Efficient Scalable Oversight via RewindingRewindable 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 2026π§ AI- The Athlete as a Scientist of One: Agentic Bayesian Experimental Design for Individualized Physiology from Consumer Wearables
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 2026π§ AI - MAServeBench: Benchmarking Multi-Agent LLM Serving Systems with Hierarchical Collaboration Workloads
MAServeBench is a benchmark that treats multi-agent collaboration as a configurable dimension of LLM serving workloads. Its composable stages model how agents coordinate and exchange context in documented multi-agent workflows, and Hierarchical Semantic Traces record operations and dependencies for reproducible comparisons. Task-linked measurements tie workloads to GPU/NPU behavior, showing serving efficiency does not always mean timely task completion. Code is released.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Quasi-polynomial time algorithms for block basis reduction of linear codes
Kolesnikov and Novoselov give block basis reduction (BKZ) algorithms for linear codes that need polynomial or quasi-polynomial numbers of calls to a minimum weight codeword oracle at fixed block size. The earlier Ghentiyala and Stephens-Davidowitz bounds are exponential. The paper adds a truncated BKZ and a DeepBKZ variant, with an implementation for block sizes 2 to 24 and approximation-factor estimates on random codes.
eprint.iacr.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π‘οΈ Security - 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 - Turing LLM Post-Training: Calibrated Distillation and Efficient Dynamic-MoE Execution
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 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 - REALM: Identifying Minimal Task Assets for LLM Agents
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 2026π‘οΈ Security - 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 - AskStream: When and What to Ask Proactively under Uncertainty in Streaming Video Understanding
AskStream is a multi-turn benchmark for streaming video understanding that adds a third proactive action, ask, alongside wait and answer, for cases where video or user information is incomplete. It has 438 videos, 2,714 manually annotated frame-level time windows (wait, ask, answer), four task categories and eight subtasks. The strongest model reaches only 52.18% on the proposed Ask-F1 metric, in both multiple-choice and open-ended settings.
arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026π§ AI 
Shor's algorithm compiled on cat qubits: 22 593 qubits for RSA-2048 in 3.14 daysShor'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 2026π‘οΈ Security- Think Before You Act: Decision Gate for LLM Agents
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 2026π§ AI - CALM: Conflict-Aware Lifecycle Memory Governance for Long-Horizon Language Agents
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 2026π§ AI - Structured Security Explanations for Learning-Based Vulnerability Detection
VulnLens is a framework for binary C/C++ function-level vulnerability detection. A label-blind LLM Explainer describes each function from seven fixed perspectives (data flow, memory access, object lifetime, arithmetic and type behavior, control flow, shared state, external-resource access). A supervised Detector fuses code and explanations by cross-attention. It reaches 75.5% accuracy and 77.2% F1 on a 526-function PrimeVul test set, and 66.4% accuracy on SecVulEval without retraining.
arxiv.orgpaperAdded 30 Oct 2026, published 27 Oct 2026π‘οΈ Security - Heads of Security: Learned Attention Gates Improve Prompt Injection Robustness
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 2026π‘οΈ Security - 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 - CRISP: Co-designing Pruning and Scheduling for Efficient MoE Inference on Edge Servers
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 2026π§ AI - Towards Quantum Sensing for Side-Channel Analysis
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 2026π‘οΈ Security - DCKV: Learned Transform Coding of the Prompt KV Cache in Hybrid Language Models
DCKV is a differentiable codec for the prompt KV cache of hybrid language models, which keep a KV cache only in their few full-attention layers. Transform, quantizer and entropy models (conditioned on prompt tokens and already decoded neighbouring positions) are trained end to end against a frozen model. On Qwen3.5-2B it stores a token in over 55 times fewer bytes than bf16 with at most 1.8 points lost on three long-context benchmarks, and over 100 times fewer with at most 3.6 points lost.
arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026π§ AI - Poly2MsgLeaks: Certified Key Recovery from Polynomial-to-Message Leakage
Poly2MsgLeaks 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 - OblivDGL: Oblivious Deep Graph Learning Made Practical
OblivDGL is a secure computation framework for GNN training and inference, modelled on Deep Graph Library. It uses an oblivious message-passing paradigm and an oblivious graph representation built on function secret sharing (FSS). It leaks only total node and edge counts. On four benchmark graph datasets it cuts training communication up to 1024x and runtime up to 14.3x against prior secure GNN systems.
eprint.iacr.orgpaperAdded 28 Oct 2026, published 27 Oct 2026π‘οΈ Security - Analyzing Compiler-induced Constant-time Violations with Compiler at Scale
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 2026π‘οΈ Security - Reva: Real-Time Vulnerability Detection through Contextual Probing of Large Language Models
REVA (REal-time Vulnerability Analysis) is a vulnerability detector that puts a module threat model, lightweight static analysis findings and call-graph information into one LLM forward pass. A trained probe reads the model's internal representations of the new function. On PrimeVul's held-out test set, REVA on GLM-5.3-Flash reaches 60.0% accuracy in about one second per function, matching the agentic VulTriage (59.2%) at over 250 times the speed and under a tenth of the cost. The probe is 14% to 17% more accurate than the LLM's own verdict.
arxiv.orgpaperAdded 28 Oct 2026, published 26 Oct 2026π§ AI - Reva: Real-Time Vulnerability Detection through Contextual Probing of Large Language Models
REVA (REal-time Vulnerability Analysis) is an LLM-based vulnerability detector that screens each new function in one forward pass. It feeds the model a pre-computed module threat model, lightweight static analysis findings and call-graph data, then reads the model's internal representations with a trained probe. On PrimeVul's held-out test set with GLM-5.3-Flash it reaches 60.0% accuracy in about one second per function, matching the agentic VulTriage (59.2%) while over 250 times faster.
arxiv.orgpaperAdded 28 Oct 2026, published 26 Oct 2026π‘οΈ Security - A Memory-efficient Implementation of Wiedemann XL
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 2026π‘οΈ Security - Subexponential Algorithms for LWE via Rejection Sampling on Superlattices
Subexponential Algorithms for LWE via Rejection Sampling on Superlattices is an ePrint paper by Ligeng Fan, Changpeng Shao, Hongbo Li and Dingkang Wang. It extends the Ducas, Engelberts and Loyer superlattice framework to give a provable classical search-LWE algorithm for prime q in [n^2, 2n^2], using m=4n samples and time and space exp(O(n/ln ln n)). A second variant runs in 2^((1/2+o(1))n/ln R) for general parameters. It resolves a gap left by Kirchner and Fouque.
eprint.iacr.orgpaperPublished and added 26 Oct 2026π‘οΈ Security - Decision Attacks on Known-Covariance Ring-LWE via Public Gram Determinants
Anis Bkakria's ePrint 2026/2638 gives polynomial-time decision attacks on Known-Covariance Ring-LWE (KCov-RLWE), the assumption Micciancio and Suhl introduced for simulation-secure threshold encryption. The public sample and covariance hint form a bordered Gram matrix whose determinant leaks a residual that differs between real and uniform samples. The attacks cover broad prime-modulus families at ring rank one and transfer to composite moduli with an odd prime divisor. Power-of-two moduli are not covered, and the paper does not break the full threshold-encryption scheme.
eprint.iacr.orgpaperAdded 26 Oct 2026, published 25 Oct 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 - Intervention-Certified Amplification: A Validity Gate for $Ο$-Weighted Post-Training
Intervention-Certified Amplification (ICA) is a validity gate for sigma-weighted hybrid SFT/RL post-training. The paper shows that DIAA, a per-sample sigma-weighted hybrid, emits unexecuted tool-integrated reasoning on 29% of MATH-500 prompts (three times vanilla GRPO) even with Python fences removed from its anchor. ICA gates sigma with paired-rollout evidence, cutting TIR to 13.8% (frozen) and 11.2% (online) and raising accuracy from 47.5% to 48.7% and 49.3%. It transfers to GSM8K.
arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026π§ AI - Ranking Is Not Deciding: Benchmark Shortcuts and Operating-Point Collapse in Query-Time RAG Poisoning Defenses
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 2026π‘οΈ Security
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