323 results for “ps”

Ethereum Research post that quantifies how well network-layer mechanisms hide validators' IP addresses from their consensus messages. Simulations with the ethp2psim tool show Dandelion and Dandelion++ give little to no anonymity at any forwarding probability, while onion-routing schemes give meaningful privacy. Deployment needs spam protection, Sybil-resistant peer discovery and low latency; the authors suggest an integrated onion-routing protocol or standalone Tor.

ethresear.chpaperPublished and added 2 Nov 2026🛡️ Security

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

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

The paper proposes a 3D-integration defense against side-channel attacks on post-quantum cryptography implementations. It uses decoy keys and physical shielding through chip stacking, and moves Number Theoretic Transform (NTT) computation to dedicated chips, complementing masking and shuffling. The authors validate it against a generic attack on intermediate values inside an NTT butterfly. It covers ML-KEM, ML-DSA, FN-DSA and SLH-DSA.

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

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

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

CompSkill is a skill retrieval method for LLM agents that indexes skill documents with learned 512-d keys and compiles matched same-task execution comparisons into skill offsets and capped key residuals (KeyOpt). Experience changes which document the executor receives, not its text. Compact keys lift SkillRet NDCG@10 from 64.0 to 70.9, and it completes 256 of 332 ToolQA tasks with GPT-5.6 Luna.

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

ShiftRAG is a decoupled retrieval-augmented generation framework that uses one LLM backbone but separates discriminative retrieval learning from autoregressive generation. It projects retrieval alignments into continuous soft tokens so the generator receives retriever-side signals, and adds a soft orthogonality objective against rank collapse in decoder-only models. The abstract reports gains in retrieval and question answering over state-of-the-art baselines, without numbers.

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

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

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

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

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

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

SPARK is a corrective multi-episode framework for embodied retrieval-augmented generation. After the first episode, a semantic critic flags regions of the knowledge base, a targeted planner generates revisits, and a selective merge adds, removes or replaces entries. On four AirSim outdoor maps with 158 EQA questions, one corrective episode improves accuracy on every map while storing 13-41% fewer objects than append-only updating.

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

ProLand (Product-space Landing) is a retraction-free optimizer for LoRA fine-tuning that keeps the basis factor near the Stiefel manifold. It coordinates factor updates with Muon-style spectral directions, designing updates in the space of the weight increment so objective optimization and orthogonality restoration stay separate. It reports the highest average score among compared methods across 11 tasks on DeBERTa-v3-base, Qwen2.5-3B, Llama-3.2-3B and Moonlight-16B-A3B at several ranks.

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

TextSeal2 is a distortion-free watermark for LLM output that keeps its signal under speculative decoding. It merges two or more keys into one virtual key coupled to fresh noise through AutoMark's Gaussian copula, and verifies drafts with an extra exponential race. On three LLMs it detects 8 to 22 points more 100-token answers than TextSeal at a 10^-3 false-positive rate.

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

Multi-View MoE is a sparse mixture-of-experts design that uses several independently initialized routers to select experts, then reuses their relative scores to mix learned latent views into each selected expert's input. Each expert still runs one forward pass, so active-expert count, FLOPs and all-to-all volume are unchanged. On a 24-layer 10B-A1B model trained on 708B tokens, it gains 1.18 points on a nine-task mean; Multi-Router alone gains 0.6.

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

Rewindable interactive proofs (RIPs) are a new model of relativizing interactive proofs for scalable oversight, in which a weak verifier can rewind an AI prover to an earlier saved state. The paper builds an RIP for computations robust to changes in a small fraction of oracle answers. The honest prover runs in linear time, the verifier runs in nearly linear time in the input length after one-time preprocessing, and no cryptography is used. A preliminary MNIST experiment shows an RIP can be faster than a deployed proof system.

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

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

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

Understanding and Mitigating Role Spoofing in LLMs is an arXiv paper (October 2026) on role spoofing, a prompt injection where malicious tool output impersonates the user. It shows models track "who said what" using both content cues (semantics, style) and structure cues (control tokens, formatting). Attacks combining fake structure and content succeed often, adversarial training stops only content-based attacks, and linear probes trained to predict conversation roles detect spoofing across settings.

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

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

Nadim Kobeissi's ePrint paper 2026/2683 argues that ProVerif and Tamarin should be called symbolic protocol analyzers, not provers. It audits their published soundness arguments, finding false statements, missing proofs and gaps between paper and implementation, and reproduces reported Tamarin bugs that accept false claims. It also covers model gaps in parsers and state machines, weak secrecy queries and unjustified primitive equations. The page is an abstract only.

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

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

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

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

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

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

Beyond the Event Horizon of Keccak extends a 2011 observation that a path in the Keccak sponge from a zero-capacity state back to one yields infinite second-preimage families. Three theorems cover loops from any capacity value x, k-block loops and cycles, and Joux-style multicollisions with search cost independent of k. Verified examples run on Keccak-f[200] and Keccak-f[1600] with c=32; it also proposes bridge-based protocols.

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

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

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

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 how much a model's final answer depends on its own written chain of thought, using a synthetic box-swap task with exact ground truth. It edits one step in the model's chain to a wrong but valid state and checks whether the answer follows. Across 25 open-weight models, reliance rises with size and saturates (Qwen3 from 16-18% at 0.6B to 97-100% at 14B). Reliance tracks capability (r = 0.99) and is unchanged by post-training.

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

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

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

Soft Multi-Token Prediction (Soft-MTP) is a one-line change to multi-token prediction (MTP) pre-training. The authors show the MTP cross-entropy gradient splits exactly into a KL self-distillation term and a noisy residual, and a straight-through "KL gradient-recovery trick" keeps only the KL term. From 167M to 1.2B parameters it gives lower NTP perplexity, faster speculative decoding and higher zero-shot accuracy than standard MTP, with no added parameters or overhead.

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

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

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

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

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

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

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

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

Metacognitive Decoding is a lightweight plug-in controller for diffusion language model backbones. It learns when to finalize tokens and how far generation should extend, and is trained on agent trajectories to balance correctness against generation cost. Across five benchmarks it cuts inference FLOPs by 20.6% on average versus the most efficient baseline, with competitive task accuracy. The abstract names no specific benchmarks or backbones.

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

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

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

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

CRISP is a combination-aware framework that co-designs pruning and runtime scheduling for Mixture-of-Experts inference on memory-constrained edge platforms. It reorders neurons by importance so one stored expert supports several execution widths, gives hot, warm and cold expert combinations different widths, and overlaps expert loading with computation. On Mixtral-8x7B and DeepSeek-MoE-16B it raises throughput up to 2.1x at the same memory budget, with accuracy loss limited to 3.6%.

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

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

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

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

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

Radical isogenies on every model is an IACR ePrint paper by Thomas Decru (October 2026). It gives radical isogeny steps of every odd degree N directly on short Weierstrass, Montgomery, twisted Edwards, Huff, Jacobi quartic, twisted Hessian, Legendre and Jacobi intersection curves, plus Kummer lines. In Tate normal form a step drops from 12.5N to 10N multiplications. Montgomery and twisted Edwards curves cost 10.5N+O(1) per step and 2N to push a point. All algorithms are inversion-free, with exact operation counts.

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

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

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

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

Cheap 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. It describes AI identifying hardware components, extracting firmware, decompiling object code and cloning apps. It maps these practices onto trade secret, copyright, anti-circumvention, computer misuse and contract law, and sketches possible legal responses with trade-offs.

arxiv.orgpaperAdded 3 Nov 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

A Memory-efficient Implementation of Wiedemann XL is an ePrint paper (2026/2641) giving the first efficient on-the-fly implementation of XL with block Wiedemann for the multivariate quadratic problem. It builds on the CCNY SSE implementation, removes column-index tables, and computes column indices with O(n^2) instead of O(n^3) look-ups. It runs in comparable BW1 time with up to five times less memory, and an AVX2 port covers F256, the main field of UOV.

eprint.iacr.orgpaperPublished and added 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

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

A stage-aware visual attribution framework for Vision-Language Navigation (VLN) that explains a robot's action choices. It identifies "instruction-stage insensitivity", where existing explainers give similar outputs across navigation stages. The method builds action-conditioned temporal instruction-visual attributions, aggregates relevant historical evidence by stage, and applies an instruction-guided spatial filter. It improves attribution quality across several VLN policies and benchmarks, helps diagnose failure cases, and was tested on a Unitree G1 robot.

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

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

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

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

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

Ranking Is Not Deciding is an October 2026 arXiv paper showing that query-time RAG poisoning filters score near-perfectly because of how the poison set was built. PoisonedRAG's code prepends the target question to each passage. Removing that prefix takes RAGuard from AUROC 0.953 to 0.758 and its miss rate from 0.191 to 0.763. The authors release a script that profiles a poison set.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🛡️ Security

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

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

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

FORK is a reward for rubric-based reinforcement learning that jointly optimizes recall and a new precision objective. Precision requires each unit of a response to be necessary (rubric-driven necessity credits) and factually correct (a grader). Rubric-coverage rewards otherwise produce verbose, less accurate responses. FORK keeps similar coverage with shorter outputs, raising win rate against the base policy from 10.6% to 68.0% on RubricHub-Medical and from 22.1% to 74.1% on HealthBench.

arxiv.orgpaperAdded 27 Oct 2026, published 24 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

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

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

Learning with Quantization and Worst Case Lattice Problems is an IACR ePrint paper (2026/2613) by Ziwei Hong and Zhongqiu Fang. It proves that search and decision learning with quantization (LWQ) with public dither is as hard as quantum GapSVP and SIVP on arbitrary lattices, using the randomized polar lattice quantizer of Lyu et al. It also shows that a complete reduction for interval uniform error LWE (MAMBA Frost) and hardness at concrete MAMBA parameters are not established.

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

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

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

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

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

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

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

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

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

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

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

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

RIFT is a family of selection-based KV cache compression strategies for speculative-decoding draft models. It targets "decode staleness", where prefill-focused selection loses relevance as output grows, by balancing prefill information with recent generated tokens, refreshing the selection adaptively, and training drafters with sparse attention masks. Qwen3.5-4B drafting for Qwen3.5-122B keeps over 98% of full-cache acceptance length at 32K input, 4K output and an 8K KV budget.

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

TRIAD is a benchmark for omni-modal ambiguity resolution. It has 327 hand-written text-image-audio queries in 86 scene groups across 18 ambiguity categories, and removing any one modality makes the answer non-unique. Across 14 omni-modal systems, the best model trails humans by over 20 points in group accuracy. Without answer options, model group accuracy falls to single digits, and leave-one-out tests show audio cues are often underused.

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

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

huggingface.copaperAdded 22 Oct 2026🧠 AI

IFC-MCQ is a method that automatically generates multiple-choice benchmarks from Industry Foundation Classes (IFC) building-model files, so answers can be graded by exact option-set match instead of human or LLM review. Its taxonomy has 15 categories and 36 subtypes in four groups, with answer keys from the file and distractors from file values or rules. Two agentic setups, tool-based and code-based retrieval, showed category-specific failures and variation by IFC model and interface.

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

Rerandomizable Garbling for Arithmetic Circuits is an IACR ePrint paper (2026/2590) by Acharya, Meyer, Orlandi and Roy. It gives the first rerandomizable garbling scheme for arithmetic circuits, built on techniques from garbling via homomorphic secret sharing. Communication is O(|C| log B), with 17 group elements per multiplication gate and free additions. Security rests on subgroup-indistinguishability assumptions such as DCR and class groups.

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

PQCD-TLS is a post-quantum TLS handshake that binds confirmation codes into chained key derivation, so a faulty signature or KEM verification step causes a key mismatch and handshake failure. The authors build confirmation-code frameworks for ML-DSA and Falcon signatures and for ML-KEM, HQC and DAWN, with unpredictability proofs. Across 48 configurations, handshake latency overhead stays below 1%.

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

This ePrint paper presents an optimized HAETAE implementation, a lattice-based post-quantum signature scheme, for Cortex-M4, Cortex-M3, MSP430 and AVR. It keeps polynomials in 16 bits, shares buffers between objects with disjoint lifetimes, and replays Gaussian samples instead of regenerating them. On an ATmega2560, HAETAE-2 key generation, signing and verification run in 8,070 B of SRAM. Stack use drops up to 34% and signing is 1.18-2.51x faster than Low-Stack HAETAE.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 21 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

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

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

A Spectral Theory of Local-to-Global Leakage is an IACR ePrint paper (2026/2571) by Rei Ueno on additive secret sharing over finite abelian groups. It gives an exact leakage decomposition into local leakage magnitude and spectral alignment across shares. Two channels with identical local mutual information can have exponentially different global leakage. Automorphism-weighted sharing can cancel leakage perfectly with finitely many shares, which ordinary additive sharing cannot do.

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

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

Lafa (Large flow language model) is a blockwise continuous flow language model scaled to 8B parameters, with causal attention on the prefix and bidirectional, asynchronous flow within each block. It is adapted from pretrained autoregressive models and trained at 0.5B, 7B and 8B with under 30B tokens. A self-speculative decoding scheme verifies flow drafts autoregressively in one model, giving 2.54x the throughput of an AR model at comparable quality. Flow maps allow one- and few-step generation.

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

This paper studies 2-bit post-training quantization (PTQ) of six dense and mixture-of-experts models from 3.8B to 32B parameters. It finds the quantization representation matters most: vector codebooks give usable models where scalar PTQ falls to chance. Calibration and recovery data must be chosen jointly. The recipe uses about 2.3 effective bits per weight, keeps 89 to 97% of full-precision QA accuracy, and needs 1.4 to 30x less compute than the strongest QAT baseline.

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

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

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

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

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

Fault-Induced Nonce Repetition in Threshold Lattice Signatures is an ePrint paper (2026/2566, Yulong Meng) showing that faulting t devices in a t-of-n threshold ML-DSA deployment recovers the full signing secret, verified on Mithril with exact recovery in 96/96 scan groups. It gives a reveal-structure criterion across Quorus, TALUS and Trilithium, a threshold law m* = (ℓ-1)N+⌈N/K⌉ for imperfect faults, and two-layer transcript detection (305/305 hits, no false positives).

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

Graphs and cycles in curve-based collision walks is an IACR ePrint paper (2026/2560) on van Oorschot-Wiener style collision attacks in the multi-user setting. It covers elliptic curve discrete logarithms and isogeny pathfinding on supersingular F_p^2 and F_p curves, and proposes new pseudorandom walks. For F_p^2 curves, L keys need about L + log(L) useful collisions with no infiltrator. For F_p curves, about 0.75L keys fall to L collisions with an infiltrator.

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

Into the Unknown is a benchmark that gives an AI agent a science paper and an unpublished research direction, then asks it to write a follow-up paper. Scientists at US Department of Energy national laboratories supplied the directions and the conditions a submission must meet, which form the grading rubric. Nine frontier agents wrote 15 follow-ups each (135 total) across five domains. None met all conditions, and 114 failed an integrity check, mostly for claiming work never done or reporting numbers that contradict recorded evidence.

huggingface.copaperAdded 20 Oct 2026🧠 AI

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 preprint grades evidence for pathology vision-language models and tests four claims with controlled probes on released checkpoints and public data. Stage-redacted report embeddings raise pooled C-index by 0.019 (progression-free interval) and 0.016 (overall survival) across 1,633 TCGA patients. Zero-shot prompting matches a linear probe. SlideChat scores 74.82% with the slide and 74.41% without it. Paraphrasing flips 8-23% of answers.

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

Analysis of DeepDistinguisher, a transformer model that tells Goppa codes from random linear codes. Using saliency maps and attention visualization, the authors find a low-weight-row artifact in the random training instances that the model partly uses. They propose a pairwise-XOR rule on generator matrix rows that correlates strongly with the model's predictions. On random matrices meeting the minimum distance and XOR constraints, the model loses most of its accuracy, so it exploits general combinatorial properties rather than hidden algebraic structure.

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

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

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

This 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

SHADE is an alpha-stable Lévy diffusion forecaster that stops denoising at a nonzero depth, so the leftover stable kernel shrinks the predictive distribution instead of leaving it overconfident. It offers a scale-calibrated halt and a context-amortized halt that needs no noise labels. Median CRPS improves 21.7% on synthetic data with alpha at or below 1.3, with tail calibration gains on cryptocurrency and foreign-exchange series.

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

Privacy with Leakage from Falsifiable Assumptions is an ASIACRYPT 2026 paper by Francesco Berti and Itamar Levi. It replaces simulatability with "secure refreshing", a key-refreshing assumption that evaluation laboratories can test directly, and adds "strongly protected secure refreshing". Using these, it proves privacy of the PSV encryption scheme without assuming a leak-free component.

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