396 results for “pi”

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

WorkFactory is a framework that builds coherent, executable professional tasks for training working agents. It plans an information support graph from professional intents, then uses probe executions to find and repair missing or inconsistent task information. The authors built 112,686 tasks across 612 occupations and fine-tuned Qwen models at 4B, 27B and 35B-A3B (WorkPilot). WorkPilot-35B-A3B rose from 879 to 1565 Elo on GDPval-AA v2.1.

huggingface.copaperAdded 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

RADIOlogy is a training-free, single-pass zero-shot anomaly detection method that uses multi-head attention pooling in vision-language models. It scores anomalies by contrasting average similarity between vision features and sets of normal and abnormal prompts, with no windowed execution. RADIO agglomerative features drive a dense refinement pipeline for pixel-level segmentation, and the method is applied to medical images.

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

VeriCUA is a platform-agnostic framework that verifies task completion by computer-using agents through agentic exploration. The verifier inspects context to build a task rubric, then picks GUI or code checks for unresolved requirements and combines findings under explicit evidence rules. It works online and offline across desktop, mobile and web, and agrees better with reference labels than the baselines, with the largest offline gains on hard cases.

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

Decompose, Estimate, Select is a coverage-aware retrieval pipeline for long-form RAG. It splits a query into subquestions, ranks candidates with a coverage-trained bi-encoder, predicts a document-by-subquestion coverage matrix with a cross-encoder, and picks documents by greedy maximum-marginal coverage. On TREC RAG 2024, α-nDCG@10 rises from 0.625 to 0.702 (gold-nugget bound 0.718). A grounded self-feedback loop raises V_strict by 0.086 over two passes.

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

AutoBench-CT is a modular benchmark for constant-time analysis tools, with manually verified per-violation ground truth across 64 cryptographic tasks. It scores seven tools on operational performance, detection quality and usability. The authors explain why CT-grind (2010) is still the most used tool, list recurring limitations across tools, and propose a composite pipeline for stronger constant-time guarantees. Accepted at ACSAC 2026.

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

Turing LLM post-training combines calibrated multi-teacher on-policy distillation (C-MOPD) with an execution path for dynamically routed mixture-of-experts models. C-MOPD matches each teacher on a local Top-K support with teacher-wise loss normalization and clipping. On Turing-20B-A2B it scores 0.9988 on four-domain integration, beats Qwen3-8B on math and code (85.92 vs 80.33 mean), and cuts consolidation step time from 398.7 to 139.3 seconds in a PPU profile.

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

Cryptanalysis of VTBSAE, a verifier-based PAKE protocol for the Industrial Internet of Things, showing it fails to resist client impersonation after server compromise. The authors propose E-VTBSAEv1, which binds the client point E1 to fresh server randomness, and prove AKE security and forward secrecy in the random-oracle model under CDH. Raspberry Pi 4B tests show runtime comparable to VTBSAE with the same communication and storage overhead.

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

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

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

Paper studying whether self-evolution weakens personal agents' robustness to indirect prompt injection (IPI). A two-phase method has the user ask the agent to follow tasks in external content, then tests with a third-party injected task the user did not authorize. Against the non-evolving base agent, attack success rose 14.0 to 48.8 points under skill evolution and 3.0 to 39.3 points under reinforcement learning. Code is released.

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

Colin O'Flynn's paper shows that the flash accelerator in most microcontrollers produces the same timing collisions as a cache, so cacheless Cortex-M devices remain open to cache-collision attacks on AES. AES keys were recovered from as few as 750 traces across six cryptographic libraries, eight architectures and over twenty devices. It also attacks SPI bootloaders, Ethernet, CTAP2 over USB and 802.15.4, and releases the open-source tool FlashWhisperer.

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

Shor's algorithm compiled on cat qubits is an IACR ePrint (2026/2665) giving an end-to-end resource estimate on superconducting cat qubits, using repetition and LDPC cat codes. Factoring RSA-2048 needs about 23,000 cat qubits and 3.14 days, assuming phase-flip noise p_Z = 5e-4 and no bit-flips. secp256k1 ECDLP needs at most 30,000 qubits and 1 hour. A space-for-time trade runs RSA-2048 in 1.25 hours on about 611,000 qubits.

eprint.iacr.orgpaperPublished and added 28 Oct 2026🛡️ Security

This paper replicates Chen et al.'s study of LLM-based smart contract vulnerability detection, first on GPT-4 and then on Mixture-of-Experts, RNN-like and State Space Models. The exact replication confirms the high-recall, low-precision pattern on GPT-4. The differentiated replication finds the pattern does not transfer uniformly: GPT-4 and Gemma-3 behave alike despite a roughly 100x parameter gap, while two MoE models diverge.

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

This arXiv paper (October 2026) replicates Chen et al.'s study of LLM-based smart contract vulnerability detection. It confirms the high-recall, low-precision pattern on GPT-4, then tests Mixture-of-Experts and RNN-like models plus a preliminary State Space Model run. Gemma-3 matches GPT-4's profile despite far fewer parameters, while two Mixture-of-Experts models diverge, so architecture family alone does not predict detection behavior.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🛡️ Security

JevAct is a runtime verification framework that intercepts selected LLM agent actions before execution and returns feedback for bounded revision. It compiles domain policies into executable state checks and structured questions scored by a prefill-only decision model, giving probability-based judgments without generation. Tested on eleven agents with tau^2-bench Airline and Telecom and BFCL, it gained consistently on Telecom and BFCL and was mixed on Airline.

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

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

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

The paper proposes POB-ICM (contingency matrix with Chi-square independence testing) and POB-UNBP (a uniformity null model under permutation-based evaluation) to detect positional bias in top-1, single-turn LLM recommenders. On a RAG product-recommendation testbed with 2000 queries and 20 permutations each (40,000 instances), both methods reject positional independence in all evaluated models. Three metrics quantify the effect, and the bias persists across candidate-set size and temperature. Code is public.

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

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

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

EnvSched is an online scheduler for agentic reinforcement learning that picks which environment-task pairs to roll out next. It builds a dual-relation graph over environments and tasks, propagates past rollout statistics to predict within-task reward variance, and adds environment quotas and a similarity penalty. On Qwen3-8B it scores 48.81 average on BFCL-v3, tau-Bench and ACEBench-Agent after 40 steps, above random selection at 120 steps.

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

This paper defines a character naming task for films: given a name spoken in dialogue and its video context, the model must pick which visible character bears that name. It provides a dataset with annotations for character naming and first appearances, a video-and-dialogue model, and a comparison with proprietary multimodal LLMs. It also proposes a progressive framework that builds a character bank as the film plays, without cast lists or actor photographs.

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

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

p-DCIM is a 28 nm SRAM digital compute-in-memory macro (0.16 mm2, 0.41 mW) that works as an Ising machine for RAG evidence selection. Selection is cast as a QUBO problem, and the same MAC datapath computes embedding similarities and annealing steps. Replacing LLM reranking with it gives a selection-energy estimate over six orders of magnitude lower and cuts total energy per query by 67%, with comparable token-level F1 on the evaluated examples.

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

TraceTrial-Bench is a benchmark that scores agent task performance and agent harness reliability separately. It has 95 mechanism-oriented tasks across seven components: context, execution, governance, lifecycle, observability, tools and verification. Across 4 harnesses, 9 models and 3,420 runs, macro-averaged harness scores range from 59.8% to 72.0%, and 31.1% of runs succeed despite failing at least one harness criterion.

huggingface.copaperAdded 27 Oct 2026🧠 AI

Your Wish Is My Commit is a controlled study of over-compliant refactoring in Codex and Claude Code, run across five models, 50 algorithmic tasks and 10 object-oriented tasks. Over 3,000 turns of repeated "refactor further" prompts, no model stopped on its own: each rewrote up to 31x the original codebase before first stopping and resumed edits after declaring the task complete. The paper defines six behavioral signals for detecting over-compliance.

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

"Cheap AI-Assisted Reverse Engineering and What It Will Do" is an October 2026 arXiv paper (Computers and Society) arguing that generative AI strains the law of reverse engineering. AI systems can identify hardware components, extract firmware, decompile object code and clone apps, which removes the cost barrier that laws rely on. The authors map these practices onto trade secret, copyright, anti-circumvention, computer misuse and contract law, and sketch possible legal responses with their trade-offs.

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

An empirical study of security concerns in the OpenClaw AI personal assistant framework, using 25K+ posts from 13 subreddits (162 security-relevant) and 358 security-tagged GitHub issues (180 retained). It maps 521 CWE instances across 59 weakness types, concentrated in access control, sensitive-data exposure, injection and unsafe execution. It extracts 543 practitioner mitigations into seven strategies and reconstructs a sensitive-data exposure path with dummy secrets.

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

Scope-Gap Injection (SGI) is an indirect prompt injection method for multi-stage LLM-agent safety pipelines, tested in the IJCAI 2026 OpenClaw Agent Security Attack Competition. The visible task and skill stay benign while target identifiers, bypass parameters and authorization steps sit in an attachment written as a business worklist, which the S2 LLM judge appears not to inspect. SGI bypassed all three stages (static scan, LLM judge, live audit) for 5 of 19 targets, best F = 0.385. Attachment placement had 78% S2 acceptance versus 23% in-skill. The paper closes with defenses that align evaluator scope with agent channels.

arxiv.orgpaperAdded 27 Oct 2026, published 26 Oct 2026🛡️ Security

SynTrace is a single-round compiler that adds accountability to linear private information retrieval (PIR). The client mixes the retrieval query with m audit queries, so audit syndromes identify up to d deviating servers among t colluding ones before reconstruction. The paper proves perfect query privacy and zero false identification within the threshold. For Shamir-based PIR it needs k=t+d+1 servers. For a 32 KiB record from a 1 GiB database it uses 2.25 MiB, against 8.96 MiB for the Eriguchi, Kurosawa and Nuida (TCC 2022) homomorphic-MAC construction.

eprint.iacr.orgpaperPublished 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

UTS #39 (Unicode Security Mechanisms) is the Unicode Consortium's technical standard for detecting identifier spoofing. It defines the General, IDN and Email security profiles for identifiers, confusable detection through skeleton and bidiSkeleton mappings, and mixed-script detection with resolved script sets and Restriction Levels. It also covers limited contexts for ZWJ/ZWNJ and the confusables and identifier data files. The fetched text is truncated, so later sections are not covered.

unicode.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

Philippe Gaborit proves that maximum-likelihood decoding of Gabidulin codes is NP-hard even for dimension one: deciding rank distance at most n-2 from the code {λg} is NP-complete. The reduction is deterministic, so Rank Syndrome Decoding and Rank Minimum Distance for F_{q^m}-linear codes are NP-complete under deterministic reductions, where the earlier reduction was randomized. It goes through a new pivot problem, Twisted Square Membership.

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

This paper presents a pipeline that uses persona-conditioned vision language models to surface interpretive ambiguity in visualizations. Topic-relevant personas elicit candidate takeaways, and a Target-Pattern-Scope framework lets LLM judges label statement pairs as Compatible, Disjoint or Opposing, which are summarized in an ambiguity profile. The authors benchmark VLMs on chart comprehension per chart type, check labeling against human annotations, and run case studies on public visualizations and a redesign.

arxiv.orgpaperAdded 27 Oct 2026, published 25 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

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

The paper builds two corpora, EventTyp and the human-validated EventTypGold, to study how people infer typicality and atypicality of events from what is said. Large-scale human and LLM experiments show that three CUES predict these inferences in humans: Commonsense Knowledge, Utterance Emphasis and Snark. In most settings LLMs lack the commonsense knowledge to judge an event's general typicality, so they struggle to follow the CUES.

arxiv.orgpaperAdded 27 Oct 2026, published 23 Oct 2026🧠 AI

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

GordieHowe is a language, certifying compiler and zero-knowledge proof system for building anonymous credentials on standardized signatures (ECDSA, RSA, ML-DSA). It proves an ECDSA-signed mobile driving license presentation in 64.8 ms with a 276 KiB proof, 10.1x faster than Google's Longfellow, and an ML-DSA-44 signature in 87.3 ms with at least 116 bits of quantum security. Compiler output and security theorems are checked in Lean. It introduces series-parallel IOPs and extends the BCS transformation in the QROM.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 23 Oct 2026🛡️ Security

Accordion is a mixture-of-experts language model (Accordion-6B-A0.5B) that supports five inference budgets through fixed-prefix layer looping, with the minimum budget being the unlooped model. After 3.6T pretraining tokens, deeper inference raises the macro-average over 37 tasks by 3.01 points, and the middle budget captures 89% of that gain with half the extra layer executions. SFT and RL keep the budgets, merging three domain experts via parameter-group merging and budget-matched multi-teacher on-policy distillation.

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

When Agents Fail: Model or Harness--Who's to Blame? proposes five measures for evaluating agent harnesses together with their underlying models: Valid Completion Rate, Model-Harness Fit, Cross-Model Fit, Interaction Residuals and Token Load. It tests four harnesses (OpenCode, Codex CLI, Pi CLI, DeepAgents) with four models on six benchmarks, across 111 settings and 52,168 trajectories. Harness rankings change by model and benchmark. OpenCode ranks first in cross-model performance.

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

Preprint (IACR ePrint 2026/2611) proposing a federated learning defense that replaces gradient detection with zero-knowledge-proof sample-level audits of client datasets. A staking mechanism forces attackers to train on discovered poisoned samples with flipped labels, producing counter-gradients that remove the backdoor without knowing the trigger. ZKP commitments lock the training data, which blocks adaptive attacks and gradient tampering. On CIFAR-10 under Dir(0.1) non-IID, it holds attack success rate below 6% against distributed backdoor and edge-case attacks.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 23 Oct 2026🛡️ Security

MART-GRPO is a variant of group-relative policy optimization for vision-language-action (VLA) reinforcement learning. It uses a LoRA-fine-tuned VLM to find the last visible error without recovery in a failed rollout video, keeps failure feedback up to that point, and drops it afterward. Martingale stopping conditions show this preserves the expected GRPO gradient and can reduce covariance. With OpenVLA-OFT on RoboTwin 2.0, it raises success rates over full-trajectory GRPO across multiple tasks.

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

AKV introduces cache engineering, which lets LLM agents control their active KV cache with drop (exclude cache from future attention) and repos (adjust retained keys' position encodings) without editing the trajectory or re-prefilling. The AKV serving engine keeps a stateless API while managing stateful cache reuse, eviction and restoration. A tool-response rolling-drop policy raised benchmark scores by 1.7 points and cut cost 27.7% versus threshold-triggered summarization.

huggingface.copaperAdded 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

Balsamic is a complete homomorphic Unbalanced Oil and Vinegar (UOV) signer, used to build one-round threshold UOV signatures from a packed discrete-CKKS FHE circuit. A paired-pivot linear solver and pivot-success certificates allow encrypted retry selection. In 2-out-of-3 semihonest signing, each online partial signature takes 10.7 s on an RTX 6000 Ada and 7.77 s on a GB300, with 5.25 MB per response.

eprint.iacr.orgpaperAdded 24 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

EpiSEM-Bench is an executable benchmark of 300 matched unsafe/safe task pairs across six domains. It tests whether tool-using LLM agents take prohibited actions when a tool response omits a safety-relevant fact, even though the request is benign and all returned statements are true. It finds large variation across ten models, and recovery access cuts unsafe execution for all ten.

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

BeNice! is a post-training method for looped language models that broadcasts the final recurrence's KV-cache into the first recurrence, so one cache can serve all recurrences. It follows a first stage that uses stochastic depth in pretraining to induce contractive residual dynamics. It matches standard finetuning's unshared validation perplexity and composes with multi-latent attention, Kimi delta attention and fp8, mxfp8 and nvfp4 cache quantization. In simulated low-memory long-context inference it raises cache hit rates by up to 31.4% and throughput by up to 23.6%, and cuts p90 time-to-first-token by up to 83.6%.

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 studies reverse sycophancy: whether users bend toward a confident, consistent, wrong LLM. In a within-subjects experiment, 32 participants learned science topics from five GPT-5-mini assistants with 0 to 100% of facts replaced by falsehoods. Scores fell from 7.4 to 2.9 out of 10, planted falsehoods were chosen on 60% of falsified items against 11% of correctly taught ones, and confidence stopped tracking correctness. It introduces a baseline-corrected Human Deference Index.

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

CellTrack-Bench is a benchmark that tests vision-language models on microscopic cell tracking and compares them with specialised trackers. It has 1,537 problems covering 9,041 cell-linking decisions, drawn from 15 cell-tracking datasets across four imaging modalities, with a hard subset built from failures shared by distinct algorithmic trackers. The authors compare environment harnesses, benchmark 9 closed-source and 13 open-weight models, and analyse failure modes.

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

Paper by Shivansh Jaiswal and Somitra Sanadhya (IACR ePrint 2026/2601) on integer coordinate families for threshold RSA signatures with prime exponent e. It shows the CT-RSA 2026 Ng-Lau scheme admits trivial forgeries and has a flawed proof. It proposes repaired scheme PiIC and integer secret-sharing scheme PiZ for any prime e and any (t,n), both proven TS-UF-CMA secure under the standard RSA assumption. It also gives super-polynomial coordinate lower bounds for pairwise families.

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

This empirical study benchmarks recent LLMs against fine-tuned encoder-decoder models on knowledge graph-to-text generation, using reference-based metrics and graph-grounded, reference-free LLM-based evaluation validated against human annotations. Recent LLMs narrow or close the gap on reference-based metrics and do better on graph-grounded ones. Prompted models make fewer contradictions and triple omissions, and fine-tuned models' errors grow faster with graph size.

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

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

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

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

SBFS-FT (symmetry-based few-shot fine-tuning) adapts the MatterSim 1M universal machine-learned interatomic potential with low-rank adaptation on a small set of compounds sharing one crystal symmetry. Tested on half-Heusler (space group 216), space group 225 and spinel (227) datasets, it cut lattice thermal conductivity error 2-3 times versus the base model. It matched per-compound fine-tuning while taking 5-7 times less compute for 100 compounds.

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

Table-free constant-work BCH decoding by algebraic index elimination is an IACR ePrint paper (2026/2580) that replaces the table scans in Walters and Sinha Roy's constant-work BCH decoder with fixed circuits over public addresses. It proves correctness through radius t and trace regularity, removes 5.2 and 7.2 KiB of tables, and runs 35.7 to 82.4 times faster on an i7-13700. Ordinary C masks compiled to received-word-dependent branches under Apple Clang 21, so the authors use a compiler-opaque primitive and run timing screens on four platforms.

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

Survey and human-coded evidence map of 118 RAG defense papers, organized by measured endpoint across a seven-family taxonomy (content disclosure, formal privacy proxies, access control, corpus-use detection, pipeline integrity). Only 17.8% of studies use disclosure or reconstruction as the primary endpoint, and most use non-adaptive attacks on general QA data. A testbed shows a normalized 8-gram guard cuts 20-token copying to 0.0%, yet a three-query adaptive attacker recovers 48.3% of hidden facts on Qwen3-8B.

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

RDK-CKKS is a framework for rigorous probabilistic error analysis of CKKS homomorphic encryption. It injects randomization keys (encryptions of zero) at key-switching and rescaling so each operation's error has provable tail bounds, which noise flooding needs for IND-CPA^D and threshold settings. It gives a full rigorous analysis of binary bootstrapping, at about 3% extra modulus and 27% extra key communication over bootstrapping keys.

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

Broadcast and Trace from Lattices is a 2026 ePrint preprint by Jeffrey Champion. It builds collusion-resistant broadcast and trace with public tracing from attribute-based encryption with succinct ciphertexts and lockable obfuscation, both known from decomposed LWE. Ciphertexts are sqrt(N)·poly(λ, log N) in size, with poly(λ, log N) keys. It is the first such scheme based on a simple, instance-independent lattice assumption.

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

Sign Here is an arXiv paper (October 2026) testing whether building packages from source, instead of pulling from registries, prevents malware. It aggregates 47,066 malicious npm and PyPI packages from five datasets and indexes 3.9 million npm and 764 thousand PyPI packages. Checking for an attributed source blocks 87.7 to 99.9 percent of malware depending on vetting level, but only 47.4 percent of benign packages attribute a buildable source.

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

InterWebCoder is a framework for interactive webpage code generation by multimodal LLMs. It synthesizes self-contained webpages with executable test scripts from recorded interaction trajectories, repairs failures using runtime evidence, then trains with test-ID-weighted SFT and GRPO using test pass rates and visual similarity as rewards. The pipeline yields 1,736 verified samples from 2,058 tasks. A 9B model scores 48.54 on IWR-Bench and 48.42 on Vision2Web, and reward latency drops over 97%.

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

AgenticRoboRL is a framework for post-training robot planners across different harnesses, combining supervised fine-tuning with multi-turn reinforcement learning. Harness-specific adapters link recorded planner interactions to task outcomes, so one RL pipeline covers both VLA skill invocation and control-code generation. On LIBERO-PRO, a 9B planner rose from 18.1% to 71.0% without external memory, near Codex (GPT-5.5) with memory at 72.1%. Code and checkpoints are promised.

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

Calibrated accommodation pairs (CAP) is a preference-data method that rewards chat models for matching a user's dialect density while keeping Standard English for Standard-English users. The paper traces SFT, DPO and RL stages across 10 open model lineages and finds post-training standardizes replies, while the DPO update still favours British and Australian spelling over AAE and Singlish. On a fully open model, CAP improves convergence on unseen features without added dialect for Standard-English users or lower instruction following.

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

Benchmark of the three NIST post-quantum standards, FIPS 203 (ML-KEM), FIPS 204 (ML-DSA) and FIPS 205 (SLH-DSA), measuring speed and energy for key generation, signing and verification. Tests ran on Raspberry Pi 1, Zero and 4 and on laptop hardware, using OpenSSL 3.5 and a FNIRSI FNB58 USB power meter. ML-KEM and ML-DSA match classical elliptic-curve cryptography, while SLH-DSA signing on 32-bit devices cost up to 243% more energy than on 64-bit.

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

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

ASTOR is an inference-time framework that has an LLM map a natural-language decision problem to a grammar-guided abstract syntax tree, then deterministically compiles it into executable optimization code. The paper also introduces SoftOR, a benchmark of natural-language problems with soft constraints. On SoftOR, ASTOR has the best aggregate accuracy among comparable single-request and multi-agent LLM methods, and it stays competitive on hard-constraint-only autoformulation benchmarks.

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

The paper analyses DeepDistinguisher, a transformer-like model that tells Goppa codes apart from random linear codes. Saliency maps and attention visualisation reveal a low-weight-row artifact in the random training instances that the model partly relies on. A pairwise-XOR rule inspired by minimum distance correlates strongly with the model's predictions, and the model loses most of its power on random matrices meeting both constraints. This suggests it exploits general combinatorial properties, not hidden algebraic structure.

eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026🧠 AI

TokenGate is a small drop-in component that adds end-to-end learnable token pruning to trained LLMs for long-context prefill. It uses token-gated attention, inspired by Mixture-of-Experts, to re-weight attention logits with token importance scores, which gets gradients through the non-differentiable top-K step. The authors report 1.4-11.3x faster time-to-first token and 1.5-3.4x lower peak memory with no accuracy loss, on Qwen3 and Qwen3.5.

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

EpiLens is a lightweight, open-source retrieval-augmented generation package that applies a fixed retrieval template and output schema to every paper in a corpus and links outputs to source passages. On a corpus of 2,297 epidemiology papers, its classifier matches or exceeds supervised and zero-shot baselines on four tasks without task-specific training data, scored against 214 manual labels. It also compares model-based study shares across parameters and pathogens.

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

The paper traces sycophancy across 741 checkpoints from 30 open model lineages, from early pretraining to released models. It plants initial verdicts on moral scenarios and measures how far models follow a user's opinion or drop their answer under pressure. The behaviour usually exists before post-training, but which questions a released model yields on is set mainly during post-training. For Tülu-3 8B, its own preference-stage checkpoint removes 85% of remaining prediction error, a sibling model only 19%.

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

This paper presents a hybrid framework for axial compressor flow that couples a Multi-Fidelity Ensemble Kalman Filter (MFEnKF) with machine learning. The filter merges PIV experiments with steady RANS and unsteady hybrid RANS/LES simulations. A neural network trained on the enriched data predicts Navier-Stokes volume forcing terms from velocity, velocity gradients and turbulent viscosity. A Mixture of Experts fixes local errors near blade tips, giving lower experimental discrepancy than baseline RANS.

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

SpanGuard is an indirect prompt injection (IPI) guardrail that splits detection into two steps. Lightweight unsupervised segmentation first locates candidate instruction blocks in tool outputs, then a small LLM judges whether each block is authorized by the user's request. The authors report a 0.32% attack success rate with negligible benign utility loss, and the guardrail still works with substantially smaller models.

arxiv.orgpaperAdded 21 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

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

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

IISFA (Ineffective Instruction-Skip Fault Attack) targets bitsliced block cipher implementations protected by redundancy countermeasures. Skipping one bitslice instruction leaves the result unchanged only when all m S-box inputs fall in an ineffective set, so each released ciphertext restricts all m subkeys. The authors give known- and chosen-plaintext key-recovery procedures and expected injection counts, and recover keys in every trial on GIFT, SKINNY and PIPO implementations on an 8-bit AVR microcontroller using clock glitches.

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

HERMES is a privacy-preserving inference (PI) system optimised for multi-token LLM decoding rather than prefill. It swaps high-round nonlinear protocols for compute- and bandwidth-heavy ones with fewer rounds, and adds a homomorphic-encryption matrix-multiplication protocol that keeps the KV cache encrypted on the server and uses grouped-query attention. On Llama 3.1 8B Instruct it cuts TPOT by up to 2.7× and total PI latency by up to 2.1×.

eprint.iacr.orgpaperAdded 19 Oct 2026, published 18 Oct 2026🧠 AI

"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

PIAtlas is a taxonomy of prompt injection attacks organized by how the injected instruction subverts the model, with six mechanism classes as roots of a technique and sub-technique hierarchy. The authors release PICorpus, a license-clean collection of public prompt-injection datasets, and map thousands of records to PIAtlas under a human-verified protocol. Results show attacks combine several techniques from few mechanisms, agent benchmarks cover only part of the space, and adaptive attackers favor mechanisms that curated benchmarks underrepresent.

arxiv.orgpaperAdded 21 Oct 2026, published 18 Oct 2026🛡️ Security

"GKS FHE: Security Analysis" is a Cryptology ePrint Archive preprint (2026/2523, October 2026) giving a formal security analysis of the Gribov-Kahrobaei-Shpilrain private-key fully homomorphic encryption scheme. The abstract says the scheme needs no bootstrapping or de-linearization, gives bit-exact decryption, supports native encrypted search and has unbounded depth. The page shows only the abstract, so the analysis's methods and results are not visible.

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

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 a fixed-point C implementation of the Mitaka signing algorithm, a simpler, parallelizable Falcon (FN-DSA) variant. It replaces MitakaZ's wide integral Gram root decomposition with a fixed-point square Cholesky decomposition, which cuts the cost of integer Gaussian sampling, and proves bounds on intermediate values and representation errors without empirical assumptions. Signing averages 361 kcycles (Mitaka-512) and 707 kcycles (Mitaka-1024) on AVX2, and 4.0 and 7.8 Mcycles on an STM32F411 Cortex-M4F. This makes floating-point-free lattice signatures practical on constrained devices.

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

Zippel is a language, compiler, runtime and analyzer for writing zero-knowledge proof systems in syntax close to the pseudocode in cryptography papers. Its type system and runtime remove vulnerability classes by construction, for example by synchronizing transcripts automatically and applying the Fiat–Shamir transform correctly. Its static analyses check completeness and soundness from the protocol description alone. The authors implemented 35 proof systems, including Sumcheck, Schnorr, KZG, Groth16, Bulletproofs, Hyrax, Spartan, Dory, HyperPlonk, DeKART and Pari. They report performance competitive with hand-written multi-core implementations.

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

SEER-Repro is a benchmark of 20 published papers based on the SEER cancer registry, with the data needed to reproduce their findings. An LLM agent reproduces 77.4% of 2,190 atomic claims. A second agentic pipeline finds alternative analytical choices and reruns the analyses, flagging potentially consequential choices in 9.5% of fully reproduced findings for expert review.

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

FuTransformer is a CUDA implementation of a Llama-style Transformer block for NVIDIA Hopper GPUs. It uses FP8 prefill with fused GEMM epilogues and INT4 weights plus an INT4 KV-cache copy for decode, with a block-diagonal Hadamard transform to cut cache quantization error. On one H100, prefill is up to 1.48x faster than compiled PyTorch FP8, and INT4 decode is 1.3-2.4x faster than the faster PyTorch baseline. Llama-3.1-8B decode takes 2.8 ms per step at batch one, versus 8.1 ms for compiled transformers. WikiText-2 perplexity rises from 6.24 to 7.23 with round-to-nearest INT4 weights, or 6.99 with GPTQ.

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

TileDRAM is a compiler-runtime toolchain that adds tile-to-DRAM layouts as a compiler-visible abstraction for compute-unit-local 3D DRAM GPUs. It extends GPU Linear Layout to DRAM placement and picks layouts per tile access signature using row-buffer behavior. On 108 MoE FFN cases in Accel-Sim/Ramulator2, row-conflict rate fell from 75.7% to 15.2%, with a 1.77x geomean speedup. Overhead on an RTX 3090 is 0.17%.

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

CHAMP is a Cayley hash function that hashes binary strings by mapping each bit to a 2x2 matrix over a finite field and multiplying the matrices. The product's semigroup structure gives the hash homomorphic properties. The authors (Demin, Ovchinnikov, Shpilrain) discuss its collision resistance, implementation and performance. Cayley-style hashes have a history of structural attacks, so the security analysis is what a cryptographer would check.

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

LILIE (Language-Informed and Lossless Image Encoder) is an invertible image transform built from a normalizing flow transformer, trained jointly with a vision-language model via next token prediction so captions can be recovered from noised features. The encoder supplies a language-informed loss for text-to-image generation. JiT models trained with it cut gFD from 66.84 to 53.01 against a pixel-only baseline on the GPIC dataset. Code, encoders and models are released.

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

Introduces SLEEK, a QC-MDPC code-based KEM using a three-iteration parallel bit-flipping decoder and a closed-form decoding-failure-rate (DFR) estimate, enabling parameters with analytically bounded failure probability and standard IND-CCA transforms. SLEEK-LT targets IND-CCA for long-term keys; SLEEK-E targets IND-CPA for ephemeral keys using a new QC key structure with security equivalent to QC-MDPC and cheaper key generation. Parameter sets cover 80/128/192/256-bit security, and the optimized AVX2 implementation gives ciphertexts 4.9x smaller than HQC (47% slower) and 1.3x–2.7x faster than BIKE.

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

ePrint paper presenting the first optimized implementation of the BF-max bit-flipping decoder for the code-based KEM BIKE, combined with other decoders to trade off decoding failure rate, runtime, public-key and ciphertext size in ephemeral-key settings. It cuts public-key and ciphertext sizes by 7.8% versus LeanBIKE at similar speed and DFR, and gives a combined size 22% below BIKE and 2.79x below HQC. It shows BIKE can fit constrained protocols such as IKEv2, which matters for practical post-quantum migration.

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

Presents a language- and leakage-model-agnostic framework for provably secure compilation of probabilistic side-channel countermeasures. It uses a deterministic leakage-preservation relation that implies preservation of d-NI, d-SNI and cryptographic constant-time without probabilistic proof obligations. The authors prove in Rocq that a model of the Jasmin compiler's core pipeline and several optimizing passes preserve security in the value-probing model with glitches. They also add source-level annotations to harden against transition leakage and release libmasking, an arbitrary-order masked gadget library in Jasmin that compiles to ARM and RISC-V, with C bindings and hardware benchmarks.

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

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

ePrint paper presenting the first fully masked hardware implementation of HQC (key generation, encapsulation, decapsulation) on FPGA. It introduces a mixed Boolean/arithmetic masking for sparse-dense vector multiplication, proven first-order secure in the transition- and glitch-robust probing model and PINI-composable, at only 3% extra cycles and 81 random bits per cycle. It also shows a profiled horizontal side-channel attack on Rotate-and-Add multipliers that recovers support indices within ±8 positions from 200 traces despite TVLA showing no leakage after 10 million traces, and adds integrated hiding countermeasures costing 1.7% latency.

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

Ignored, Not Refused audits 33,000 trajectories from eleven open-weight agents (2-14B parameters) on InjecAgent and AgentDojo. A label-blind LLM judge finds most failed indirect prompt injections are ignored (76-80%) rather than refused (17-18%). Changing only the system prompt moves InjecAgent ASR from 0.2% to 43%. The Attack-Task Outcome Matrix (ATOM) shows 21.7% of AgentDojo episodes were safe but left the user task incomplete. The corpus, judge prompt and code are released.

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

"Ignored, Not Refused" audits 33,000 trajectories from eleven open-weight agents (2-14B parameters) on InjecAgent and AgentDojo. A label-blind LLM judge finds 76-80% of failed injection attacks were ignored and 17-18% refused. Changing only the system prompt moves InjecAgent attack success rate from 0.2% to 43%, and the ATOM matrix shows 21.7% of AgentDojo episodes were safe but left the user's task undone. The corpus, judge prompt and code are released.

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

ePrint paper presenting safeRNSgcd, which computes the single Bézout coefficient needed at the deepest level of Falcon's NTRU solver while keeping large integers in a residue number system. Centered explicit CRT recovers the exact low bits needed by SafeGCD batches using single-word fixed-point arithmetic. A C implementation integrated into ntrugen reduces inversion and private-key basis-generation cost on a Raspberry Pi 5 and two x86-64 CPUs, versus the original ntrugen and a variant that skips the unused coefficient.

eprint.iacr.orgpaperAdded 17 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

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

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

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

The paper introduces 3DVerifierBench, a 10-task benchmark for generative verification of 3D content. It scores accept/reject decisions, rejection explanations, and responses to controlled changes in specifications and candidates. A human study shows a 21.6-point gap to the strongest tested model, GPT-6 Astra. The authors also build a pipeline of multi-view fact extraction, controlled 3D editing and native annotations. It yields 14,126 training examples with contradiction explanations and linked counterfactuals. Verification-guided selection raises complete factual success from 26.7% to 90.0% with oracle selection over sixteen candidates, and human-guided recursive refinement satisfies all seven requirements for every input in an eight-input study.

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

Proposes protecting the retrieval datastore in RAG systems by applying the same random projection to datastore embeddings and query embeddings. This provably preserves query-item semantic similarity while substantially mitigating data extraction attacks. Across multiple RAG architectures and datasets it beats prior differential-privacy-based methods on retrieval and generation quality, with comparable empirical privacy under strong attacks, making it a practical baseline for privacy-preserving RAG.

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

Proposes applying the same random projection to both datastore embeddings and query embeddings in RAG systems. The authors claim this provably preserves query-to-item semantic similarity while substantially mitigating datastore data-extraction attacks. Across several RAG architectures and datasets it reportedly beats prior differential-privacy-based defenses on retrieval and generation quality, with comparable empirical privacy under strong attack models. It is presented as a lightweight baseline for privacy-preserving RAG.

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

The paper fine-tunes a pretrained CNN wind-field super-resolution model with differentiable topology-inspired auxiliary losses on speed, gradient magnitude, soft high-speed level sets and local maxima. It also uses fixed-pair supervision at ground-truth persistence-pair endpoints located offline with the Topology ToolKit. A 16-configuration loss study on a fixed 168-field benchmark finds that gradient supervision gives the strongest persistence-diagram improvement, while fixed-pair supervision gives the most consistent joint improvement in persistence-diagram and merge-tree distances. The combined objective cuts bottleneck, W2,∞ and W2,2 distances by 31%, 25% and 27% relative to the pretrained CNN, at a cost of about half of standard fine-tuning's PSNR gain. It also shows that pointwise metrics and topological distances rank CNN and GAN models differently.

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

Describes a general-purpose LLM-based AI tutor for computing courses that accepts questions in Mandarin and replies bilingually, also generating glossaries of technical terms. It uses a specialized multi-source RAG pipeline with a novel question extraction mechanism to draw on diverse course materials. Evaluated on 91 Piazza posts: 83.52% answered correctly and helpfully, 76.92% of questions got high-quality translations, and 64.47% got glossaries.

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

Proposes a modified Physics-informed DeepONet (PI-DeepONet) architecture that removes redundancy in how the Branch network processes input functions across collocation points. It is mathematically equivalent to the original but uses smaller input tensors and fewer Branch evaluations. The authors derive training-time scaling laws, then test on 1D, 2D and 3D linear advection problems. Accuracy matches the original, the maximum speed-up is 3.75× in 2D, and a 3D model trains on an NVIDIA A100 only with the new formulation, because the original runs out of memory.

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

DropKV is a KV-cache eviction framework for long-context LLMs. It decouples the joint eviction decision into independent per-token scores based on residual-output perturbation, which avoids the NP-hard combinatorial problem. It has a provable constant-factor approximation guarantee under a cone condition, which the authors verify empirically on three long-context LLMs. The fused Triton kernels never materialize the full attention matrix. They give a 19.8x scoring-kernel speedup and up to 9.9% end-to-end prefill speedup. On RULER and LongBench it beats state-of-the-art eviction baselines at equal cache budgets.

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

Paper arguing that agent human-approval prompts count as a second factor only if agent compromise cannot forge them and they name exactly what is approved. It defines twelve attacks on the approval path (decoy recipients, amount cancellation, card padding, label spoofing, enrollment squatting, threshold splitting) and tests them against AP2, Microsoft's action-bound approval design and MCP elicitation. An audit of ten agent runtimes found no third-party verifiable approval record and post-approval argument changes in four of eight. The reference defense ASU (Agent Step-Up) renders the card from a canonical effect descriptor, has a phone passkey sign its digest, and executes only the stored call. It is proven in Tamarin (53 lemmas) and evaluated on 27,108 scripted trials and 609 AgentDojo pairs. The trusted card cut forged-narration attack success from 0.54 to 0.03 and raised LLM-approver catch rate from 0.30 to 0.98.

arxiv.orgpaperAdded 17 Oct 2026, published 15 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

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

Proposes RTL-level architectural changes to general-purpose processors that remove the root causes of micro-architectural leakage, so masked cryptographic software runs securely under the Robust but Relaxed (RR) d-probing model without security-aware compilers or hardware-specific software workarounds. Each countermeasure is formally proven secure and integrated into the Ibex RISC-V core, which is evaluated by tool-assisted leakage assessment of the full netlist and by physical measurements on an FPGA.

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

DPF-SGD is a federated SGD algorithm that stays robust to Byzantine agents while satisfying ϵ-differential privacy. It combines private step sizes with a DP noise-decay schedule and adds a privacy-preserving comparative elimination (PPCE) filter that suppresses Byzantine updates during aggregation. The authors prove almost-sure convergence to the global optimum despite persistent noise and adversaries. On synthetic and MNIST data, final optimization error is one to two orders of magnitude lower than DP baselines.

eprint.iacr.orgpaperPublished and added 14 Oct 2026🧠 AI

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

Systematization of knowledge on private Transformer inference (PTI) that builds an empirical graph of 62 systems, with system attributes as node features and evaluation compatibility as edges. Node-level attribution finds that cryptographic progress drives most speedups but always co-occurs with confounders such as model fidelity, hardware and cost accounting. Edge-level auditing finds only 1 of 1891 candidate system pairs directly comparable. Graph-level analysis shows the scale frontier plateaued at 13B parameters and projects the cost of deploying current PTI techniques on production-scale Transformers.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

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

MiniDog is a framework for training a text-to-image flow-matching diffusion transformer from scratch on four RTX 3090 GPUs, with 55 minutes of pretraining and 15 minutes of fine-tuning. It pretrains on 26k ImageNet dog images that were captioned, then fine-tunes on 2k FLUX.1-schnell generated images. Image and text features are precomputed and cached. The model is a 22M-parameter transformer with the E2E-INVAE semantic latent space, a small Qwen text encoder and in-context conditioning, and training is accelerated with iREPA and an EUPE perception encoder. The repo evaluates with FID, HPSv2, PickScore and VAE reconstruction metrics. It lowers the barrier to hands-on diffusion training research.

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

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

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

EmoGenie is a benchmark for emotions expressed and evoked by text-to-image outputs. It has 6,400 images from eight T2I models using 800 emotion-targeted prompts. It includes EmoGenie-QA (90,000 closed-set and 1,057 open-ended questions) and EmoGenie-Assess (13,175 mean opinion scores for valence, arousal, dominance and aesthetic quality). The authors also propose EmoProbe, which pairs emotion QA with response-aligned, dimension-specific regression heads. They report that it beats baselines on emotion QA and correlates better with human ratings. The work shows how reliably MLLMs interpret affect in generated images.

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

Proposes a vision-language-model framework that incrementally reconstructs heading hierarchies across tens or hundreds of pages using a level-wise multimodal memory. The memory keeps a compact set of earlier pages chosen by anchors (here, the most recent node at each heading level), so the VLM sees the visual, textual and layout patterns of headings at every level. The authors also introduce CLDoc-HHRBench (250 documents, 11,212 pages, expert-verified annotations) and report results on it and three existing benchmarks. It matters for RAG pipelines that need coherent document-level structure, which page-by-page OCR/VLM parsing doesn't give.

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

Systematic study of which framings of a user's claim trigger sycophancy in factual question answering, where every answer is objectively verifiable. Across 14 models and 42K questions, correct and incorrect user opinions are expressed through 21 framing types in 8 dimensions, and multiple opinion-driven behaviors are measured. All models are susceptible regardless of size; confidence of statement, attribution to whom, and framing as shared between user and model matter most, while expertise, authority and social pressure matter little. Larger models resist incorrect opinions better but are no better at recovering from their own errors, so the authors argue sycophancy evals must probe framing and measure more than a single flip rate.

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

Introduces a cube-attack framework with an elimination strategy. It finds key conditions and cubes whose conditional superpolies are cheap to compute, then solves the resulting equation systems to recover key bits. The supporting techniques are a nested coefficient solver, conditional monomial prediction, an automated cube search combining numeric mapping with monomial prediction, and two testing methods for large equation systems. Against round-reduced Trivium it gives a practical key recovery at 840 rounds (complexity below 2^55, 77.8% success) and at 845 rounds (below 2^56, 98.1% success on 59.1% of keys). It also gives a theoretical weak-key attack at 855 rounds for 2^74 keys. These are the best known results, extending prior practical attacks by 13 rounds and theoretical attacks by 4.

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

Introduces Sauron, an evidence-first agentic verifier that checks rubric criteria for long-horizon agent tasks by investigating both environment state and execution trace, and trusts a claim (including an agent's self-reported success) only after validating it against its source. Bounded queries and joint investigation of related criteria cut context use, and each verdict carries an auditable evidence receipt. On 2,586 human-labeled criteria from 100 personal-assistant episodes it has the best F1 of five verifiers across eight backbones (0.96 vs 0.92 for Gandalf with GPT-5.6 Sol, at 23% lower cost). It detects 97% of injected failures across 13 fault families, including reward-hacking simulations, with 2% false flags on clean controls, and leads on the TRAIL agent-error benchmark. It matters because unreliable rubric verdicts used as RL rewards can reinforce bad behavior.

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

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

The paper introduces verifiable reconstructible straightline codes (VRSCs) as an abstraction for compiling zkSNARKs into Universally Composable (UC) secure protocols. It shows that the witness-succinct UC compiler of Ganesh et al. (EUROCRYPT 2023) is missing a security requirement: permuting previously revealed evaluation openings yields a fresh accepting proof, which invalidates the claimed simulation argument. The fix uses verifier-enforced disjoint evaluation domains and a pairing-based Fischlin-style instantiation with structured Reed-Solomon codes and jointly computed KZG openings. This brings certificate generation to O(n log n) with constant extra proof size and verification work.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 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

StableJudge is a defense framework against score-hijacking prompt injection in LLM-as-a-Judge pipelines (e.g. paper review, recommendation). A compact 0.13B "Sentry" model localizes injected spans and sanitizes them before the judge scores the content, trained on adversarially evolving attacks, and deterministic fine-grained scores are derived from the judge's output distribution. It improves score recovery under attack on held-out scientific abstracts and out-of-distribution attack variants while preserving general performance, decoupling safety from judge model scale. Code and data are promised but not yet released.

arxiv.orgpaperAdded 15 Oct 2026, published 12 Oct 2026🛡️ Security

Paper presenting the first on-chip, TDC-sensor-based power side-channel attack on a co-tenant FPGA to reconstruct full-scale color ImageNet inputs (224x224) from a victim's first CNN convolution layer, using ML-based upscaling and recoloring. It separates genuinely leaked information from ML-generated post-processing. It also evaluates a pixel-substitution defense based on Hamming Weight, which is largely defeated by an attacker retrained on the transformed data. The authors conclude that robust defenses need randomized or input-dependent transformations.

arxiv.orgpaperAdded 16 Oct 2026, published 12 Oct 2026🛡️ Security

Proposes Staggered Instruction-Fetch Duplication (SIFD), a processor-level fault-injection countermeasure that fetches each instruction twice in consecutive cycles while fetching two different instructions per cycle, so inconsistent fetches are detected without full processor duplication, compiler support or crypto hardware. Implemented on a custom RV32I core on an Artix-7 FPGA, it costs 15.0% more slices and 16.3% more execution time. Attack success falls from 0.86% to below 0.01% under clock fault injection and from 0.32% to 0.13% under EMFI, with 73.18% of CFI and 11.44% of EMFI attempts detected.

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

Introduces Confidential Error-correcting Codes (CECs), a primitive that decodes reliably from perturbed codewords while keeping the message confidential, defined by confidentiality, robustness and soundness. The Randomness-Security Separation framework encodes a nonce and masks the payload with a PRF; the Conc-CEC instantiation uses concatenated Reed-Muller and Reed-Solomon codes and claims stronger concrete security than PRCs (CRYPTO'24). The application, CECMark, watermarks AI-generated images and reportedly beats Gaussian Shading, PRCMark and T2SMark across 56 perturbations with a false positive rate below 1e-5 and no side effects.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 10 Oct 2026🧠 AI

Introduces Confidential Error-correcting Codes (CECs), a cryptographic primitive for decoding perturbed codewords while keeping the message confidential, defined by confidentiality, robustness and soundness. The Randomness-Security Separation framework encodes a nonce and masks the payload with a PRF. The Conc-CEC instantiation uses concatenated Reed-Muller and Reed-Solomon codes, and the authors claim stronger concrete security than PRCs (CRYPTO'24). The CECMark application watermarks AI-generated images, and the authors report it beats Gaussian Shading, PRCMark and T2SMark across 56 perturbations with a false positive rate below 10^-5.

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

Presents an EM side-channel attack on LLaMA-3.2-1B-Instruct running on a Raspberry Pi 5, showing passive physical measurements leak generated tokens and user prompts. Three attacks: a profiling attack (97.2% accuracy over 50 medical/privacy keywords), a hidden-state reconstruction attack predicting tokens from the 128k vocabulary (94.3% on profiled classes; 67% of unseen words ranked in top 100), and a hidden-state inversion attack reconstructing prompts semantically. Shows that local edge inference does not guarantee confidentiality of tokens and inputs.

arxiv.orgpaperAdded 16 Oct 2026, published 9 Oct 2026🛡️ Security

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

HOA (Humanize Olympiads Agent) is an open, flow-level framework that coordinates several agent sessions through three feedback channels: an independent reviewer that critiques each candidate, a structured wiki supplying task-specific knowledge the base model may lack, and a verifier that checks generated Lean proofs against trusted challenge statements. With GPT-5.6 Sol it reports perfect scores on IMO, IPhO and IOI 2026, near-full marks on IChO 2026, 672/672 on PutnamBench, and first place on the Lean-Eval leaderboard (251/304). With the open model Kimi-K3 it matches or exceeds a GPT-5.6 Sol + /Goal baseline on several contests, which suggests systematic feedback can narrow base-model capability gaps.

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

BLiMPPI is a multilingual evaluation suite of over 12,000 negative polarity item (NPI) minimal pairs across 33 languages. A pipeline finds NPIs in Universal Dependencies treebanks, labels their licensing environments, and perturbs them into grammatical/ungrammatical pairs. Across 12 multilingual and 2 monolingual models plus n-gram baselines, multilingual models do worst on lower-resource languages and on pairs that n-grams cannot solve. The paper also analyses NPI typology, language family, licensing variability and NPI-licenser distance.

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

First systematic empirical study of Cross-chain Verification Vulnerabilities (CVVs) in bridge smart contracts, such as flawed validation of state proofs, message sender identities and signatures. From 588 audit reports across 36 bridge projects it derives 11 CVV patterns in 5 categories and builds a dataset of 830 instances across 786 contract files, mapped to reports, source versions and pattern labels. Existing tools cover few CVV patterns, and only two of four tested LLMs beat a 29.3% Macro-F1 constant baseline (best 51.2%), which motivates detectors built for cross-chain verification semantics.

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

SmartSalesBench is a multi-turn conversational benchmark that tests LLM sales agents against psychologically grounded, resistant buyer personas over conversations of up to twenty turns. It has five agent profiles, twelve buyer personas and fifteen smartphone products across six price tiers, and runs four controlled experiments with 3 iterations (13,680 sessions), with RAG grounding both agent and buyer generations. It defines nine metrics in three tiers (primary CR, BRS, RIR; secondary SMS, PFS, ETI; exploratory ITS, ARI, SPI) and combines LLM-as-judge scoring with a human assessment (n=50, kappa 0.71, Pearson r 0.81 with BRS). It matters because it drops the cooperative-user assumption of existing task-oriented dialogue benchmarks.

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

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

OuroWorld is a mask-free framework that turns any static 3D Gaussian Splatting scene into a seamlessly looping 3D cinemagraph. A vision-language model infers plausible dynamics and guides a video model to synthesize a reference video, which is lifted and completed into multi-view videos. Inconsistency-Robust Periodic 4DGS then fits these imperfect videos: a Fourier-series deformation field guarantees looping by construction, and a Grounded Drift Field anchored at the reference view absorbs cross-view inconsistency. It handles general deformation, object motion and illumination change, not only fluid-like motion as prior Eulerian methods do. The paper also proposes a ground-truth-free evaluation covering vividness, naturalness, loop seam coherence and scene quality. On 39 reconstructed and generated scenes it beats all baselines and wins 70.8%-99.0% of user-study comparisons.

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

The paper reimplements Q-FLAIR, which grows a quantum feature-map circuit gate by gate until the training loss stops improving. It runs the method on 784-pixel MNIST 3-vs-5 classification at five training-set sizes (N = 2000 to 10000), then fine-tunes each circuit to count Caro et al.'s active gates K. It finds no predictable link between training-set size and the circuit size Q-FLAIR converges to, and seed variance is nearly as large as any trend. The Caro et al. generalization bound holds in 14 of 15 runs, but the gap correlates only weakly with the bound (r = 0.12), so K explains little of the variation. This is a useful negative result for anyone hoping to jointly optimize circuit depth and data size.

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

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

The paper proposes evaluating LLM agents on epistemic humility, meaning their willingness to recognize, act on and communicate uncertainty. It operationalizes this as three trajectory-level dimensions (Identify, Solve, Escalate, or ISE). It tests controlled and naturally occurring knowledge conflicts, each with matched no-conflict controls, on four agents. Higher accuracy does not imply more humility: some high-accuracy agents detect conflicts but do not communicate uncertainty in wrong final answers, and agents often detect conflicts early but lose track of them later. Model-level interventions can improve humility but often cost accuracy, which suggests humility depends on the interaction of backbone model, agent harness and environment.

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

Connected Self Forcing extends Self Forcing, which trains autoregressive video generators on their own rollouts with KV caching but detaches historical caches and so cuts backward gradients between chunks. It reconnects gradient paths across chunks, passing gradients through generated latents into the computations that produced them, so later predictions can shape how earlier context is generated. Shortcut gradient replay recovers cross-chunk gradients without keeping the full rollout computation graph. Combined with distribution matching distillation, it improves long-horizon visual quality and temporal consistency with no change to inference.

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

ePrint paper giving four constructions of optimal threshold implementations (t+1 shares, no fresh randomness) for bijective S-boxes, a masking countermeasure against differential power analysis and glitch leakage in hardware. It shows that Q30^5 admits optimal TIs, gives a matrix criterion for quadratic permutations (yielding new 6-bit classes), and gives a weighted interpolation construction for power permutations that disproves the Piccione et al. 2023 conjecture. It also gives a derivative-uniform framework that proves every 4-bit cubic permutation admits optimal TIs. A companion repo has TIs for 5-bit quadratic permutations, 4-bit cubic permutations, the PRESENT S-box and GF(16) inversion.

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

Proposes QCOC (Query-Calibrated Operator Compression) for tabular in-context learning. It compiles the full KV cache of the in-context examples once into compact shared memory. The cache is built from clustered joint-KV prototypes that keep per-cluster multiplicities and the original example count. Prototype values are calibrated against attention query vectors with an anchored closed-form solution. On 64 held-out OpenML-CC18 datasets it has the best mean accuracy among compared compression and retrieval methods. On seven long tables it averages 0.23 points below full context. Compressing 8,192 examples to 512 slots gives 10.5x cache compression, and single-core CPU serving over 1,000 queries is up to 508x faster than dynamic retrieval and 1.98x faster than full context.

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

The paper builds the first dated copy network of agent skills (SKILL.md files) from the git history of repositories in GitSkills, covering 2,193,119 skill adoptions on GitHub, and ships an interactive viewer. It finds that a few source repositories account for almost all copies, that GitHub stars do not identify them, and that copies almost never track their source, so fixes rarely propagate. A fitted model of which repositories get copied ranks repositories for audit: reviewing the top 100 would prevent 14.9% of later adoptions of high-risk skills, against 0.5% for the 100 most-starred. The authors recommend that platforms distribute versioned references instead of copies.

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

Builds the first dated copy network of agent skills (SKILL.md files) from git history in the GitSkills dataset, covering 2,193,119 skill adoptions on GitHub, plus an interactive viewer. Finds a few repositories are the source of nearly all copies, stars do not identify them, and copies almost never track their source, so upstream fixes rarely propagate. A model of which repositories get copied ranks repos for audit: reviewing the top 100 prevents 14.9% of later adoptions of high-risk skills versus 0.5% for the 100 most starred. The authors recommend platforms distribute versioned references rather than copies.

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

Mazor, Morgan and Pass prove that LLMs, modeled as next-token predictors, are equivalent to monotone (order-preserving) compression algorithms: either can be built from the other with the same error up to an additive gap of 2. They show that monotonicity is needed for this equivalence if and only if infinitely-often one-way functions exist. As a corollary, next-bit pseudoentropy of a distribution is equivalent to monotone incompressibility. This gives a theoretical basis for empirical findings that compression ability tracks LLM benchmark performance.

eprint.iacr.orgpaperAdded 11 Oct 2026, published 8 Oct 2026🧠 AI

Introduces DITTO, a stack-based, context-aware scanner for Pickle-based pre-trained models that tracks Pickle VM state transitions and applies semantic analysis to infer model intent. Authors found 9.3% of 10,000+ popular Hugging Face repos still use Pickle, and release PickleBench (959 benign, 92 malicious models, including extension registry attacks missed by existing tools). Reports 100% coverage, 0% false negatives, 0.7% false positives, F1 0.966, outperforming current scanners.

arxiv.orgpaperAdded 9 Oct 2026, published 7 Oct 2026🛡️ Security

RECAST (Routing Evidence through Computation, Access, and Synthesized Tools) reframes retrieval-augmented generation as a sequential decision process: a lightweight RouterLM iteratively selects or formulates primitive/custom operations, a frozen CompilerLM turns custom operations into executable code, and a frozen AnswerLM produces the final answer once evidence is judged sufficient. RouterLM is trained with supervised fine-tuning followed by GRPO, and the system reaches a 75.6% mean success rate across six heterogeneous benchmark families, beating the strongest large-model baseline by 15.9% and a training-free Gemini 3.5 Flash router by 5.0%, with strong zero-shot generalization on three held-out benchmarks. It targets tasks where evidence must be derived through filtering, aggregation, or computation across multiple sources rather than retrieved verbatim.

arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🧠 AI

RunningTab is a framework that gives LLM agents performing 'direct workspace interaction' (searching and reading files from a terminal without indexing to produce a deliverable) an environment-side 'tab': a per-task record, maintained outside the model's context, of requirements the agent has declared, excerpts read with provenance, and listed-but-unopened files flagged as candidates. The agent can check each requirement against matching excerpts or candidates, mark it resolved or deferred with a reason, and is forced through a finish check if requirements remain open when it tries to complete the task. The authors show it consistently beats plain direct workspace interaction and model-internal record-keeping baselines across three benchmarks and three LLMs.

arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🧠 AI

SLDR is a post-fine-tuning defense against malicious fine-tuning-as-a-service attacks, where an attacker fine-tunes an aligned LLM on a legitimate-looking task to covertly erode its refusal behavior. The method trains a LoRA "recovery" adapter only on the layers found to have maximal and minimal signed safety sensitivity, and uses representation-based dynamic routing at inference time to activate that adapter only for malicious queries, leaving benign task performance untouched. On Llama3.1 fine-tuned for SST2, it cuts the average harmful-output score from 11.54 to 0.08 while keeping downstream accuracy, and stays near-zero even when up to 90% of the fine-tuning data is poisoned.

arxiv.orgpaperAdded 9 Oct 2026, published 7 Oct 2026🛡️ Security

SLDR is a post-fine-tuning defense that protects aligned LLMs from malicious fine-tuning-as-a-service attacks, which can quietly erode refusal behavior while keeping task performance intact. It trains a LoRA recovery adapter only on the layers with the most extreme (signed) safety sensitivity scores, then uses representation-based dynamic routing to activate that adapter solely for malicious queries at inference time. Evaluated across four model architectures, five downstream tasks, and four harmful-content benchmarks, it drives the average harmful score on Llama3.1/SST2 from 11.54 down to 0.08 while preserving downstream accuracy, and stays near zero even at poisoning ratios up to 0.9.

arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🧠 AI

This arXiv paper examines why current LLM-based automated vulnerability-patching benchmarks overstate model performance, identifying pitfalls at the agent level (prompting/tooling inflating success without improving patch quality), framework level (permission errors, infra bugs, timeout handling silently skewing scores), and dataset level (single PoC tests failing to verify root-cause fixes). The authors curate 112 historical bugs across 84 open-source C/C++, Go, and Rust projects, each annotated with PoC tests, regression tests, and developer-intent tests, and show that while PoC pass rates look high, developer-test pass rates stay low and barely improve with newer models. They provide guidelines for more rigorous, reproducible evaluation of LLM patching systems.

arxiv.orgpaperAdded 9 Oct 2026, published 7 Oct 2026🛡️ Security

This IACR ePrint note revisits a 2025 Crypto paper by Belohorec et al. that proved the PST multivariate polynomial commitment scheme is black-box extractable under two new assumptions, ARSDH(n) and GARSDH(n). The authors show both are in fact equivalent to the original ARSDH assumption from Lipmaa et al. (Eurocrypt 2024), and separately identify a gap in the proof that canonical PST is extractable under GARSDH(n). This matters for implementers and auditors relying on PST/KZG-style polynomial commitments, as it clarifies the actual cryptographic strength underpinning extractability claims used in zk-SNARK and other succinct-proof constructions.

eprint.iacr.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🛡️ Security

Krenn, Lesaignoux, Mir and Spini (AIT Austrian Institute of Technology) propose an "issuer inspection" mechanism for issuer-hiding attribute-based credentials (ABCs): an inspection authority can release time-bound keys that let verifiers locally flag presentations tied to a specific compromised issuer without unmasking other issuers or periods. They formalize the syntax/security model, give a generic construction, and instantiate it concretely using Groth's randomizable structure-preserving signatures, Schnorr-style proofs, and an adapted Gentry anonymous identity-based encryption scheme, showing the overhead stays practical. This addresses the accountability gap introduced when issuer-hiding ABCs conceal which authority issued a credential during verification.

eprint.iacr.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🛡️ Security

TR-PTQ is a post-training quantization method for transformers that identifies learned LayerNorm scale parameters and GELU's compounded approximations (not SoftMax) as the main sources of accuracy loss when quantizing, contrary to common assumptions about numerical precision. It introduces a unified integer-only formulation using shared Taylor Region exponential/logarithm primitives so division and square roots can run entirely in the log-domain with standard integer arithmetic, paired with a calibration-free, outlier-aware LayerNorm optimization, eliminating the need for floating-point hardware for nonlinearities while keeping accuracy degradation under 1.5% across vision and language benchmarks.

arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🧠 AI

EntroPrefill proposes a Renyi-entropy-guided mechanism for pruning context mid-prefill in retrieval-augmented generation, using sink-isolated regularized head pooling compatible with grouped-query attention to decide which tokens can be safely discarded. It derives a mixture-to-head deletion envelope bounding feasible token removal, a finite-sample observer guarantee valid under adaptive layer selection, and a conditional transformer perturbation bound with explicit Lipschitz constants and a first-token decision-margin corollary, plus a systems-level break-even analysis of KV-transfer payload versus pruning savings. The paper is explicitly theoretical, defining assumptions and formal limits without reporting empirical speedup or accuracy-preservation results.

arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🧠 AI

ASPIRE is an automated red-teaming engine for LLM agents that discovers indirect prompt-injection vulnerabilities beyond pre-specified attack scenarios. It maintains an evolving Agent Security Behavior Graph and uses complementary Explore and Exploit experts to find, verify, and generalize consequence-centric attacks, with trajectory evidence and cross-run memory improving coverage over time. Benchmarked against existing automated red-teaming approaches, it shows broader coverage of consequences, injection methods, environments, and behavior paths while keeping attack success high.

arxiv.orgpaperAdded 8 Oct 2026, published 6 Oct 2026🛡️ Security

PEARS is a hybrid reinforcement-learning framework for sample-efficient online adaptation of pretrained tactile-manipulation policies to out-of-distribution conditions. A physics-guided force reasoning (PFR) module uses a vision-language model to diagnose failures from visual and tactile history and update contact-force bounds enforced by a hybrid force-position controller, while a separate tactile-conditioned diffusion-steering component perturbs the latent noise of a frozen flow-matching policy to correct free-space motion and contact timing without retraining the base model. It improves success rates by 12.4-37.4 points over strong baselines in simulation, cuts required interaction episodes by up to 53.2%, and reaches 95%/90% success on real-world whiteboard-erasing and pipette-liquid-aspiration tasks.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

ALIVE is a framework for first-frame-guided video editing that inserts objects so they coherently participate in interactions (e.g., being picked up or manipulated) rather than appearing static. The authors curate a 35,800-pair editing dataset combining 3D-rendered, model-generated and real-world videos plus pairs from ROSE, and train a vision-language model to predict interaction guidance from the edited first frame and an instruction naming the object. They introduce the ALIVE-interaction benchmark to measure interaction fidelity, source preservation and visual coherence via a unified VLM-based protocol, reporting a 43.9% Overall improvement on this benchmark and 4.4% on a general video object insertion benchmark over the strongest baseline, with VLM-predicted guidance adding a further 0.95-point gain.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

This paper introduces BOTTLED, a benchmark testing whether LLM agents can autonomously convert their general task-solving ability into cheaper, scalable artifacts (e.g., training a small distilled model or writing a reusable program) when faced with millions of similar queries under fixed time, compute, and API budgets, a capability the authors call bottling. Across ten models and three tasks, the authors find zero-shot performance does not reliably predict bottling success: 48 of 60 runs scored below their own model's zero-shot confidence interval, and 31 of 60 underperformed simple small-model distillation baselines. Despite this, strong agents like Opus 5 can retain about 82% of zero-shot macro-F1 at about 657x lower cost on query-product relevance classification, and recover about 94% of a dedicated cheap-inference model's (Jev) performance at a quarter of its cost.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

This paper studies robust bandits (formerly "imprecise bandits," Kosoy 2025; Appel and Kosoy 2025), a framework for unrealizable learning in the bandit setting where the true environment may lie outside the learner's hypothesis class. While prior work showed a Θ(√T) regret learner exists for a broad class without computational guarantees, this paper identifies a special case admitting a polynomial-time learner achieving Õ(√T) regret, and proves several natural generalizations of that case are NP-hard — mapping the boundary of computational tractability. The authors frame this as relevant to AI alignment, following Kosoy's argument that efficient learners for unrealizable problems are a prerequisite for solving alignment.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

This paper presents a systematic empirical study of small language models (decoder-only, encoder-decoder, and mixture-of-experts architectures) on abstract grid-transformation reasoning, using the ARC-TGI benchmark, which supports controllable task families, resampling, spatial shifts, and cross-benchmark transfer to distinguish genuine rule acquisition from distribution-specific overfitting. Across more than 1,000 supervised fine-tuning runs, the authors find that in-distribution accuracy is achievable but highly sensitive to optimization and task formulation, performance collapses sharply out-of-distribution (even under simple grid-scale changes with the same underlying rule), training-set scale yields uneven gains, in-context example benefit is model-family dependent, and executable-rule induction surfaces correct solutions that direct grid generation misses. Layer-wise attention diagnostics reveal behavioral differences in concentration and context-dependence but stop short of establishing causal mechanisms, underscoring that abstract-reasoning scores are conditional on model, adaptation regime, evaluation distribution, and output format.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

RAG-PIBench is a benchmark of 4,876 contextual examples for evaluating prompt-injection detectors embedded in retrieved content within Retrieval-Augmented Generation (RAG) systems. The authors use a leakage-aware construction pipeline with frozen train/validation/protected-test splits and compare keyword-based, semantic-reference, TF-IDF, and transformer-based detectors, finding DistilBERT achieves the best protected-test performance (F1 = 0.896, PR-AUC = 0.968) while TF-IDF with SVM or logistic regression remains competitive. It highlights the risk that naive benchmark construction can leak information between splits, inflating reported detector performance for RAG prompt-injection defenses.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🛡️ Security

This paper proposes a formal framework for "alignment scaling laws," modeling the compute/capability burden needed to hold a fixed safety target for each risk category as a power law B_r(N)=a_rN^alpha_r, where the exponent alpha_r determines whether alignment gets easier, keeps pace, or accumulates "alignment debt" as capability N grows. The authors prove theoretical results (the largest exponent among corrected risks sets the long-run regime, super-exponential headroom requirements above alpha=1, small-model fits underestimate large-scale exponents for power-law mixtures) and apply a preregistered protocol twice: a reanalysis of adversarial-training data on Pythia classifiers (attack-success compute exponent 0.60) and a pilot across Qwen2.5 (0.5B-72B) and Qwen3 (0.6B-14B), finding scaling helps for truthfulness and stated dispositions but sycophancy (exponent ~0.83-0.89) and a planted backdoor behave worse or remain undetermined, with the backdoor surviving blind safety training at most model sizes. The work matters as an early attempt to put the "does alignment get harder with scale" debate on measurable, falsifiable footing rather than anecdote.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

This IACR ePrint paper proposes a security-aware benchmarking framework for deploying memory-heavy PQC (ML-KEM, ML-DSA) on the dual-core ESP32-S3 (Xtensa LX7) running FreeRTOS, aimed at smart-grid substation devices bound by the IEC 61850 GOOSE tripping window (≤3ms). It details an asymmetric task-isolation design pinning crypto to Core 1, a heap-auditing methodology for FreeRTOS's heap_4.c allocator tracking key-state allocations across DRAM, and an analytical latency model of CPU stall overhead caused when side-channel masking forces secret polynomial shares to spill to external Octal-SPI PSRAM during non-sequential Cooley-Tukey NTT strides. The work gives embedded engineers a methodology to evaluate PQC side-channel/latency trade-offs before migrating real-time grid infrastructure.

eprint.iacr.orgpaperAdded 8 Oct 2026, published 6 Oct 2026🛡️ Security

This IACR ePrint paper examines the post-quantum security of Bitcoin bridges that rely on off-chain garbled-circuit evaluation of succinct proofs to validate withdrawals, where input labels encoding the proof must be committed on-chain. The authors show current pairing-based proving systems are vulnerable to quantum attacks, and that swapping to post-quantum proving systems shrinks circuit size but drastically inflates proof size, forcing proofs to span multiple Bitcoin blocks and raising on-chain cost. They outline research directions to improve proof efficiency, circuit representation, and label encoding for quantum-resistant bridge designs.

eprint.iacr.orgpaperAdded 8 Oct 2026, published 6 Oct 2026🛡️ Security

This arXiv methodology paper extends empirical Bayes g-modeling, which estimates a latent prior over regression coefficients from data, to the case where the entire prior distribution depends on covariates. The authors show the unrestricted nonparametric MLE (NPMLE) of this covariate-dependent prior is ill-posed, and resolve it by introducing an "infinite Mixture-of-Expertizing" class of conditional priors, proving that the resulting NPMLE is exactly a softmax-gated Mixture of Experts whose number of experts is determined by the data rather than fixed in advance. They give two exemplar-based estimation algorithms with automatic expert selection, a post-hoc aggregation step for interpretability, and theoretical results (Lipschitz continuity of conditional priors, universal approximation of continuous gating functions by aggregated softmax gates), validated on synthetic and real datasets.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

An IACR ePrint paper presenting K-LMS, an experimental variant of the NIST-standardized LMS hash-based post-quantum signature scheme in which the underlying hash is swapped for Korean primitives: the LSH-256 hash function (reference and vectorized) and a Tandem-DM double-block-length construction built from the CHAM, LEA, and ARIA block ciphers. The authors implement a non-invasive backend-swapping bridge over the reference LMS code and benchmark keygen/sign/verify latency, key/signature sizes, memory, and parameter sweeps, finding that hash substitution doesn't change key/signature sizes but that two measurement pitfalls (hash dispatch overhead, and software vs. hardware-accelerated SHA-256 baselines) can shift performance comparisons by an order of magnitude or 6x respectively. They report LSH-256 K-LMS signing on par with SHA-256 LMS in software, and 3.3-3.9x faster than a prior K-XMSS implementation because LMS needs fewer hash calls per Winternitz step than XMSS.

eprint.iacr.orgpaperAdded 8 Oct 2026, published 6 Oct 2026🛡️ Security