Your Benchmark Is an Empirical Measure Over DifficultyThe 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 2026π§ AI
Multi-View MoE: Letting Every Router Shape Expert ComputationMulti-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 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 - A Unified Hessian-Guided Framework for Post-Training Quantization
This preprint proposes a unified post-training quantization framework for large language models. It derives a Hessian-guided approximation of the output change caused by joint weight and activation quantization, using a tractable Fisher approximation. Quantization ranges, linear transformations and weight rounding are optimized together under one output-sensitivity objective. The authors report gains over state-of-the-art transformation and rounding methods in channel-wise and block-wise settings.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - DICE: Decentralized Incentive-driven Federated Learning for Efficient & Scalable Customization of Pretrained Models in Web3
DICE is a blockchain-powered federated learning (BCFL) algorithm for customizing large pretrained models among untrusted Web3 participants. It combines dynamic progressive learning and model slice verification, so hardware-limited participants can train part of the model, with a tree-structured committee consensus protocol and an efficiency-driven incentive scheme to scale to more data holders. Experiments cover vision and language models.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Appreciation Teaches Unauthorized Execution: Self-Evolution Degrades the Robustness of Personal Agents to Indirect Prompt Injection
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 2026π‘οΈ Security - 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 - LiveSearch: Questions as Executable Verifier Code for a Live Benchmark of Professional-Source Financial Search Agents
LiveSearch is a live benchmark for financial search agents that stores each question as executable verifier code: a typed program of calls to a professional financial data service plus a declarative extractor. Gold answers are recomputed daily with no human or LLM in the loop, so memorized answers go stale. Six tool-calling models score 70.9% to 76.7% under web search, and 70% of questions change gold value over three days.
arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026π§ AI 
Alg\{Ossa\}: Decentralized Secure Aggregation with Rotating Committees and Eventual DeliveryAlgOssa is a decentralized secure aggregation protocol built on Ossa, a generic committee-aided, single-server construction for one-shot clients (following OPA and Willow, CRYPTO 2025). Ossa realizes secure aggregation with non-selective abort against a malicious server. AlgOssa adds Algorand-style sortition to rotate leaders and committees, bounds the chance of an honest server, and gives eventual delivery. Classical and post-quantum routes are evaluated.
eprint.iacr.orgpaperAdded 28 Oct 2026, published 27 Oct 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 - Maximum-Likelihood Decoding of Gabidulin Codes Is NP-Hard Even in Dimension One
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 {Ξ»g} 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 2026π‘οΈ Security - Detecting and Understanding Ambiguity in Visualizations with Role-Playing Vision Language Model Interpretations
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 2026π§ AI - TriageRAG: Failure-Aware Self-Training for Adapting Retrieval-Augmented Language Models to Specialized Domains
TriageRAG is a failure-aware self-training method for adapting retrieval-augmented LLMs to specialized domains using only an unlabeled corpus. It probes the student on self-generated questions with and without retrieved context, sorts them into known, solved, context-failure and retrieval-failure, and routes each to a matching training signal. With Qwen3-4B on 11 datasets it beats budget-matched SimRAG by 1.2-6.4 points and recovers closed-book accuracy where retrieval hurts.
arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026π§ AI 
IQuest-Q1: Advancing Foundation Capabilities for Agentic CLI SystemsIQuest-Q1 is an open-source sparse Mixture-of-Experts model with 320B total and 15B activated parameters, built for agentic coding in CLI systems. Pre-training combines Stable SplitNorm, Muon and muP with a two-stage curriculum that raises the coding-data share in stage two. Mid-training extends context, and post-training mixes SFT, domain-specialized RL and MOPD across scaffolds, followed by model merging.
huggingface.copaperAdded 24 Oct 2026π§ AI- Faster and Smaller Anonymous Credentials from Standardized Signatures
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 2026π‘οΈ Security - Learning with Quantization and Worst Case Lattice Problems
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 2026π‘οΈ Security - Sharpness-aware Orthogonalized Momentum Algorithms
SOMA (Sharpness-aware Orthogonalized Momentum Algorithm) combines sharpness-aware minimization (SAM) with the Muon optimizer for LLM training. It avoids SAM's extra gradient computation by using stale, purely stochastic gradient noise as the adversarial perturbation. The authors prove sample complexity does not depend on perturbation direction, study robustness to heavy-tailed gradient noise, and test SOMA against post-training quantization degradation and catastrophic forgetting during fine-tuning.
arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026π§ AI - How Much Do You Trust A Dog? Quantifying Hint-Following in Reasoning Language Models
This paper measures how hints such as "[Source] thinks the answer is (X)" shift LLM answers on MMLU questions, varying the source between a professor, a friend and a dog across Qwen, Gemma and SmolLM models. The shift fits an exponential tilt: a roughly constant mean change in the hinted answer's log-odds, with susceptibility largely shared across sources. The shift persists when the source is declared inaccurate or the prompt warns against trusting it. Synthetic experiments show how KL-regularized reward training could produce this pattern.
arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026π§ AI - Prompt Injection Through Forensic Evidence: A Taxonomy and Proof of Concept of Anti-Forensic Attacks on LLM-Assisted Digital Investigations
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 2026π‘οΈ Security - Protection Is Not One Event: A Survey of RAG Protection Research by Measured Endpoint
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 2026π‘οΈ Security - HAETAE on a Diet: Less Stack, Less Time on Low-End Microcontrollers
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 2026π‘οΈ Security - Benchmarking Arabic Dialects for Math Problems
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 2026π§ AI - Efficient NTRU+ Implementations on Cortex-M
Efficient NTRU+ Implementations on Cortex-M presents NTRU+ KEM implementations built on 3-way NTTs for Arm Cortex-M0+, M3, M4 and M7. It adds omega-folded front layers that handle ternary inputs without modular reduction. The speed-optimized version cuts execution time by up to 52.43% against the official optimized C code. The memory-optimized version cuts peak stack use by up to 82.60%.
eprint.iacr.orgpaperAdded 21 Oct 2026, published 20 Oct 2026π‘οΈ Security - A Systematic Evaluation of Fault Attacks and Countermeasures on ML-DSA Rejection Sampling
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 0.04β0.05% on x86-64 and 0.83% on Cortex-M4 for the third check.
eprint.iacr.orgpaperAdded 21 Oct 2026, published 20 Oct 2026π‘οΈ Security - Post-quantum commutative group actions from oriented superspecial abelian varieties
Castryck, Dina, Kutas and Lorenzon study the Shimura class group action on superspecial principally polarized abelian varieties with a CM orientation, as a post-quantum commutative group action for key exchange. They give tools to represent CM orientations through higher-dimensional isogenies and to compute the action in the restricted effective group action model. These rely on a CM version of Kani's lemma and a trick that often halves the ambient dimension. The paper also gives a method for finding oriented superspecial abelian surfaces and analyses obstructions to transitivity.
eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026π‘οΈ Security - E-Layout: Layout-Stage Logic Restructuring via RL-Optimized Equality-Saturated Graph Retrieval-Augmented Generation
E-Layout is an ML-driven framework for post-placement logic restructuring in IC physical design. It builds equivalent logic alternatives with e-graph equality saturation, links logic choice to physical realization through a graph-based RAG model, and refines the choices with reinforcement learning on design-level timing feedback. On 9 designs it gives about 6% TNS and 2% WNS improvement and 1% less area than a commercial physical design flow.
arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026π§ AI - Concrete LDVR Attacks with Fewer Signers
Concrete LDVR Attacks with Fewer Signers is an ePrint paper by Renas Bacho on the low-dimensional vector representation (LDVR) problem behind adaptive security of threshold Schnorr. It combines a Chinese remainder theorem construction with a generalized birthday search to attack 488 signers with 173 corruptions over the Curve25519 scalar field, down from 5,020 signers. For adversarial evaluation points, it reaches optimal success probability and breaks 228 signers with 114 corruptions in 168.60 seconds.
eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026π‘οΈ Security - SHADE: Shrinkage via Halted Alpha-stable Denoising for Heavy-Tailed Probabilistic Time Series Forecasting
SHADE is an alpha-stable LΓ©vy 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 2026π§ AI - A Note on the Security of ECDSA with Presignatures or Biased Nonces
A Note on the Security of ECDSA with Presignatures or Biased Nonces (Couteau and Makriyannis, ePrint 2026/2545) examines alternative ECDSA modes built for faster distributed signing. It shows that the security of some variants used in deployed libraries is equivalent, in the generic group and random oracle model, to the average-case hardness of k-SUM. It also proves fine-grained lower bounds on generic group attacks against modes analyzed by Groth and Shoup (Eurocrypt 2022).
eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026π‘οΈ Security - SpanGuard: Decoupling Instruction Localization from Authorization for Lightweight IPI Defense
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 2026π‘οΈ Security - From if to Oops: Turning a conditional into a key-recovery oracle
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 2026π‘οΈ Security - Computing endomorphisms of superspecial principally polarized abelian surfaces in time and memory \(p^{4/5+o(1)}\)
Computing endomorphisms of superspecial principally polarized abelian surfaces is a cryptanalysis preprint by Marc Houben and Lam L. Pham, posted to IACR ePrint in October 2026. It gives an algorithm that, under a standard heuristic, finds a non-scalar polarized endomorphism in expected time and memory p^(4/5+o(1)). The abstract is the only text available, so the method and its effect on specific schemes are not described.
eprint.iacr.orgpaperAdded 19 Oct 2026, published 18 Oct 2026π‘οΈ Security - Revisiting the Security of VDOO
Revisiting the Security of VDOO is an ePrint paper (October 2026) on VDOO, a multivariate signature scheme submitted to China's NGCC program. It models VDOO as a multilayer Rainbow construction, keeps only the last oil space to get a generalized UOV instance, and applies wedge attacks. For the 512-bit parameter set it estimates 2^432 gates (reconciliation) and 2^379 gates (intersection), below the claimed level and the specification's figures.
eprint.iacr.orgpaperAdded 19 Oct 2026, published 18 Oct 2026π‘οΈ Security - SoK: PIAtlas: Decomposing the Unbounded Prompt Injection Attack Space
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 2026π‘οΈ Security - Superspecial points on Humbert surfaces and the probability of (N,N)-splitting
Determines, to first order in 1/p with an explicit error bound, the proportion of superspecial principally polarized abelian surfaces over F_p-bar (p β‘ 3 mod 4) that are optimally (N,N)-split. The expected number of optimal splittings is exactly 5NΒ·J_2(N)(p-1)/(pΒ²+1), so the split proportion is about 5NΒ·J_2(N)/p. This gives the stationary target density of the splitting-detection attack on the dimension-two superspecial isogeny problem, which was previously known only from experiments or a one-sided bound. The proofs use Siegel's mass formula and genus averages over refined Humbert invariants, and every formula was checked by exact computation at twelve primes.
eprint.iacr.orgpaperPublished and added 17 Oct 2026π‘οΈ Security - FedRAPP: Federated Test-Time Prompt Tuning for Vision-Language Models with Reliability-Aware Prompt Personalization
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 2026π§ AI - SLEEK: Sparse Quasi-Cyclic Codes with Closed-Form DFR Key Encapsulation
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 1.3xβ2.7x faster than BIKE.
eprint.iacr.orgpaperAdded 17 Oct 2026, published 16 Oct 2026π‘οΈ Security - I Want to Ride My BIKE: Customizing Bit-Flipping Decoders for Ephemeral Key KEMs
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 2026π‘οΈ Security - Identify, Classify, Act: Per-Instruction Controlled Compliance for LLMs over External Data
EDIC is a decomposed reward framework that trains LLMs to decide, for each instruction embedded in external data such as documents or tool returns, whether to follow or reject it. A judge initialized with SFT and refined with GRPO attributes compliance per instruction and yields separate rewards for beneficial and harmful compliance. On Qwen3-4B it raises harmful-rejection by up to 7.7% and Arena-Hard-Auto utility by 12.5% over the strongest prior defense.
arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026π§ AI - Perceive, Predict, and Plan: a Unified World Action Model for Egocentric Dexterous Manipulation
P^3-WAM is a unified world action model for egocentric dexterous manipulation with three modes: Perceive (reconstruct articulated hand motion and camera trajectories from video), Predict (generate visual futures under prescribed actions) and Plan (jointly generate action trajectories and their anticipated visual outcomes from an initial image, a language instruction and an optional visual goal). It adds a compact structured 3D action representation to a pretrained video diffusion transformer through a mixture-of-transformers design with shared base weights, modality-specific adaptation and bidirectional visual-action attention. Randomized modality visibility lets video and 3D actions act as either context or target. Unified training beats single-mode training and outperforms specialized baselines on 3D hand reconstruction and action-conditioned video prediction across four datasets, and gives better instruction adherence than video-generation baselines.
arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026π§ AI - Rethinking Routing Exploration in Modern Mixture-of-Experts
The paper identifies an "exploration dilemma" in modern Mixture-of-Experts models. Shared experts reduce the variability of token representations, which makes routing less exploratory. Fine-grained expert segmentation enlarges the combinatorial routing space and so needs more exploration, which means its extra expressiveness may go unrealized. It proposes MirrorMoE, which shrinks the exploration space by suppressing unnecessary experts per token, and reports consistent gains across model scales.
arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026π§ AI - Knowledge That Is Linear Before Fine-Tuning Generalizes After It
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 2026π§ AI - Multi-sourced AI Tutor for Non-Native English Speaker Students
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 2026π§ AI - Onboard Lightweight LLM Reasoning for Human-Aware Safe UAV Navigation
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 2026π§ AI - GraphACM: Graph-based Action Consistency Measurement for Uncertainty Quantification in LLM Agents
GraphACM merges equivalent states from multiple rollouts of the same LLM agent task into a directed, action-labeled graph, so actions can be compared at shared decision states. It defines node-level and global action-consistency measures, and uses the global score as an uncertainty proxy to predict task success or failure. Across three LLMs on ALFWorld, AppWorld and WebShop it reaches an average AUROC of 0.811, above the strongest behavioral-consistency baseline (0.729) and verbalized confidence (0.769). It also shows where in a trajectory agent actions diverge.
arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026π§ AI - Reaction Attack on CKKS Deployments with Asymptotically Optimal Query Complexity
Shows a protocol-level reaction attack on CKKS homomorphic-encryption deployments that publish coefficient-to-slot (C2S) evaluation keys and let the client return a 1-bit validity response. A malicious evaluator shifts a public encryption key, uses the public C2S layer to repack it into a ciphertext whose validity depends on one secret coefficient, and recovers a ternary secret key with O(H) queries, within a factor 2/log2(3) of the entropy lower bound. Each query is re-randomized with a fresh encryption of zero to evade trivial detection. The authors implemented it end to end in Lattigo v6.2.0 and recovered full keys across four standard CKKS parameter sets. It does not break RLWE or CKKS IND-CPA security.
eprint.iacr.orgpaperPublished and added 15 Oct 2026π‘οΈ Security - Thinking in Graphs: Neo4j-Backed Exploit-Chain Reasoning for LLM Penetration-Testing Agents
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 2026π§ AI - edaBits in the Exponent (edaBitE) and Issuer-Hiding Blind Signatures from ML-DSA and VOLE-in-the-Head
Presents the first blind signature in which the issuer runs unmodified NIST-standardized ML-DSA, so it works with certified secure elements. Issuance is an ML-DSA signature on a commitment. The blind signature is a VOLE-in-the-head zero-knowledge proof of knowledge of that signature, about 1 MB, under 1 s to produce and 0.5 s to verify. Security reduces to ML-DSA EUF-CMA and the proof system. Issuer-hiding and selective disclosure add a few tens of KB. The paper also introduces edaBitE, a batch consistency check between binary and prime-field commitments that replaces the costly edaBits checks.
eprint.iacr.orgpaperPublished and added 14 Oct 2026π‘οΈ Security - Causal Awareness in End-to-End Encrypted Group Messaging
Cryptography paper that extends Chen and Fischlin's two-user Signal analysis (Eurocrypt 2024) to group chats. It introduces "causal awareness": a receiver can reconstruct part of the causal context in which an accepted message was sent. Network delay or adversarial interference can otherwise show an authentic message in a misleading context. Senders transmit records of what they have sent and received, and receivers verify these records and compare them with their own. The paper gives correctness and security definitions parameterized by how much causal information must be recovered, baseline constructions with proofs, and checkpoint-aided and server-aided variants. It also discusses how to surface this information in a messenger's UI.
eprint.iacr.orgpaperAdded 14 Oct 2026, published 13 Oct 2026π‘οΈ Security - ML-Assisted Power Side-Channel Attacks on ML Accelerators in Shared FPGAs
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 2026π‘οΈ Security - Efficient Vectorized Implementation of MQOM
ePrint paper presenting a vectorized implementation of the MQOM v2.1 post-quantum signature scheme (a NIST additional-signature candidate). It uses a transpose-enabled paired-matrix trick to cut wasted SIMD work and memory traffic. It benchmarks key generation, signing, verification and memory on Arm NEON, x86 AVX2 and AVX-512, with peak dynamic memory down by up to 39.43%. Supplementary experiments check how the matrix-layout optimization carries over to the Round-3 MQOM v3 code. Useful for practitioners assessing the deployment cost of post-quantum signatures.
eprint.iacr.orgpaperAdded 14 Oct 2026, published 10 Oct 2026π‘οΈ Security - KedgeFlow: A Programmable Control Plane for Enterprise AI Agents
KedgeFlow specifies language-independent contracts so trusted controllers can authorize state-changing operations by enterprise AI agents and privileged gateways can enforce that authority independently of agent reasoning. It combines evidence-backed policy checks, signed permission for an exact action, a single-use shared allowance, target-side conditions and retained outcome records, with two conditional properties (at most one claim per allowance; every dispatch linked to a grant and claim) that hold even if the agent is compromised. A reference implementation exposes the path via MCP; evaluation on a three-member etcd cluster gave exactly one successful claim for each of 82 allowances across 6,560 concurrent attempts, and one committed effect in a 32-call gateway race. The authors report limits: permissive policy still authorized an undesirable effect, and distributed reservations, production access routes, crash recovery and real-target exactly-once effects remain untested.
arxiv.orgpaperAdded 15 Oct 2026, published 10 Oct 2026π§ AI - KedgeFlow: A Programmable Control Plane for Enterprise AI Agents
KedgeFlow proposes language-independent contracts in which trusted controllers issue signed exact-action permissions and privileged gateways enforce them, using single-use allowance claims, target-side conditions and retained outcome records. It aims to stop altered approved actions, concurrent permission reuse and retries after lost responses, even with a compromised agent. A reference implementation exposes this path via MCP; evaluation on a three-member etcd cluster showed 6,560 concurrent claim attempts yielding exactly one success per each of 82 allowances. It also reports that permissive policy still authorized an undesirable effect, and lists untested areas such as distributed reservations and production access routes.
arxiv.orgpaperAdded 15 Oct 2026, published 10 Oct 2026π‘οΈ Security - Hide and Seek: HIDE Concepts to SEEK explanations for black box models
Hide and Seek (HnS) is a concept-level attribution method for black-box vision models. It needs no model weights and no concept-labeled datasets. Given a concept and an image set, it uses a vision-language model to retrieve concept-aligned images and localize the concept, masks the localized region, and takes attribution from the change in the black-box model's output. It agreed better with ground-truth attributions than existing methods on CelebA and COCO-Stuff, and it works on tasks existing concept attribution methods cannot handle, such as image captioning and satellite image segmentation.
arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026π§ AI - Provable Subexponential Algorithms for Module-SIS and Module-LWE
ePrint preprint extending the DucasβEngelbertsβLoyer provable subexponential Gaussian sampler (built on Wagner's generalized birthday algorithm) to module matrices over power-of-two cyclotomic rings. It gives provable 2^{O(n/lnln n)}-time algorithms for Module-SIS and for decision and search Module-LWE using only the k+β ring samples provided, plus sample-preserving scalar search-LWE and narrow Module-LWE variants. Separate heuristic estimates suggest these Wagner-style attacks do not reach the NIST security targets of ML-KEM and ML-DSA.
eprint.iacr.orgpaperAdded 12 Oct 2026, published 8 Oct 2026π‘οΈ Security - OmniCapBench: A Deep-Structured Evaluation Framework for Fine-Grained Audio-Visual Captioning
OmniCapBench is a benchmark of 786 densely annotated videos that evaluates audio-visual captioning in multimodal LLMs. Instead of scoring free-form captions, it asks models for sets of atomic, verifiable units across three tracks (entity references, visual shots, audio events). These are scored with deterministic constraint checks plus localized LLM-based semantic comparisons, which gives coverage, localization and stable scoring together. Tests on frontier MLLMs show strong local perception but weak long-horizon audio-visual reasoning, especially identity drift and cross-modal misalignment.
arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026π§ AI - Caught in the Act: Probes Effectively Detect Sabotage and Catch Unverbalized Deception
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 2026π§ AI - ORCAGen: Orchestrating Context-Aware Malware Deception with RAG-Guided Generative AI
ORCAGen uses RAG over a curated knowledge base of malware procedures and active-defense strategies, plus structured prompting, to have LLMs generate proof-of-concept malware and matching deception orchestration code offline. Strategies are validated against the PoC malware before being added to a runtime playbook that enforces only the verified, deterministic logic. It is evaluated on GPT-4o, GPT-5.5, Gemini 3.5 Flash, Qwen3-Coder and Claude Sonnet 4.5 over synthesized scenarios and 150 real keylogger, infostealer and ransomware samples, measuring execution success, hallucination rate, refinement effort, deception effectiveness and runtime overhead.
arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026π‘οΈ Security - Reverse-Bleichenbacher: Bleichenbacher Attacks are Practical Again
Presents Reverse-Bleichenbacher, a variant of the Bleichenbacher RSA PKCS#1 v1.5 padding-oracle attack that recovers the least significant bytes first and stops once the TLS PreMasterSecret is found. With improved trimmers and coverage-optimized search, median oracle queries against a 2048-bit key drop from 11,428 to 1,801, and 68.3% of runs finish within a 30s timeout under single-server conditions. The authors find 640 vulnerable domains in the Tranco Top 1M (many behind Alibaba Cloud load balancers) and demonstrate MITM malicious update injection leading to remote code execution.
eprint.iacr.orgpaperAdded 11 Oct 2026, published 8 Oct 2026π‘οΈ Security - Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks
Evaluates Jev, a "System One" decision model that returns a choice among given options instead of generating text, on 13 multiple-choice benchmarks covering knowledge, reasoning and multilingual understanding. It is compared with 19 LLMs in frontier, representative and small tiers. Jev is competitive with frontier LLMs on knowledge and commonsense tasks, and best on MMLU-Redux and ARC-Challenge. It beats most representative LLMs and all small ones outside math, but is 17.7 points below the frontier median on MathQA and scores lower than all 19 LLMs there. This suggests specialized decision models can match general LLMs on knowledge-driven decisions but not on multi-step calculation.
arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026π§ AI - PathTime-VLA: Path-Time Decoupling for Factorized Post-Training of Vision-Language-Action Policies
PathTime-VLA reparameterizes Vision-Language-Action actions as a progress-indexed interaction path X(s) plus a positive interval-time profile that defines a monotone time law t(s). Geometry is thus decoupled from execution speed, so teleoperation timing artifacts don't contaminate the path target. A staged post-training recipe uses demonstrations and DAgger interventions for a target-domain prior, Speed-DQN to learn execution-speed multipliers from robot interaction, and Path-AWR to refine the diffusion path generator from rollout outcomes. On three tasks it reaches 58/60 successes versus 57/60 for BC+DAgger at fixed 1Γ, with about 39β52% shorter mean completion times on successful trials.
arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026π§ AI - Who Verifies the Verifier? Co-Evolving Inspectable Graders with Self-Improving Agents
Proposes making the verifier the evolving object in self-improving agent loops. The verifier is an inspectable expression over small, mostly deterministic drawback detectors, synthesized from clustered failures, gated at birth, and selected for agreement with a ten-item anchored reference set plus consensus over unlabeled outputs, never for the agent's score. On MBPP+ it gains +0.21 held-out agreement over the hand-authored seed and ends ahead of the bare LLM judge it contains. Removing the anchor guards collapses the verifier into an always-pass grader that still trains skills as well, so downstream task score cannot certify a self-evolved verifier. The paper's Double Ratchet pairs the verifier with a lifecycle-managed skill loop and retains 88-110% of the lift from ground truth or a rubric on code generation, enterprise text-to-SQL and reference-free report generation.
arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026π§ AI - RECAST: Learning to Compute the Right Context through Adaptive Evidence Routing
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 2026π§ AI - Explicit Geometric Chain-of-Thought for Vision-Language-Action in Autonomous Driving
GeoCoTDrive is a vision-language-action (VLA) framework for autonomous driving that introduces an explicit geometric chain-of-thought: it first grounds 2D regions corresponding to decision-critical cues, then retrieves localized 3D geometric priors from a geometric foundation model within those regions, interleaving these features into the autoregressive context to drive trajectory generation. The paper also introduces "planning-relevant grounding," a new region-level grounding supervision task, and the PlanningGrounding dataset to teach VLAs planning-oriented grounding, showing consistent gains in safety-critical planning across multiple end-to-end driving benchmarks.
arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026π§ AI - Does Document Structure Help Dense Retrieval? A Placebo-Controlled Ablation of Four Mechanisms Across Two Corpora
This paper runs a mechanism-isolating ablation of four document-structure treatments used in retrieval-augmented generation β structure-aligned chunking, LLM-generated chunk contexts, heading-path metadata, and hierarchical two-stage retrieval β under one protocol with matched chunk sizes and a semantically null placebo (shuffled heading paths) to control for the confound that any prepended text perturbs embeddings. Using a coverage-aware nDCG metric and document-clustered bootstrap with Holm correction across 200 Wikipedia Featured Articles (951 queries) and 1,585 QASPER papers (4,303 questions), it finds that structure-aligned chunking with real heading paths beats both contextualized fixed windows and the placebo, naive hierarchical retrieval hurts performance (traced to first-stage section recall failures), and gold structure beats LLM-induced structure on Wikipedia but not QASPER. Effect sizes are small but statistically significant and consistent across corpora, giving RAG practitioners evidence-based guidance on which structure-aware retrieval tricks actually help versus which are placebo effects.
arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026π§ AI - Hashing Beats Trees: Practical Oblivious Dictionaries in SGX
An IACR ePrint paper by Erik-Oliver Blass and Travis Mayberry proposing oblivious probabilistic hash tables (chaining, power-of-two-choices, stash-less cuckoo, de-amortized cuckoo hashing) built over Path ORAM as a replacement for oblivious AVL trees when deployed inside Intel SGX enclaves. It shows that SGX's small trusted client memory lets the position map recursion be shrunk to β€3 levels, making constant-access hash tables beat Ξ(log n) tree traversal, and derives via large-scale simulation the concrete padding parameters needed for "double obliviousness" (bounding hash-table rebuild probability to β€2^-40) that prior ORAM literature lacked. Their SGX implementation reports up to 20x faster lookups and 12x faster inserts than oblivious-tree baselines.
eprint.iacr.orgpaperAdded 8 Oct 2026, published 7 Oct 2026π‘οΈ Security - EntroPrefill: Renyi-Guided Context Pruning with Conditional Stability Guarantees for Retrieval-Augmented Generation
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 2026π§ AI - Foundations of Proactive Agents: Principles, Technical Layers, and Proactivity-Gym
This arXiv paper proposes foundations for proactive LLM agents organized around three joint principles (3T: Task Capability, Temporal Allocation, Trust) and a five-dimensional design space (task scope, anticipation horizon, activation trigger, processing timing, intervention depth). It introduces PROACTIVITY-GYM, a simulation-based testbed with multi-day scenarios, stateful environments, and persona-conditioned simulated users, and reports evaluations across 23 model-harness configurations plus a 30-participant human study showing sharp trust declines after intervention misalignment and a preference for imperfect sleep-time assistance over interruptive help.
arxiv.orgpaperAdded 6 Oct 2026π§ AI - Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration
This paper introduces Quantizer-Aligned Recalibration (QuAR), a single-pass, backprop-free test-time adaptation method for quantized vision transformers that recalibrates activations at frozen quantizers to counter distribution shift without updating model parameters. On ImageNet-C with ViT-B across 3-, 4-, 6- and 8-bit weight/activation precision, QuAR beats the strongest backprop-free baselines by up to 4.00 points while cutting latency 46% and adding only 0.17 MB memory overhead, with gains holding across continual streams, label shift, seven OOD suites, and three other backbones.
arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026π§ AI - Language Unalignability: Why Some Concepts Resist Cross-Cultural Benchmark Evaluation
This paper argues that the "Translation-Isomorphism Assumption" underlying multilingual LLM benchmarks is not just violated in practice but ill-posed for a class of concepts (pragmatic markers, honorifics, diachronically stratified terms). The authors formalize "Ξ±-unalignability" using a usage-cloud framework of contextualized embeddings, showing via FLORES-200 translation failures, LOBSTER reasoning scores, and a nine-model case study on the Yami language that models encode cross-lingual regularities (e.g. Yami clustering with other Austronesian/low-resource languages) that are not actionable via neuron-level interventions (a "Representation-Intervention Gap"). They propose a multidimensional diagnostic profile (Cycle-Consistency, Pragmatic-Load Disagreement, Manifold-Curvature Mismatch, RIG) to replace single-scalar cross-cultural evaluation.
arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026π§ AI - K-LMS: Design, Implementation, and Evaluation of LeightonβMicali Signatures with Korean Cryptographic Primitives
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 2026π‘οΈ Security - Towards Looped Models Done Right, Part II: Rethinking at Fixed Points
This paper studies looped language models β architectures that reuse a shared block of layers recurrently β and argues that as recurrent hidden states approach fixed points, the specific path taken to reach them matters less, enabling several efficiency tricks: truncated backpropagation through time during training, terminal key-value (KV) cache sharing across loop iterations for faster decoding, a distilled student model that prefills up to 1.79x faster, and RL policy gradients computed from saved rollout states that run 2x faster than backpropagating through the full trajectory. To make these fixed points reliable, the authors replace fixed-depth training with a learned depth prior (regularized with an entropy term to stay broad, improving on Huginn's fixed broad prior) and introduce orthogonal input injection, which removes the component of the injected input that the hidden state could otherwise amplify or cancel. Across model scales from 100M to 1.6B parameters, both changes lower perplexity, and at 1.6B the learned prior with a 3x smaller KV cache matches the downstream performance of full-cache fixed-depth training.
arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026π§ AI - MemPilot: Orchestrating On-Demand Multimodal Memory Curation for LLM Agents
MemPilot is a framework that lets LLM agents curate memory on demand rather than query-agnostically, using a multi-step policy trained via reinforcement learning to choose between retrieving from pre-built memory or delegating query-specific curation of raw multimodal history to heterogeneous LLMs and VLMs. The policy controls evidence amount, curation instructions, model choice, and visual access, and is optimized with objective-wise advantage decoupling and prefix-based marginal utility estimation to balance performance, cost, and latency. Tested across five multimodal agent-memory benchmarks, it achieves broader performance-cost-latency trade-off frontiers than existing trade-off-aware baselines.
arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026π§ AI - BazaarBench: Delegation Safety in Decentralized C2C Marketplaces Run by LLM Agents
BazaarBench is a simulated consumer-to-consumer marketplace benchmark that measures whether LLM agents acting as buyers and sellers stay safe and honest β tracking ownership, item condition, and commitments to detect six failure types across five transaction stages. Running three 100-agent markets for 30 days plus 45 seven-day continuations across five models (inventories drawn from a public eBay sample), the authors show that double-selling and item-condition deception rise sharply under deadline pressure and adversarial instructions, with GPT-5.4 completing 55.5% of committed sales involving unavailable or misrepresented items; the team releases the simulator, saved market states, eval code, and 357,608 agent-call records.
arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026π§ AI - BRANCH-MoE: Balance-Aware Tree Routing for Large Embedding Models
BRANCH-MoE proposes routing experts in a Mixture-of-Experts layer through a binary decision tree of depth log2(E), where each internal node's branching probability is centered on an exponential-moving-average of arrival-weighted scores, avoiding the need for an auxiliary load-balancing loss. The paper proves the EMA mechanism prevents routing-mass collapse under log-concave arrival distributions, links expert execution frequency to stochastic-gradient convergence rate, and shows that tree-prefix device assignment bounds cross-device communication. It is benchmarked against Switch softmax routing, DeepSeek-V3 dynamic-bias routing, Skywork logit-normalized routing, and hash routing on Criteo CTR, Forest Covertype, HIGGS, and YearPredictionMSD with E=16 experts and top-4 routing.
arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026π§ AI - BrainTRACE: Tracing Longitudinal, Multimodal, and Volumetric Evidence in Brain MRI Clinical Reasoning
BrainTRACE is a report-grounded benchmark for evaluating whether vision-language models can trace the evidence structure required for longitudinal brain MRI interpretation. It contains 7,273 scored VQA instances derived from 1,778 longitudinal patients, 7,299 MRI studies, and ~29k co-registered 3D MRI sequence volumes, organized across five levels of clinical reasoning and evidence demands like longitudinal comparison, multi-sequence integration, and volumetric spatial grounding. Evaluation of 20 VLM configurations finds that current models identify isolated visual cues but rarely compose them into grounded longitudinal clinical interpretations.
arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026π§ AI - ANT: A Multi-Granularity Network Traffic Dataset and Benchmark for Agents Behavior Auditing
ANT (Agent Network Traffic) is a dataset and benchmark for auditing LLM agent behavior from network traffic without inspecting private user content, covering 3,114 execution episodes across 20 tasks and five scenarios, with 276,417 bidirectional flows and 40,049 behavior-primitive segments organized into 47 macro groups. The authors benchmark 13 traffic-analysis baselines on three tasks β agent risk identification, scenario recognition, and behavior primitive classification β finding that methods struggle when malicious workflows mimic benign tasks and when scenarios share similar traffic patterns, while primitive classification works better for frequent, distinctive macro groups than for rare or semantically similar ones.
arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026π‘οΈ Security - SatBleed: Security of Commoditized Communication Modules in Satellites
SatBleed is an arXiv paper presenting the first in-depth security evaluation of commercial off-the-shelf (COTS) Communication Subsystem (COM) modules used in small satellites. The authors build a threat taxonomy for COM-targeting attacks, analyze representative COM hardware from multiple vendors, and uncover severe firmware, protocol, and architectural vulnerabilities. By correlating findings with open-source telemetry data, they identify at least 28 vulnerable missions currently in orbit that are susceptible to hostile takeover via their command-and-control link.
arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026π‘οΈ Security - One-Round Threshold XMSS and SPHINCS+ from Fully Homomorphic Encryption
IACR ePrint paper constructing and implementing the first one-round threshold signing protocol for the hash-based post-quantum signature schemes XMSS and SPHINCS+, producing signatures that verify unmodified against RFC 8391 and FIPS 205. The approach shares the secret key among t-of-n signing parties and evaluates the signing algorithm homomorphically using threshold FHE over discrete CKKS, achieving 3.4ms amortized per hash and 0.69s online threshold XMSS signing.
eprint.iacr.orgpaperAdded 8 Oct 2026, published 5 Oct 2026π‘οΈ Security - Area-Efficient Hardware Architecture of the Post-Quantum HQC Cryptosystem with Side-Channel Protection
This IACR eprint paper presents a resource-efficient FPGA hardware architecture for the NIST-selected HQC post-quantum KEM that defends against power-based side-channel attacks by reusing otherwise-idle KECCAK hardware to inject scrambling dynamic power during decoding, adding less than 0.5 percent area overhead compared to masking/shuffling countermeasures. The fully parameterized design supports HQC-128/192/256 and was prototyped on a Xilinx Artix-7 FPGA (139 MHz, 11,489 LUTs, 6,785 FFs, 4 DSPs), with functional correctness verified via NIST Known Answer Tests and leakage suppression confirmed via Test Vector Leakage Assessment.
eprint.iacr.orgpaperAdded 8 Oct 2026, published 5 Oct 2026π‘οΈ Security - Agentic-ZTA: A Multi-Agent Architecture for Autonomous Zero Trust Enforcement
Agentic-ZTA is a multi-agent architecture that operationalizes the NIST SP 800-207 Zero Trust Architecture control loop using coordinated AI agents. A Policy Enforcement Point intercepts access requests, enriches them with context, and routes them to a policy-engine agent that invokes domain-specialized core and supporting agents; policies are retrieved via RAG and embedded into agent prompts at inference time, with trust scores aggregated by a trust algorithm to produce the final access decision. The authors implement and evaluate the framework on a testbed, reporting 95.0% accuracy, 93.9% precision, and 96.3% recall on representative access-control scenarios.
arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026π‘οΈ Security - GPU-Assisted FAEST v3 Signing: Challenge Grinding and CPUβGPU Pipelining
An IACR ePrint paper presenting a GPU-assisted implementation of FAEST v3 signing, a post-quantum signature scheme, focusing on two problems: preserving the correct minimum accepting counter when parallelizing "challenge grinding" across GPU threads, and building a two-slot CPU-GPU pipeline to overlap CPU work with GPU batches for multi-signature throughput. Across the six non-EM FAEST v3 parameter sets, the authors find the CPU-GPU path beats optimized CPU-only signing for single-sign latency in 4 of 6 cases (up to 1.6x speedup for FAEST-192s) and wins throughput in 9 of 18 parameter/batch-size combinations, with GPU correctness validated to match the reference signature output.
eprint.iacr.orgpaperAdded 8 Oct 2026, published 5 Oct 2026π‘οΈ Security - N2SF-Based Post-Quantum Cryptography Migration Framework for Data in National and Public Institutions
An IACR ePrint paper proposes PQC migration decision criteria for data held by national and public institutions, derived from South Korea's N2SF (National Network Security Framework) protection requirements and each data object's cryptographic dependencies/operational state. It distinguishes migration actions (payload retention, key rewrapping, re-encryption, communication migration, signature renewal) from execution decisions (allow/defer/deny), requiring consistent completion checks, recoverable registered copies, and current authority. A reference implementation ran 225 policy trials and 9 interruption trials, showing per-stage/completion rechecking prevented unauthorized completions or reads that occurred when reusing initial decisions, and confirmed clean resumption after interruption; institutional approval and production-scale key management remain unvalidated.
eprint.iacr.orgpaperAdded 8 Oct 2026, published 5 Oct 2026π‘οΈ Security - X-Wing on Cortex-M85: Architecture-Aware Integration and End-to-End Evaluation
An IACR ePrint paper presenting an architecture-aware implementation of the X-Wing hybrid KEM (ML-KEM-768 plus X25519) for the ARM Cortex-M85, using its M-profile Vector Extension NTT and a shared-inversion trick during encapsulation to cut cycle counts by 12 to 19 percent versus a baseline, at a cost of about 35KB extra flash. The authors found that their optimized variant introduced secret-sign-dependent operand-address selection, a side-channel weakness, and produced a corrected version whose fix is only partial since some key-varying timing signals persist and remain unexplained, so no constant-time guarantee is claimed.
eprint.iacr.orgpaperAdded 8 Oct 2026, published 5 Oct 2026π‘οΈ Security - Batched LSH on ARMv8 NEON and Its Application to K-SPHINCS+
IACR ePrint paper presenting hand-written AArch64 NEON assembly implementations of LSH (the Korean standard hash used in K-SPHINCS+), comparing several published optimization techniques and introducing a four-message batched approach that reaches 1.53-2.0x the throughput of sequential KISA reference C code. The authors integrate this batched LSH core into the SPHINCS+ reference framework's WOTS+, FORS and Merkle tree computations to build the first optimized K-SPHINCS+, which signs 2.5-3.2x faster than reference implementations while producing byte-identical signatures across hash backends; all code and measurements are public.
eprint.iacr.orgpaperAdded 8 Oct 2026, published 5 Oct 2026π‘οΈ Security - Optimized NEON Implementation of MAYO on Apple M1
An IACR ePrint paper presenting an optimized ARMv8 NEON assembly implementation of MAYO Round 3 (one of NIST's post-quantum signature candidates) for Apple M1, generated to be byte-identical to upstream intrinsics but 2β2.6x faster for signing and verification across all four parameter sets. The authors also run a constant-time testing campaign with matched null runs and find that while linear-algebra operations are statistically indistinguishable from constant-time nulls, verification (on public data) exhibits a measurable key-fixed timing effect present in both their implementation and the upstream code.
eprint.iacr.orgpaperAdded 8 Oct 2026, published 5 Oct 2026π‘οΈ Security - Deploying Native-Rust KpqC: Pluggable Post-Quantum KEMs and Signatures across TLS, Space Links, and the OQS Ecosystem
This IACR ePrint paper presents a native-Rust, memory-safe implementation of the Korean KpqC competition's standardized post-quantum schemes β KEMs NTRU+ and SMAUG-T, and signatures HAETAE and AIMer β replacing the reference memory-unsafe C code. The authors expose all seven KEM parameter sets and four signature modes through a single pluggable provider demonstrated across three real deployment front-ends: a rustls TLS 1.3 key-exchange group, an OQS/liboqs-compatible C ABI, and an analytical CCSDS/SDLS satellite constrained-link model, showing SMAUG-T variants are smaller/faster than ML-KEM-768 on constrained space links and a memory-optimized NTRU+ build cuts peak stack by up to 25% at negligible latency cost.
eprint.iacr.orgpaperAdded 8 Oct 2026, published 5 Oct 2026π‘οΈ Security - Optimizing Montgomery Arithmetic for RSA on Cortex-M0+ and Cortex-M3
An IACR ePrint paper presenting optimized Montgomery multiplication/squaring kernels for RSA-2048 and RSA-3072 CRT private-key operations on ARM Cortex-M0+ and Cortex-M3 microcontrollers, targeting cryptographic tokens that must keep RSA for legacy authentication compatibility. Using 15-bit limbs, wrapped-sum carry recovery (M0+) and doubled half-diagonal buffer limbs (M3), the authors report 12.6β13.1% faster Montgomery operations and ~12.6β12.8% faster warm CRT-RSA cycles on M0+, smaller gains on M3, and a 31.47% cycle advantage over an NTT-based multiplication for 2048-bit operands.
eprint.iacr.orgpaperAdded 8 Oct 2026, published 5 Oct 2026π‘οΈ Security - Plotkin Attacks! On the (Mis)use of Raptor Codes in Asynchronous Verifiable Information Dispersal
Victor Shoup shows that "reconstruct-later" asynchronous verifiable information dispersal (AVID) designs β used by data-availability systems including an early Walrus design/testnet β are insecure when built on the standardized Raptor codes R10 (RFC 5053) and RaptorQ (RFC 6330). Using the Plotkin bound, he proves that a Byzantine disperser can choose a subset of f+1 honest fragments that fails to reconstruct the message, gives randomized polynomial-time algorithms to find such subsets, and implements a working attack that defeats an RFC 6330 RaptorQ decoder on every adversarial subset tested (success for f from 9 to 45). Benchmarks show plain Reed-Solomon coding is an MDS alternative that is often faster and never much slower than R10/RaptorQ, making it the safer practical choice for this design pattern.
eprint.iacr.orgpaperAdded 8 Oct 2026, published 4 Oct 2026π‘οΈ Security - IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusion Models
IDRF is a reward fine-tuning framework for few-step masked discrete diffusion generators that replaces the intractable sequence-level KL penalty in reverse-KL-regularized objectives with an inverse-distillation regularization term, proven to upper-bound the true KL divergence to the reference distribution. It frames few-step diffusion sampling as a finite-horizon Markov decision process and optimizes reward via a clipped policy-gradient objective over the student's own trajectories, without needing reference-model rollouts. Across DNA, image, and text generation tasks, IDRF reaches high reward with up to 32x fewer denoising steps than the reference model while reducing reward hacking and preserving sample quality.
arxiv.orgpaperAdded 5 Oct 2026, published 2 Oct 2026π§ AI
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