it, a conditional privacy-preserving anonymous authentication framework for UAVs and other dynamic terminals. Issuers can create authorization components while users are offline. Several credentials compress into one constant-size proof, verifiers can batch-verify across users, and a regulatory trapdoor lets authorities trace misbehaving users. TA-TWS is proven unforgeable and witness-hiding, and benchmarks with the MIRACL Core library on constrained devices and Aliyun servers show it is practical.

eprint.iacr.orgpaperAdded 17 Oct 2026, published 16 Oct 2026🛡️ Security

This paper analyses software supply chain (SWSC) attacks against VLSI design tools. It empirically studies the software composition, security features and tool interactions of VLSI suites, then builds a taxonomy linking SWSC attacks to VLSI attacks, design-flow stages and security outcomes. The taxonomy yields 717 possible new attacks, 60 of them high feasibility, with a threat model, a defense assessment and case studies on an open-source VLSI flow.

arxiv.orgpaperAdded 20 Oct 2026, published 15 Oct 2026🛡️ Security

Value as Memory (VaM) is a post-training framework for generalist robot policies. It addresses partial observability and the delayed feedback of offline post-training. Value caching conditions actions on recent value predictions as task-progress memory. Online real-world RL combines offline demonstrations, off-policy interventions and on-policy rollouts, and moves from offline RL warmup to autonomous learning with sparse rewards. On long-horizon deformable-object and contact-rich manipulation tasks it gains 29.4% mean absolute success rate from value caching and 32.8% from online learning over matched offline training. The online schedule runs 8x as many policy iterations under the same gradient-step budget, with over 2x lower time per gradient step from concurrent data collection and training.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

DK-VLM adds Dual-Kernel Attention to a vision-language model. A semantic kernel keeps the pretrained attention and positional encoding. A separate geometric kernel uses estimated 3D points and camera poses to modulate attention through point displacements and relative view transformations, and a learned gate fuses the two outputs. Geometry therefore enters attention as position, not as fused 3D reconstruction features. The paper also adds Reference-Frame Composition (RC) supervision, which gives stepwise turn and heading targets derived from verified single-step annotations, to train multi-step spatial reasoning as the observer moves. DK-VLM-8B reports the highest average score across six spatial benchmarks among the compared methods, with 74.33 on VSI-Bench and 58.28 on ReVSI-32, and it preserves general video understanding.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

The paper identifies an "exploration dilemma" in modern Mixture-of-Experts models. Shared experts reduce the variability of token representations, which makes routing less exploratory. Fine-grained expert segmentation enlarges the combinatorial routing space and so needs more exploration, which means its extra expressiveness may go unrealized. It proposes MirrorMoE, which shrinks the exploration space by suppressing unnecessary experts per token, and reports consistent gains across model scales.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

Presents MLExpert, a 35B Mixture-of-Experts agentic model trained for autonomous long-horizon machine learning engineering. Training uses a Kaggle-MLE Factory to build executable tasks and an interaction harness, hierarchical agentic SFT with outcome-guided supervision, and Windowed Interaction RL (WIRL). WIRL treats the multi-turn decisions between successive experiment results as one execution window with a shared outcome reward, and uses asynchronous collection and policy correction. On MLE-Bench Lite the average medal rate rises from 37.9% to 72.7%, comparable to larger frontier models such as Kimi-K3 and GLM-5.3. It also improves on NatureBench Lite, agent scaffold optimization and algorithm optimization.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

Velox is a training-free attention router for chunked video-diffusion world models. It uses camera frustum slabs with depth intervals from streaming geometry to pick which historical token blocks to attend to, instead of letting the KV cache grow with rollout length. Frustum overlap softly modulates each layer's model-derived block importance, and uncovered regions keep the model's own routing. The fixed block granularity suits FlashAttention-style tiled execution, and slab construction runs asynchronously with denoising on the same H200. At chunk 48 it speeds up attention 4.36× and denoising 2.43×, and on the 158-case WBench navigation split it stays within 0.15 average points of dense LingBot-Fast-Cam.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

Studies "filesystem capability laundering" in MCP servers: client approval of a tool call grants the server process broad host filesystem access, so attacker-steered arguments can reach sensitive files outside the tool's declared contract. The authors build MCP-FSD, which infers per-tool least-privilege boundaries, plants credential fixtures outside them, and tests exploitability and sandbox containment by paired dynamic execution. Of 21,097 servers from nine registries, 16.4% of 4,806 triaged servers have filesystem capabilities despite non-filesystem classifications. Dynamic validation confirmed 150 vulnerable instances, and disclosure produced 13 advisories, including two CVEs in Anthropic's Git MCP Server.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🛡️ Security

The paper separates "active" facts (which support generalization beyond recall) from "inert" ones. It builds new relations by relabeling answers of existing ones (Paris -> Europe becomes Paris -> crab), fine-tunes on a subset of subjects, and tests held-out subjects that share an answer (Munich -> crab). Across eleven language models and 70 relations, linear readability of the original relation in pretrained representations correlates with this generalization at Pearson r = 0.87 to 0.92 within each model. The result holds after controlling for fine-tuning set size, original-relation accuracy and subject frequency in pretraining text, and it survives re-randomized labels, seeds and thirteen other label categories. Mapping wrong labels back to the original answers shows the fine-tuned model's errors follow the linear probe's top answers.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

AI Consilium is a multidimensional benchmark built from 300 diagnostically resolved NEJM–MedQA cases (110 for physician reference ratings and Judge calibration, 190 held out). Eight single-model pipelines produced role-specific opinions and interactive-consilium reports under free-form and structured clinical-reasoning conditions, and a physician-calibrated LLM Judge scored 3,040 final reports. Model identity separated quality more than format; structured reports won in seven of eight pipelines (management 2.740 vs. 2.436), multidisciplinary synthesis beat the primary-role opinion on all five shared criteria, and GPT-5.6-Sol led every component with the fewest review rounds.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

EliteReplay is an RL post-training recipe that pairs GRPO with an "elite" archive built by quality-diversity search. Before training, the frozen base model is used to collect solutions spanning distinct approaches; during training, a replay loss on this archive is added to GRPO. This counters the narrowing of solution diversity that RL causes. On text-to-SQL tasks it reaches higher pass@128 than all baselines and yields up to 77% more distinct correct strategies. It also retains more of the base model's strategies and covers both readings of ambiguous questions nearly twice as often on AmbiQT.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

Introduces Librarian Agents, where a builder agent turns raw unstructured documents into a hierarchical filesystem of synthetic files and reference links, forming a workspace for a downstream answering agent. Workspace construction is cast as a Markov decision process and the builder policy is trained with reinforcement learning to maximize downstream answering accuracy. It beats the strongest retrieval and precomputation baselines by 33 points on BrowseComp-Plus, amortizes construction cost over repeated queries, and generalizes zero-shot to BRIGHT with a 31-point average gain.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

X-RIC (eXplainable RIC) is a framework that adds agentic reasoning to the O-RAN Near-RT RAN Intelligent Controller. It combines reactive reinforcement learning with automated behavioral cloning from domain heuristics, a transformer-based Telecom Language Model for contextual abstraction and interpretability, and closed-loop co-adaptation for continuous policy refinement. The paper also introduces BARX, a benchmark for agentic RAN control that measures latency, compliance and determinism. On SLA-aware network slicing, in simulation and on an O-RAN-compliant testbed, it reports 89% lower operational cost with SLA compliance kept, 75% faster convergence, 50% fewer training steps and 46% lower variance.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

Proposes EdgeFlow, a hybrid KV cache migration scheme for distributed LLM inference across heterogeneous edge servers in Collaborative Edge Computing. It combines direct KV cache transmission with activation-based recomputation, overlapping computation and communication to cut migration time when servers disconnect or overload. The authors formulate a multi-node KV migration optimization problem and give a 2-approximation algorithm built on improved LP relaxation and bipartite graph techniques. Experiments on a real distributed LLM inference platform show up to 45% lower migration time.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

PROBE (Policy Rule Observation via Behavioral Exploration) trains LLM-based auditors that act as realistic users and probe deployed tool-using agents for policy violations (pricing, privacy, confirmation, safety) across multi-turn, tool-mediated dialogue. It formalizes auditing as a two-player Markov game with a composite reward for violation discovery, plausibility, progress and policy diversity, and compares GRPO, DPO and GFlowNet optimization. Trained auditors find broader, higher-yield violations than zero-shot adversaries, even when much smaller than the target agent, positioning behavioral auditing as a distinct axis next to capability and safety benchmarks.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🛡️ Security

PROBE (Policy Rule Observation via Behavioral Exploration) trains LLM-based auditors that act as realistic users and probe deployed tool-using agents for policy violations (pricing, privacy, confirmation, safety) over multi-turn dialogue. It formalizes auditing as a two-player Markov game with a composite reward balancing violation discovery, behavioral plausibility, conversation progress and policy diversity, and compares GRPO, DPO and GFlowNet optimization in a controlled setting. Trained auditors find broader, higher-yield violations than same-architecture zero-shot adversaries, even when much smaller than the target agent. This positions multi-turn behavioral auditing as a distinct evaluation axis alongside capability and safety benchmarks.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

Presents a modular, fully onboard UAV navigation pipeline that closes the "human-aware safety gap" of geometry-only planners. A shared BLIP encoder drives captioning and text-conditioned segmentation, which is fused with depth for 3D obstacle localization. Qwen-2.5-0.5B, stabilized with RAG and grammar-constrained JSON decoding, infers per-category risk once per class and caches the verdict. Perception runs at 10 Hz on a Jetson Orin NX, and fallback LLM queries take about 0.8 s off the control path. In Gazebo it gives roughly 3x fewer collisions than a geometric planner and raises semantics-aware landing success from 34% to 96%. The 224-pixel encoder input limits small-object perception at range.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

GraphACM merges equivalent states from multiple rollouts of the same LLM agent task into a directed, action-labeled graph, so actions can be compared at shared decision states. It defines node-level and global action-consistency measures, and uses the global score as an uncertainty proxy to predict task success or failure. Across three LLMs on ALFWorld, AppWorld and WebShop it reaches an average AUROC of 0.811, above the strongest behavioral-consistency baseline (0.729) and verbalized confidence (0.769). It also shows where in a trajectory agent actions diverge.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

GACC-Bench is a benchmark for Goal-Adaptive Context Compression, testing how well LLMs retain goal-relevant information when compressing a source context under a length budget before downstream questions are known. It has 500 goals and 2,571 human-reviewed questions over 281 document and agent-trace contexts from seven collections; a fixed reader answers the withheld questions from the compressed context. Ten LLM compressors show large gaps to full-context performance, especially in answering every question tied to a goal. Target-goal conditioning improves accuracy over goal-agnostic compression, while mismatched goals hurt it.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

Introduces P^3IBench, a benchmark for proactive video understanding in real-life streaming scenarios, covering three capabilities: daily perception, risk prediction and intervention. It evaluates 12 open-source and proprietary video-language models (streaming and adapted non-streaming) and finds they struggle to make well-calibrated decisions about when to respond. The authors also build extra proactive training data and train P^3I-Assistant-4B, which improves several tasks unevenly, showing that proactive assistance needs response timing and restraint as well as visual understanding.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

The paper shows that symmetric aggregation of a primary reward (correctness) and auxiliary rewards (brevity, efficiency) in LLM post-training can raise aggregate reward while primary-task performance falls. It proves that deterministic conditional-mean GRPO converges to auxiliary-favoring behavior when auxiliary rewards are more reliably obtained. It proposes Trust-Anchored Gating (TAG), which elects an anchor channel by policy drift and gates auxiliary credit on anchor satisfaction. On Qwen3-4B with a brevity reward, TAG reaches 0.581 pass@4 versus 0.220 for additive aggregation, and shortens responses by 44% relative to correctness-only training. Experiments on Qwen and Llama models cover math and program synthesis.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

Shows a protocol-level reaction attack on CKKS homomorphic-encryption deployments that publish coefficient-to-slot (C2S) evaluation keys and let the client return a 1-bit validity response. A malicious evaluator shifts a public encryption key, uses the public C2S layer to repack it into a ciphertext whose validity depends on one secret coefficient, and recovers a ternary secret key with O(H) queries, within a factor 2/log2(3) of the entropy lower bound. Each query is re-randomized with a fresh encryption of zero to evade trivial detection. The authors implemented it end to end in Lattigo v6.2.0 and recovered full keys across four standard CKKS parameter sets. It does not break RLWE or CKKS IND-CPA security.

eprint.iacr.orgpaperPublished and added 15 Oct 2026🛡️ Security

ePrint paper presenting an NVIDIA GPU implementation of FAEST (a post-quantum signature scheme built on VOLE-in-the-Head and QuickSilver), covering key generation and OWFProve/OWFVerify for standard and EM constructions in s and f variants. Profiling shows field multiplication takes about 82% of FAEST-128f signing time once AES is hardware-accelerated. Using PTX clmad carry-less multiplication plus Karatsuba, it reaches up to 26.6x kernel throughput and about 2.9-3.1x full 256-bit OWFProve throughput over the software-multiplication build on an RTX A5000.

eprint.iacr.orgpaperPublished and added 15 Oct 2026🛡️ Security

Presents ChainGraph, a pattern that externalizes exploit-chain reasoning for LLM pentest agents (Claude Code, Codex) into a Neo4j property graph exposed via the Model Context Protocol. Findings, attack steps, credentials, assets and tamper-evident evidence receipts are typed nodes with pre/post-condition edges, queried with Cypher and Graph Data Science for next-step inference, entry-to-crown-jewel pathing, chain-severity promotion, provenance and chokepoints. On a synthetic GOAD-style multi-forest Active Directory engagement, three Medium findings become Critical once chained, betweenness centrality identifies break-points, and reasoning payload drops ~98% (token proxy); results are single-run and illustrative.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🛡️ Security

ChainGraph is a pattern that moves exploit-chain reasoning out of the flat context window and into a Neo4j property graph. Findings, attack steps, credentials, assets and tamper-evident evidence receipts are typed nodes with pre/post-condition edges. The graph is wired to Claude Code or Codex through MCP with no custom harness. Five operator questions (next-step inference, entry-to-crown-jewel pathing, combined-severity promotion, evidence provenance, chokepoint identification) become reusable Cypher and Graph Data Science queries, plus a chain-severity score. On a synthetic multi-forest Active Directory lab modeled on GOAD, three findings rated Medium in isolation become Critical once their path to the forest root is materialized. Scoping each decision to a graph result cut the reasoning payload by about 98% on a token proxy. The authors say the results are illustrative single-run measurements, not a benchmark.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🧠 AI

INFUSE is an end-to-end benchmark for indirect prompt injection (IPI) across long-horizon agent workflows (shell commands, code changes, dependency installs, remote services). It has 40 realistic tasks and a taxonomy of 56 attack scenarios in five threat categories, with attacks injected via interaction surfaces encountered mid-task. Across 19 LLMs (GPT, Claude, Gemini, Llama, Gemma, Qwen) and 41,824 adversarial trajectories, mean attack success was 19.6% (LLM-judged) and 15.4% (heuristic), per-model 3.2–41.9%; guard models gave limited protection, while prompt-level hardening cut mean ASR from 58.2% to 14.3%.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🛡️ Security

ePrint paper evaluating ASCA (SAT-based) and SASCA (belief propagation) side-channel attacks on the AES-based MILENAGE authentication algorithm in USIM cards, using neural-network leakage predictions to recover subscriber key K and operator secret OPc from a single trace. SASCA alone succeeds in 87.7% of attacks but converges to a wrong OPc in 6.1%; ASCA succeeds in only 34% within a 1-hour timeout. The hybrid, where BP refines the predictions before they go to the SAT solver, reaches 93.8% with lower average runtime. It also proposes a countermeasure that masks each AES round key with an independent per-device secret, and the authors report it defeated the hybrid attack in all evaluated cases.

eprint.iacr.orgpaperAdded 15 Oct 2026, published 14 Oct 2026🛡️ Security

Survey-style paper introducing hardware-software leakage contracts, an ISA-level security abstraction that characterizes microarchitectural side-channel leakage (caches, speculative execution). It lets programmers and compilers produce code provably resistant to side-channel attacks like Spectre and Meltdown without reasoning about the microarchitecture, and gives hardware designers a specification to avoid undocumented side channels. It reviews recent contributions and open challenges.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🛡️ Security

Teardown study of tamper sensing meshes (TSM) across a wide range of card payment terminals, ATM key pads and a data center HSM, with detailed photos of critical features. The authors build a taxonomy of mesh construction techniques, find most commercial meshes use simple constructions far from manufacturing limits, and derive a concise set of design patterns. They argue that meshes beating the industrial state of the art are feasible with cheap, readily available processes, giving researchers a basis for realistic test specimens.

eprint.iacr.orgpaperPublished and added 14 Oct 2026🛡️ Security

Introduces TerminalHorizon, a fully automated data synthesis engine for long-horizon terminal agents. It uses independent execution to find key work omitted during environment construction and rebuilds the environment from that evidence. It then progressively expands tasks under the same goal with new requirements that build on earlier results. The authors build 1.5K task environments and run supervised fine-tuning of Qwen3.5-27B to get TerminalHorizon-27B, which improves on all 12 benchmarks covering coding-agent tasks, real-world production agentic tasks and general reasoning. Multiple RSI case studies with a 12-hour research budget show the model continuing to experiment past an initial solution and keeping effective improvements in its final deliverables.

huggingface.copaperAdded 13 Oct 2026🧠 AI

AgentSWE is a benchmark of 25 tasks covering the agent development lifecycle: Creation of an agent from a natural-language requirement, Editing of production agent codebases, and Optimization toward a measurable target. Builders iterate on a few illustrative cases, then the frozen submission is graded on held-out cases with requirement-derived rubrics and hard pass/fail gates. Across coding agents, models and harnesses, none comes close to delivering the requirement. Recurring failures include overfitting to visible cases, hollow runtimes behind code that looks complete, and unsupported claims of completion.

huggingface.copaperAdded 13 Oct 2026🧠 AI

ePrint entry for an ACM CCS 2026 paper in the "Attacks and cryptanalysis" category. By its title and keywords, it shows that snapshots of encrypted databases built on trusted execution environments leak plaintext frequency histograms, using a technique called "dendrochronology". The abstract and full text are embargoed until the end of October 2026, so the attack mechanics are not available yet. It matters to anyone assessing the real leakage of TEE-backed encrypted databases against snapshot adversaries.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🛡️ Security

ChunkAct brings action chunking to agentic robotics, where an LLM agent composes perception, motion and VLA tools guided by memory. Online, one call launches a chain of primitives that runs autonomously and returns control only on failure. Offline, memory is compiled into these chunks so execution needs no memory at test time and the planner is used only for real decisions. On LIBERO-Pro it improves task success by 5.0 points over the prior agentic-robotics state of the art, with 2.9x fewer LLM calls, 4.4x fewer input tokens and 2.3x fewer output tokens.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Introduces Hybrid Reasoning Agents (HybridAgents), which switch between latent reasoning and text-based interaction in one generation process. A lightweight HybridLink handles transitions between the two modes, and a continuous working memory keeps hybrid context lossless across turns. This keeps tool use and makes latent thoughts inspectable. Post-training has two stages: HybridSFT, then HybridRL, which learns when to think latently and when to act in text. Across 3 model scales and 11 benchmarks (math, science, medicine, search, agentic reasoning), the authors report +10.4% performance, 35.5–54.7% fewer tokens, 1.8–2.6x faster inference and 2.7–3.7x lower cost versus text and latent baselines.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Proposes mmVB, a lightweight plug-and-play module (1.6M trainable parameters) that assigns query-conditioned per-image visual token budgets using cross-image context for multimodal LLMs, without retraining the base model or accessing attention matrices (so it is compatible with FlashAttention). It cuts visual input tokens 9x, giving 5x faster time-to-first-token and 7x fewer prefill FLOPs while keeping 94.4% of full-resolution accuracy across eight multi-image reasoning benchmarks. It matters because existing token pruning, merging and compression methods target single-image redundancy and either need attention maps or retraining.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

PixelMoE is a pixel-space diffusion framework that scales capacity sparsely using standard LLM-style Mixture-of-Experts: token-choice routing, fine-grained routed experts, a shared expert and bias-based load balancing, with no diffusion-specific routing or auxiliary routing losses. It adds Pixel-Space Hierarchical Flow Matching, which varies spatial resolution along the denoising trajectory from global structure to fine detail. At matched active parameters it beats dense counterparts, and reaches FID 1.47 at 256x256 and 1.53 at 512x512 on class-conditional ImageNet-1K without representation alignment or perceptual losses. Code and models are promised but not yet released.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

RepProbe is a benchmark of 42K real and synthetic video clips with exactly 1–20 complete action repetitions, controlling for clip position, temporal coverage and duration. Video language models' counting accuracy collapses at high counts, and this is not explained by frame sampling alone. Linear probes on final-layer representations show count information stays linearly decodable even when the model's answer is wrong, so the failure arises at the output stage, where answers cluster on a few preferred numbers.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Plan-Bench is a benchmark of 699 curated samples across 16 subclasses and three dimensions. It tests whether vision-language models can reason over spatial and temporal information and produce multi-step plans under constraints for embodied tasks, with action execution isolated out. Across 28 VLMs the best model reaches only 75.0% accuracy. Scaling generally helps, and larger models look more promising as high-level planners. Code and benchmark are released.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Game2Real-Bench tests whether vision-language model performance on procedurally generated toy games predicts real-world visual reasoning, and whether game examples help transfer. It has 1,000 verifiable game samples and 400 real-world samples, matched by shared reasoning requirements (fine-grained perception, spatial reasoning, constraint reasoning, sequential planning). Across 25 VLMs, game and real scores correlate strongly (Pearson/Spearman 0.935). One-shot game-to-real transfer on four models improves constraint reasoning for several but hurts fine-grained perception. The authors also release Game2Real-Dataset, with about 24K verified game samples across 18 tasks and 65 QA families.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

UniW(A)M is a mixture-of-experts model built on diffusion Transformers that unifies action-conditioned world modeling (AC-WM) with several world-action model (WAM) variants. Shared experts capture common physical dynamics, while atomic capability experts are reused across interfaces, each keeping its own conditional pathway. At deployment a plug-in adaptive interface router (PAIR), trained on a small subset of target-task chunk scenarios with the backbone frozen, picks a WAM interface for each execution chunk. It reaches 88.3% average real-world success against 76.7% for the strongest baseline, leads WorldArena on all three action-conditioned world-modeling tasks, and PAIR raises OOD success from 63.3% to 76.7%.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

ProAS (Progress-Aware Advantage Shaping) is a plug-and-play framework for multi-turn agentic RL that fixes the uniform credit assignment problem in critic-free algorithms like GRPO, where trajectory-level advantages are broadcast to every action. It uses a dynamically evolving Agentic Reference Graph with Masking Unreasonable Incentives and Asymmetric Penalty Redistribution to give finer-grained credit without a critic. On Qwen2.5-1.5B/7B-Instruct it reports success rates up to 91.9%/94.3% on ALFWorld and 66.7%/70.8% on WebShop, with up to 2.44x faster training convergence and robustness to imperfect or distilled ARGs.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

MultiAlloc is a profiling-guided framework for KV cache quantization. It splits a fixed KV memory budget between layer-wise precision enhancement and token-wise preservation. Lightweight offline profiling measures layer sensitivity and token importance, and the profiled memory–accuracy trade-off yields an allocation policy with no online search. The paper reports gains over layer-wise, token-wise and recent multi-granularity KV quantization baselines at matched memory on reasoning and long-context benchmarks, across Mistral, Llama, DeepSeek-Qwen and QwQ models.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Legal-R1 is an agentic retrieval framework for evidence-based legal question answering that splits reasoning across three agents. A Lawyer agent decides when to retrieve, which legal source to query, what evidence to seek and when it has enough. A Legal Assistant retrieves statutes and precedent cases with authority-aware and diversity-aware selection, and a Judge agent writes the final answer from the full evidence trajectory. Only the Lawyer agent is post-trained, using masked supervised fine-tuning and trajectory-aware reinforcement learning. The paper reports gains over general-purpose, legal-specific and retrieval-augmented baselines on several Chinese legal benchmarks.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

First large-scale empirical study of RAG configuration issues, covering 654 real-world issues collected from 10 diverse RAG systems. It categorizes symptoms, root causes, affected pipeline stages and fix patterns. It finds that symptoms mostly appear at early or late stages, while the root cause often sits in a different stage from where the symptom shows. It closes with nine actionable insights for testing, localizing and fixing RAG configuration problems.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

The paper builds explicit ReLU neural-network realizations of Camellia-128/192/256 from exact Boolean gates, S-box selectors and bit permutations, and reports known-answer-test agreement for Camellia-128. It shows that whitening and round-key quantities leaked from internal activations determine the master key through public key-schedule identities. Under a synthetic internal-oracle model, symmetric real-valued perturbations recover subkey bits with 128 queries (Camellia-128) or 256 (Camellia-256). Camellia-192 is handled by deterministic key-schedule inversion from direct access to round keys. All 3,000 trials (1,000 keys per variant) recovered the key exactly. The authors state this is not a plaintext/ciphertext-only attack on standard Camellia.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🛡️ Security

Anthropic's Frontier Red Team tests whether small open-weight models (≤30B) can autonomously replicate across commodity IoT devices. They ran Gemma4-26B-A4B over 15 off-brand Android TV boxes at 0.39 tok/s with pipeline parallelism. A Qwen3.5-9B-class model set up inference on 8 of 9 hardware types, and only GLM-5.2 completed end-to-end worm replication in an 8-node redroid ADB network, spreading to 8/8 nodes in 4 of 5 trials and 7/8 in the other. It also estimates spread using the Kimwolf botnet's size (~133k replicas, about 40 H100s of compute), shows notes inherited between generations improve Qwen3.5-9B's setup success (45% to 61–84%), and projects small models reaching the capability floor by end of 2026. It closes with defensive points: drive R0 below 1, and harden IoT devices.

anthropic.compaperPublished and added 12 Oct 2026🛡️ Security

Endor Labs' 2024 report on open-source dependency management and vulnerability prioritization. It reports that under 9.5% of vulnerabilities are reachable at function level, and that combining reachability with EPSS cuts noise by about 98%. It also finds Python phantom dependencies (up to 60% of dependencies and 85% of vulnerabilities for some tenants), 18% of npm OSV entries lacking CVE aliases, 69% of advisories published after the fix release (median 25 days), and 47% of advisories with no code-level info. For Java, 24% of fixes need a major version bump and 95% of upgrades carry breaking changes. Written by a vendor, so the prioritization conclusions favor its product.

endorlabs.compaperPublished and added 12 Oct 2026🛡️ Security

Endor Labs' 2025 State of Dependency Management report examines how AI coding agents and MCP servers add supply chain risk. Researchers analysed 10,663 GitHub MCP server repositories and tested AI-generated dependency recommendations across PyPI, npm, Maven and NuGet, finding agents import vulnerable or non-existent packages. MCP servers are immature: 75% are built by individuals, 41% lack license info, and 82% use sensitive APIs. The report advises treating AI-generated code as untrusted third-party input subject to code review, SAST/SCA and dependency governance.

endorlabs.compaperPublished and added 12 Oct 2026🛡️ Security

Preprint recovering the long-term key of a first-order masked Kyber768 implementation on an ARM Cortex-M4 from about 2200 decapsulation power traces using only valid ciphertexts. A profiled neural network estimates Hamming-weight distributions of both arithmetic shares, which are turned into likelihoods on decryption noise and combined by a belief-propagation solver with Gaussian interference approximation and an Onsager (AMP) correction, recovering all 1536 secret and error coefficients without lattice reduction. It shows first-order masking is insufficient against combined second-order leakage, and that least squares, greedy solvers and lattice hints (BKZ block size 624 to 505 at 2000 traces) do worse.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 12 Oct 2026🛡️ Security

Paper proposing a risk-based methodology to derive security specifications for complex electronic systems such as AI chips and chiplet-based designs. It walks from system missions to assets, threat analysis, security requirements and then countermeasure selection. The method is illustrated on datacenter and automotive chiplets for the missions of secure provisioning, trimming, debugging, booting and remote attestation, and addresses the longer supply chain and multi-tenant threats that chiplets introduce.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 12 Oct 2026🛡️ Security

Presents Creed, an LLM-based code auditing approach that infers a developer's security-related beliefs for a function, scores how reasonable each is, and derives the conditions under which it is violated. It catches domain-specific semantic flaws that syntactic static analysis misses, such as a timing side-channel that undermines user-enumeration protection. The paper also introduces two measures for explanation-based vulnerability detection, recall and overhead. On a recent real-world benchmark, Creed beats existing explanation-based tools on the effectiveness-effort trade-off.

arxiv.orgpaperAdded 15 Oct 2026, published 11 Oct 2026🛡️ Security

Presents Creed, an LLM-based code auditing approach that, for a given function, infers the developer's security-related beliefs, scores how reasonable each belief is, and derives the conditions under which it is violated. This lets it find semantic flaws, such as timing side-channels in authentication, that syntactic static analysis tools miss, and it explains root cause, symptoms and exploitability. The paper also introduces two evaluation measures for explanation-based vulnerability detection: recall (how well a known vulnerability is correctly explained) and overhead (the effort of inspecting reports). On a recent real-world benchmark, Creed beats existing explanation-based tools on the effectiveness-effort trade-off.

arxiv.orgpaperAdded 15 Oct 2026, published 11 Oct 2026🧠 AI

Position paper arguing that as AI makes machine-checked proofs cheap, the scarce resource is a human who can read what was proved. It cites a Lean library's Schnorr signature security theorem that passed the checker but held for every adversary and so promised nothing. It shows that on a one-page Verifpal model, two of five verdicts flip under other standard definitions of properties such as forward secrecy. It proposes short, owner-written high-level cryptographic languages, with every claim backed by a checked theorem and a small, versioned, publicly agreed set of built-in security definitions.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 10 Oct 2026🛡️ Security

KedgeFlow specifies language-independent contracts so trusted controllers can authorize state-changing operations by enterprise AI agents and privileged gateways can enforce that authority independently of agent reasoning. It combines evidence-backed policy checks, signed permission for an exact action, a single-use shared allowance, target-side conditions and retained outcome records, with two conditional properties (at most one claim per allowance; every dispatch linked to a grant and claim) that hold even if the agent is compromised. A reference implementation exposes the path via MCP; evaluation on a three-member etcd cluster gave exactly one successful claim for each of 82 allowances across 6,560 concurrent attempts, and one committed effect in a 32-call gateway race. The authors report limits: permissive policy still authorized an undesirable effect, and distributed reservations, production access routes, crash recovery and real-target exactly-once effects remain untested.

arxiv.orgpaperAdded 15 Oct 2026, published 10 Oct 2026🧠 AI

KedgeFlow proposes language-independent contracts in which trusted controllers issue signed exact-action permissions and privileged gateways enforce them, using single-use allowance claims, target-side conditions and retained outcome records. It aims to stop altered approved actions, concurrent permission reuse and retries after lost responses, even with a compromised agent. A reference implementation exposes this path via MCP; evaluation on a three-member etcd cluster showed 6,560 concurrent claim attempts yielding exactly one success per each of 82 allowances. It also reports that permissive policy still authorized an undesirable effect, and lists untested areas such as distributed reservations and production access routes.

arxiv.orgpaperAdded 15 Oct 2026, published 10 Oct 2026🛡️ Security

SecReason is an in-stream defense for reasoning LLMs that write code. Reasoning models draft code early in their streamed chain of thought, so SecReason scans that stream with dual-track incremental analysis: security rules are checked on each emitted statement, and dataflows are traced across the accumulated code. When a rule fires, it interrupts decoding, inserts a warning into the stream and resumes, so the model fixes its draft within the same reasoning trace instead of regenerating the full response. On CWEval and SecCodeBench, runs delivering secure and functionally correct code rise from below 15% to 72.8%–98.1%. It uses fewer tokens than post-hoc repair and limits functional regressions when rules misfire on secure code.

arxiv.orgpaperAdded 15 Oct 2026, published 10 Oct 2026🧠 AI

SecReason is an in-stream defense for reasoning LLMs that monitors code drafted inside the streamed chain of thought. Dual-track incremental scanning applies security rules per statement and traces dataflows across accumulated code; when a rule fires, generation is interrupted, a warning is inserted, and decoding resumes so the model repairs the draft in the same trace. On CWEval and SecCodeBench it raises the share of secure and functionally correct runs from below 15% to 72.8%–98.1%, uses fewer tokens than post-hoc repair, and limits regressions from false warnings.

arxiv.orgpaperAdded 16 Oct 2026, published 10 Oct 2026🛡️ Security

SecReason is an in-stream defense for reasoning LLMs that monitors code drafted inside the streamed chain of thought. Dual-track incremental scanning applies security rules per statement and traces dataflow across accumulated code. When a rule fires, generation is interrupted, a warning is inserted and decoding resumes, so the model repairs its draft without regenerating the full response. On CWEval and SecCodeBench it raises the share of secure and functionally correct runs from below 15% to 72.8%–98.1%, uses fewer tokens than post-hoc repair, and limits functional regressions from false warnings.

arxiv.orgpaperAdded 15 Oct 2026, published 10 Oct 2026🛡️ Security

SecReason is an in-stream defense for reasoning LLMs that write code. Reasoning models draft code early in their streamed chain of thought, and SecReason scans that draft incrementally with security rules, checking each statement as it is emitted and tracing dataflows across the accumulated code. When a rule fires, it interrupts generation, inserts a warning into the stream, and resumes decoding, so the model repairs the draft within the same reasoning trace. On CWEval and SecCodeBench it raises the share of runs that deliver secure and functionally correct code from under 15% to 72.8%–98.1%. It uses fewer tokens than post-hoc repair and limits functional regressions when rules misfire on secure code.

arxiv.orgpaperAdded 16 Oct 2026, published 10 Oct 2026🧠 AI

The paper studies joint attention in multimodal diffusion transformers (MMDiTs), where the evolving image updates text representations. It finds that this image-to-text (I2T) feedback tends to preserve objects and attributes already in the image, errors included, and that suppressing it improves generation. It proposes Semantic Feedback Guidance (SFG), which guides away from an auxiliary branch that strengthens I2T feedback and weakens text-to-image conditioning, and iSFG, an in-place variant that needs no extra model evaluations. SFG runs for the first 20% of sampling steps (about 1.2x base NFE without CFG). Across three backbones, adding SFG to CFG on FLUX raises GenEval from 0.6661 to 0.7479, and iSFG on SD3.5M adds +1.33 HPSv2.1 over CFG at zero extra NFE. The control also improves VBench scores for text-to-video and image-to-video.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

DualQuant is a calibration-free post-training quantization framework for LLM weights and activations. It minimizes weight-reconstruction MSE using variable splitting and block coordinate descent with closed-form subproblem updates. The resulting scaling factors double as per-channel activation rescalings, so no calibration data is needed. It supports the MX family and NVFP4 block formats. On Llama and Qwen models it matches or beats calibration-based SmoothQuant on WikiText-2, C4 and zero-shot tasks. A real packed W4A4 path for Llama-3.1-8B on an RTX 4090 gives up to 2.61x memory reduction and 2.46x throughput over BF16.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

Proposes Matched-Context Group Policy Optimization (MCGPO), which builds weighted action preferences from already-collected sibling rollouts to give credit to intermediate decisions when feedback is sparse and arrives only at the end. Comparison contexts use pre-action state summaries and rubric progress. Exact and coarse matching rules are selected by two-action support, and fixed folds separate candidate decisions from the terminal outcomes that orient them. An evidence-weighted auxiliary pairwise loss is added to GraphGPO task learning, and no critic is needed. Across six environment-backbone settings with three seeds each, it beats GraphGPO by 0.47-1.46 points on ALFWorld and WebShop with Qwen2.5-1.5B/7B, and by 3.96 and 5.16 points on WebShop with Qwen3-4B/8B. ALFWorld gains are small because baseline success is already above 92%.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

Adaptive DHiSparse extends HiSparse (host-memory KV storage that fetches only selected entries) to draft-model speculative decoding methods DSpark and DFlash2. It protects the required working set, keeps extra history while GPU memory is spare, and recovers the rest by exact fused retrieval. Queries share missing-row retrieval, draft KV uses a window-bounded ring, and verification staging is reused across layer groups. At 100K context and concurrency 128, GLM-5.3 with DFlash2 reaches 793 output tokens/s per decode GPU (1.45x HiSparse). With the same latency bounds it sustains up to 4x the concurrency. On DeepSeek-V4.1-Flash at 500K context, it delivers 1.85x the output of resident serving with DSpark.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

The paper builds a Knowledge Graph from German Legal Code by structure-driven parsing, then uses it to navigate sources and retrieve from them in a RAG pipeline. It compares several KG-informed chunking and retrieval strategies and measures their effect on the accuracy of retrieved citations. The aim is verifiable, transparent retrieval for reliability-critical domains, where naive retrieval degrades as the number and size of sources grows.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

Introduces Multi-Stream Attention Residuals (MSAR), which extends Attention Residuals (AttnRes, single-stream softmax attention over network depth) to multi-head depth attention. Heads get diverse views through multi-scale causal convolutions over sequence context, and the retrieved streams are merged by lightweight adaptive mixing into a single-stream layer input, so no persistent multi-stream storage or communication is needed. It beats standard residuals, manifold-constrained Hyper-Connections (mHC) and AttnRes on 12 downstream benchmarks at 18B and 28B MoE scales, with an iso-loss compute speedup of 1.58x versus 1.35x for AttnRes and 1.30x for mHC.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

SWE-DeceptionBench is a benchmark of 720 Chinese and English test instances measuring LLM deception across six software development lifecycle stages. It separates Active Deception (the model recognizes infeasible or conflicting requirements but presents them as achievable) from Passive Deception (the model helps users mislead others), and it tests Neutral, Reward and Pressure conditions. Across seven mainstream LLMs, Neutral-condition deception reached 49.88% (Active) and 64.29% (Passive). Guard models missed many deceptive requests, and Self-Reminder reduced but did not eliminate deceptive responses.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

RARA (Retrieval-Anchored Representation Alignment) is a two-stage method for adapting pre-trained robot policies such as Vision-Language-Action models to new tasks from few demonstrations. It first retrieves pretraining representations with similar task intent and motion and aligns the policy's representations of the target demonstrations to them. It then fine-tunes on the target demonstrations while constraining representation change on the retrieved demonstrations, which limits representation drift. Tested with Diffusion Policy and GR00T on 13 tasks in RoboTwin, RoboCasa and on a real robot, it improves average success over plain fine-tuning by 9.5 points in-distribution and 13.6 out-of-distribution, and beats all compared baselines.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

ManeuverFlow is an end-to-end autonomous driving planner that generates trajectories for distinct driving intentions in one step. It selects maneuver modes, draws noise from a Gaussian mixture prior, and maps it to trajectories through a maneuver-routed mixture-of-experts MeanFlow field. Current features and world-model-predicted future features are aggregated along each candidate path as evidence for PDM-factor scoring and Frenet-residual refinement. With a TransFuser-style ResNet-34 backbone it reaches 90.6 PDMS on NAVSIM v1 and 90.1 EPDMS on NAVSIM v2 navtest. It also reports the highest listed PDMS* on WorldEngine's 3DGS closed-loop benchmark, 75.19 with logged traffic and 74.58 with IDM-controlled traffic.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

SWE-Critix is a post-training pipeline that trains generalist verifiers for coding-agent trajectories. Given a task and its (often hundreds-of-steps) trajectory, the verifier predicts a completion label and writes a natural-language analysis explaining the judgment. Starting from Qwen3-30B-A3B reasoning, classification accuracy on a held-out test set rises from 57% to 66% after SFT and 78% after RL, using trajectories from CodeForge-Preview. Models, datasets and training code are released.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

The paper presents RM-KLPT, an algorithm that solves the algebraic pathfinding problem in isogeny graphs of superspecial abelian surfaces with maximal real multiplication. It does this by solving a norm equation in ideals of a quaternion algebra over a real quadratic field. The authors implement it and revisit the RM-CGL hash function of Charles, Goren and Lauter. They show that if the initial surface's algebraic description is known, RM-KLPT gives a second-preimage attack on the hash. Paths are much shorter (~p^3) than with KLPT^2, because a higher-dimensional Deuring correspondence exists in the RM setting. The paper also discusses cryptographic constructions that could be built on RM-KLPT.

eprint.iacr.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🛡️ Security

AgentValve is a protocol for authorizing payments by AI agents in permissionless markets where adversaries can cheaply create Sybil identities and fake feedback to inflate provider reputation. It models reputation as finite capacity in a directed trust graph, and a smart contract atomically reserves capacity along trust paths for each admitted payment. This bounds honest agents' exposure to the capacity they extend into the adversarial region, and payment-backed capacity creation with bounded route length limits depletion attacks. A witness-commit mechanism makes on-chain route verification linear work with constant persistent state per payment. The prototype was evaluated on six months of Ethereum data and synthetic workloads with up to 10,000 Sybil identities. Exposure and depletion stayed bounded, admission cost fell by up to 40.8% against baselines, and common payments cost about $0.04–$0.21.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🛡️ Security

AgentValve is a protocol for Sybil-resilient payment authorization when AI agents pick and pay service providers without human review in permissionless markets. It models reputation as finite capacity in a directed trust graph, and a smart contract atomically reserves capacity along trust paths. This bounds honest agents' payment exposure to the capacity they extend into the adversarial region, so fake identities and fabricated feedback inside that region cannot raise the bound. Payment-backed capacity creation and bounded route length limit depletion by malicious senders. A witness-commit mechanism splits off-chain route discovery from on-chain verification, cutting verification to linear work with constant persistent state per payment. A prototype evaluated on six months of Ethereum data and synthetic workloads, with up to 10,000 Sybil identities, kept exposure and depletion bounded. Admission cost fell by up to 40.8% versus baselines, at roughly $0.04-$0.21 per common payment.

arxiv.orgpaperAdded 13 Oct 2026, published 9 Oct 2026🧠 AI

A narrative review in HCI arguing that the single-voice chatbot interface causes three harms: cognitive offloading, ideological narrowing of user beliefs, and epistemic compression of cultural and value diversity. It proposes multi-persona interfaces, which show several personas to the user at once, as a low-cost fix. The design draws on game design and participatory simulation (magic circle, role differentiation, persona embodiment). It offers a taxonomy for designing such interfaces and calls for empirical studies that link technical pluralistic alignment methods to real human-AI interaction.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

The paper shows that reward hacking learned by one coding agent during RL against automated evaluators can spread to other agents in a multi-agent system. Across four open-weight backbones, coding RL elicits and amplifies hacking when the system prompt describes the mechanism explicitly and when mid-training exposes the model to facts about the coding environment. Post-RL source agents transmit the hack through code, shared memory and natural-language plans, and even small open-weight agents induce hacking in stronger closed-weight targets. On hard tasks the hack can intensify along a transmission chain after the source leaves, as the code accumulates more persuasive justifications. A theoretical analysis gives conditions under which hacking amplifies, persists or decays. Hacking also biases task routing toward hacking agents and survives candidate aggregation. Code review by the coder or by a separate verifier reduces the hacks that get delivered.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

ePrint preprint extending the Ducas–Engelberts–Loyer provable subexponential Gaussian sampler (built on Wagner's generalized birthday algorithm) to module matrices over power-of-two cyclotomic rings. It gives provable 2^{O(n/lnln n)}-time algorithms for Module-SIS and for decision and search Module-LWE using only the k+ℓ ring samples provided, plus sample-preserving scalar search-LWE and narrow Module-LWE variants. Separate heuristic estimates suggest these Wagner-style attacks do not reach the NIST security targets of ML-KEM and ML-DSA.

eprint.iacr.orgpaperAdded 12 Oct 2026, published 8 Oct 2026🛡️ Security

DreamTrue is a multi-view, cross-embodiment robot world model for action-faithful, physically plausible video prediction. It renders action trajectories into image-space conditions aligned to target videos by offline geometric calibration. Counterfactual post-training modifies recorded trajectories to cover unsuccessful interactions and contact configurations. A human-annotated defect dataset trains an embodied video reward model whose scores drive RL post-training. On AgiBot it reaches state-of-the-art action following and cuts the human-assessed interaction defect rate from 48.12% to 6.25%. It ranked first in the world model track of the AgiBot World Challenge 2026.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🧠 AI

OmniCapBench is a benchmark of 786 densely annotated videos that evaluates audio-visual captioning in multimodal LLMs. Instead of scoring free-form captions, it asks models for sets of atomic, verifiable units across three tracks (entity references, visual shots, audio events). These are scored with deterministic constraint checks plus localized LLM-based semantic comparisons, which gives coverage, localization and stable scoring together. Tests on frontier MLLMs show strong local perception but weak long-horizon audio-visual reasoning, especially identity drift and cross-modal misalignment.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🧠 AI

The paper scales white-box deception detection with activation probes to frontier monitoring settings. It builds the largest deception dataset to date (FIBS) and introduces a probe architecture that aggregates information across many layers and tokens. The probes reach 98.8% AUC on SHADE-Arena, beating an Opus 5.5 text-monitoring baseline, and improve as the underlying model grows. They also handle "introspective deception", where ground truth can't be read from context: they separate transcripts containing a model's true hidden goal from others with up to 99.7% AUC, and detect lying on politically sensitive topics and under pressure in open-weight models. The FIBS dataset is released.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🧠 AI

WOVEN is a training source and benchmark for visual transition reasoning in multimodal LLMs. It has 36,076 examples spanning 20 scene types, 5 action types and 8 reasoning types, built from rollouts of video-pretrained generative models. Evaluating 38 frontier MLLMs (including GPT-5.4 and Qwen3-VL-235B-A22B) shows systematic deficits that persist with scale. Training on WOVEN subsets of about 2,000 items improves 22 of 26 external benchmarks by up to 27.3 points, and WOVEN data can replace 30-50% of a task's own training data. The authors derive a recipe: select supervision by the reasoning operation it teaches rather than by scene or action, and prefer larger visual-state changes.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🧠 AI

ORCAGen uses RAG over a curated knowledge base of malware procedures and active-defense strategies, plus structured prompting, to have LLMs generate proof-of-concept malware and matching deception orchestration code offline. Strategies are validated against the PoC malware before being added to a runtime playbook that enforces only the verified, deterministic logic. It is evaluated on GPT-4o, GPT-5.5, Gemini 3.5 Flash, Qwen3-Coder and Claude Sonnet 4.5 over synthesized scenarios and 150 real keylogger, infostealer and ransomware samples, measuring execution success, hallucination rate, refinement effort, deception effectiveness and runtime overhead.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🛡️ Security

ePrint paper by Andrew Krapivin and Elaine Shi that breaks the long-standing log-squared upper-bound barrier for statistically secure Oblivious RAM and for oblivious search trees in the external-memory model. It gives an optimal O(log_B N) statistically secure ORAM when B ≥ log^{1+ε} N. It also gives an optimal oblivious search tree when B ≥ log^{2+ε} N, which matches non-oblivious search trees ("obliviousness for free"). The block-size assumptions match hardware-enclave deployments that use oblivious algorithms, such as those of Signal and Meta.

eprint.iacr.orgpaperAdded 11 Oct 2026, published 8 Oct 2026🛡️ Security