On the Fundamental Limits of Prefill-Based Decoding Length PredictionThis paper derives bounds on predicting an LLM's decode length from prefill-phase hidden states, a primitive for inference scheduling. It models decoding as a stopped stochastic process, giving per-prompt normality and an affine variance law Ο=Ξ±ΞΌ+b. Predictors (MLP, EGTP, FiLM, soft-label, Mixture-of-Experts) converge near the bound, so system error dominates model error. It proposes fixing model, data and sampling in benchmarks and reporting relative-error metrics.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI- PhysCount: Do MLLMs Count Visual Appearances or Physical Entities?
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 2026π§ AI - A Quantitative Evaluation of Constant-Time Analysis Tools
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 2026π‘οΈ Security - M-PFT: Angular Margin for Calibrated Prompt-Tuning of Fine-Tuned Vision-Language Models
M-PFT is an angular-margin method for prompt tuning of vision-language models that fixes base-to-novel dual miscalibration (underconfidence on base classes, overconfidence on novel classes). It adds two losses: minimizing the maximum cosine similarity between tunable class embeddings, and minimizing angular distance to each zero-shot embedding. Tests on 9 prompt-tuning methods and 15 datasets show lower novel-class calibration error at comparable accuracy and negligible overhead.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Cryptanalysis and Enhancement of VTBSAE: Provably Secure Verifier-Based PAKE Protocols for the Industrial Internet of Things
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 2026π‘οΈ Security - Sparse Autoencoders as Implicit Diversification Regularisers for Cross-Sectional Cryptocurrency Returns
This arXiv preprint (October 2026) tests a sparse autoencoder as a factor model for daily cryptocurrency returns, with equities as a secondary test. SAE portfolios show consistently lower volatility than common benchmarks, linked to lower correlations among selected assets. They do not beat a simple contrarian strategy on risk-adjusted return, and factor identities change between training runs.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - PCM: A Proof-Carrying Message Protocol for Verifiable Multi-Agent Communication
Proof-Carrying Messages (PCM) is a protocol where each claim an AI agent sends carries evidence: records from an append-only Merkle log the agent cannot write, with inclusion proofs, signed by an attesting signer. Receivers verify offline in about 1 ms and label each claim proven, unproven or failed. On 926 messages (712 lies in 16 classes), PCM accepts 10 lies (1.4%), all stale evidence; plain A2A accepts all 712.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - PCM: A Proof-Carrying Message Protocol for Verifiable Multi-Agent Communication
Proof-Carrying Messages (PCM) is a protocol where each claim an AI agent sends carries evidence: records from an append-only Merkle log the agent cannot write, with inclusion proofs, signed by an attesting signer it cannot control. Receivers verify offline and mark each claim proven, unproven or failed. On 926 messages (712 lies in 16 classes) PCM accepted 10 lies, all stale evidence (1.4%), while plain A2A accepted all 712. Verification takes about 1 ms.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π‘οΈ Security - TrussTutor-Bench: Benchmarking large language models on solution accuracy, sycophancy, and error detection in statics
TrussTutor-Bench is a benchmark of 200 verified two-dimensional truss problems from statics, paired with automatically generated student solutions. It evaluates thirteen LLMs on solution accuracy (64.5% to 97.0%), error detection (90.4% to 100%) and sycophancy. Models rejected their own correct solutions in 1.6% to 50.0% of cases when a student insisted the work was wrong. Sycophancy separated models; error detection did not.
arxiv.orgpaperAdded 31 Oct 2026, published 28 Oct 2026π§ AI - Gold vs.~Translation: Are We Measuring Language, Knowledge, or Locality in Translated Benchmarks?
Gold vs. Translation is an October 2026 arXiv study of whether DeepL-translated MMLU measures the same thing as native benchmarks. It compares seven pairings against six native benchmarks in five Nordic and Baltic languages, using 29 models. Translated MMLU adds predictive value for curriculum-overlap benchmarks such as Skolprov (ΞRΒ²=.128) but not for Sweden-specific SwedishFacts (p=.242).
arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026π§ AI - Deogen: Automatic Benchmark Generation for Reasoning over Policies
DeoGen is an automatic, parametrizable benchmark generator for deontic reasoning, where a model applies policy rules to facts to reach a verdict. It autoformalizes rules into first-order logic, builds decision trees, and turns each tree path into a benchmark entry. Frontier models give the correct verdict 89-92% of the time, but only 22-40% on entries lacking enough information, even when told to abstain.
arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026π§ AI - Benchmarking decision models against generative large language models on medical multiple-choice questions
This preprint benchmarks 13 decision models against 15 generative LLM configurations, including different reasoning settings, on 1,273 text-only four-option MedQA questions. Accuracy ran 67.71% to 89.08% for decision models and 78.32% to 96.86% for generative configurations. Decision models were cheaper and faster, but answers with probability of at least 0.90 still had error rates of 1.71% to 12.77%, and probability repeatability varied across three runs.
arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026π§ AI - Metacognitive Decoding: Efficient Inference for Diffusion Language Agents
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 2026π§ AI - Perceiving and Predicting Dynamic Sports: Spatiotemporal Intelligence in Vision Models for Egocentric Tennis Video
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 2026π§ AI - When Is Additional Retrieval Worthwhile? A Cost-Aware Study of Evidence Gain and Answer Reliability in RAG
This paper studies a cost-aware, two-action RAG routing policy that chooses between dense retrieval plus reranking and a hybrid lexical-dense retriever, using a regularized linear model to predict per-question evidence gain. On 60 held-out QASPER articles (188 questions), it triggers hybrid retrieval on 24.5% of questions and matches always-hybrid Hit@10 (78.70%). Answers from 4-bit Qwen3-4B show no clear correctness or faithfulness gain, and the assessments are model-assisted.
arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026π§ AI - OpenGray: a benchmark for evaluating the decisions of radiotherapy planning agents
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 2026π§ AI - Tessera: Structure-Completed Evidence Units for Web Retrieval-Augmented Generation
Tessera is a training-free evidence delivery layer for web retrieval-augmented generation. It rebuilds retrieved pages' HTML into fine-grained evidence units, each completed with page title, section path and table headers, then packs the relevant units into the reader context at the same token budget. Tracing evidence over full English Wikipedia shows chunking loses table and infobox content. Tessera adds 5 to 6 accuracy points on three QA benchmarks and carries over to agentic search.
arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026π§ AI - Exploring the Limits of the Delta Learning Hypothesis: Single-Pair Generator Biases in Direct Preference Optimization
This paper tests the delta-learning hypothesis for Direct Preference Optimization (DPO), where the larger model's response in a single model pair is treated as preferred. It shows, in theory and experiments, that fixed pairs add generator-specific biases that predict the preference label regardless of quality. DPO on single-pair data underperforms multi-model data, consistent with reward hacking, and adding more generators partly fixes it without an external judge.
arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026π§ AI 
Poly2MsgLeaks: Certified Key Recovery from Polynomial-to-Message LeakagePoly2MsgLeaks is a profiled chosen-ciphertext power-analysis attack on the SMAUG-T (TiMER parameter set) post-quantum KEM that recovers the full 512-coefficient PKE secret from six traces on an STM32F415. It recovers 999 of 1,000 unseen secrets and proves an incorrect key is released with probability at most 2^-2095.6. Single-write message assembly and a two-share masked P2M resisted the evaluated six-execution attack.
eprint.iacr.orgpaperAdded 28 Oct 2026, published 27 Oct 2026π‘οΈ Security- Measuring Users' Security and Privacy Concerns About Third-Party Community Bots on Discord
Measurement study of 23,102 third-party Discord bots and 183,142 user reviews (2021-2026, 49 languages). A fine-tuned DeBERTa classifier plus manual validation yields 8,832 security and privacy reviews, mapped to a 31-theme taxonomy; Data Sharing (30.5%) and Data Collection (16.2%) dominate. Permission count and admin status correlate only weakly with user concerns. The authors build BotLens, an MCP-server auditing framework with six tools.
arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026π‘οΈ Security - Folding Attention Biases into Keys Makes Compacted KV Caches Run on Standard Kernels
The paper folds the per-key logit bias of Attention Matching (AM) KV cache compaction into the keys along one closed-form direction per head, fitted on context queries. The result is a plain cache that FlashAttention and vLLM paged caches can use, with output error bounded by how far each query's projection on that direction is from 1. Unmodified vLLM serves folded caches through a KV connector. On QuALITY and LongHealth the fold is within noise of AM in 13 of 14 settings. Under AM's published per-head budgets it is within noise in 9 of 10.
arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026π§ AI - Cheap AI-Assisted Reverse Engineering and What It Will Do
"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 2026π‘οΈ Security - Revisiting ZMAC+: Birthday-Bound Attacks and a Beyond-Birthday-Bound Repair
ZMAC+ is a tweakable-block-cipher MAC (List and Nandi, ToSC 2017) with claimed beyond-birthday-bound security. This ePrint paper (2026/2647) shows a tag-swap attack: for two-block tags, a distinguisher and a fresh-message forgery with constant success probability using O(2^{n/2}) queries in the ideal-TBC model. It also corrects a counting error in the published Lemma 4 and proposes a repaired variant with an adaptive PRF bound of order q^2/2^{n+min(n,t)}.
eprint.iacr.orgpaperPublished and added 26 Oct 2026π‘οΈ Security - Non-profiled Single-Trace ML-DSA Secret Key Recovery by Horizontal Distribution Analysis
Non-profiled single-trace side-channel attack on ML-DSA-65 (Dilithium) running on ARM Cortex-M4, by Linus Backlund and Elena Dubrova (KTH). It compares observed sample distributions with expected Hamming weight leakage using a Gaussian mixture model, and stacks partitioned trace segments for multiple observations. Information-set decoding corrects misclassifications; with a 2^70 enumeration budget, the full secret key is recovered from one trace in 99% of attacks, using secret key unpacking as the attack point.
eprint.iacr.orgpaperPublished and added 26 Oct 2026π‘οΈ Security - Cryptanalysis and Repair of "Decentralized Multi-authority ABE for NC$^1$ from BDH"
Cryptology ePrint 2026/2642 presents a plaintext-recovery attack on the DBDH and CBDH variants of the Datta, Komargodski and Waters multi-authority ABE for NC1 (Journal of Cryptology, 2023). It needs one corrupt authority and two users, and succeeds with probability one in the static model. The flaw is an invalid extension of the Rouselakis-Waters LSSS matrix-normalization lemma. The paper also attacks two Datta-Pal (PKC 2023) attribute-based inner-product schemes and proves repaired versions secure in the random oracle model.
eprint.iacr.orgpaperPublished and added 26 Oct 2026π‘οΈ Security 
Addendum to GPT-6 Astra System Card: GPT-6.1 TerraAddendum to the GPT-6 Astra system card for OpenAI's GPT-6.1 Terra, rated Critical in cybersecurity and High in biological and chemical capability under the Preparedness Framework. It reports multiturn jailbreak and prompt-injection results, alignment tests (auto-review circumvention, ExploitGym honeypot, monitor evasion), and cyber scores: 97.10% ExploitBench, 24.5% on the internal recent-vulnerability port, 5 of 22 Sandbox Bench targets.
deploymentsafety.openai.compaperPublished and added 26 Oct 2026π‘οΈ Security- SentryBench: A Cost-Sensitive Benchmark for Adversarial Robustness of Tool-Using LLM Agents in Public-Safety Deployments
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 2026π§ AI - Revisiting AES Key Wrap: Forgery Bounds, Structural Observations, and Recommendations for the Revision of NIST SP 800-38F
Revisiting AES Key Wrap is an ePrint paper (2026/2636) by John PreuΓ Mattsson, expanded from technical comments to NIST on the SP 800-38F revision. It shows the stated forgery probabilities ignore adaptive attacks and gives corrected bounds for KW, KWP and TKW. It also shows KW lacks beyond-birthday-bound security, since a forgery leaks a plaintext prefix and an encryption-only distinguisher exists.
eprint.iacr.orgpaperAdded 26 Oct 2026, published 25 Oct 2026π‘οΈ Security - The Patient Is Always Right? Evaluating Sycophancy in Medical Diagnosis with Graded Patient Beliefs
This paper evaluates sycophancy in medical diagnosis across nine AI models. Each model is shown clinical evidence plus a patient's belief in a specific but incorrect condition, with the belief's closeness to the evidence varied. Models adopted the belief 23.2% of the time on average, even for fictional conditions, and 43.0% under a Rare Disease Claimer persona. Medical fine-tuning and larger scale reduced susceptibility.
arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026π§ AI - On the Inapplicability of Residue-Confined Error Attacks to CRT-FHE with Non-Degenerate Error Distributions
Anil Kumar Pradhan's October 2026 ePrint rebuts the Olejnik and Naskrecki (2026) key-recovery and plaintext-recovery attack on the Pradhan CRT-RLWE fully homomorphic encryption scheme. It argues the attack works only because the chosen error distribution lies in the kernel of the CRT encoding, which makes the effective error zero. It also argues that the intended centered-binomial errors make that event negligible at the reported parameters. This is the author's own response, not independent verification.
eprint.iacr.orgpaperAdded 26 Oct 2026, published 24 Oct 2026π‘οΈ Security
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