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huggingface.copaper🧠 AI

Your Benchmark Is an Empirical Measure Over Difficulty

The paper treats a benchmark as an empirical measure over item difficulty and formalizes two failure modes: measure mismatch (benchmark difficulty distribution differs from the intended one) and coverage gaps (uncovered difficulty regions that hide localized training progress). It proposes two diagnostic metrics with theoretical guarantees and analyzes 25 popular benchmarks across 7 domains using estimated item difficulties. It also introduces BenchFill, a difficulty-targeted item rewriting algorithm that reduces local coverage gaps.

BenchFill

evals, researchΒ· Added 31 Oct 2026

arxiv.orgpaper🧠 AI

Retrieving from Your Own Mistakes: On-Policy Training of Embedding Models for Multi-Hop Retrieval-Augmented Generation

On-policy state training is a method for training chain embedding retrievers for multi-hop RAG on the states the encoder itself reaches, including those built on its own wrong retrievals. Each state is labeled with the earliest missing gold passage, other missing gold passages are masked, and updates are contrastive and mixed with teacher-forced states. It retrieves complete evidence more often than teacher-forced training and graph-based systems, and transfers to harder long-chain HotpotQA and 2WikiMultiHopQA variants the authors built.

RAGon-policy state trainingcontrastive learning+1
HotpotQA2WikiMultiHopQAarXiv

research, llm-apps, trainingΒ· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

Don't Lose the Thread: Empowering Long-Horizon LLM Agents with Cognitive Resource Self-Allocation

CORAL (Cognitive Resource Self-Allocation) is an agent-callable working-memory toolset that lets an LLM agent checkpoint task progress and verified facts, then reset its cluttered context and resume from a clean state. A Multi-episode Agentic Reinforced Policy Optimization algorithm trains the checkpointing. With an 8B model it reaches 42.8% average accuracy on GAIA, uses 6-7x fewer input tokens than a ReAct agent, and adds 8.0 points to a proprietary agent on BrowseComp.

LLMReActCORAL
Multi-episode Agentic Reinforced Policy OptimizationGAIABrowseComp

agents, research, trainingΒ· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

Decompose, Estimate, Select: Coverage-Aware Context Construction for Long-Form RAG

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

RAGbi-encodercross-encoder+1
TREC RAG 2024Ξ±-nDCG@10V_strict

llm-apps, researchΒ· Added 31 Oct 2026, published 29 Oct 2026

eprint.iacr.orgpaperπŸ›‘οΈ Security

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.

BKZDeepBKZminimum weight codeword problem+4

otherΒ· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 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.

federated learningblockchain-powered federated learningprogressive learning+1
arXiv

research, trainingΒ· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 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 (Ξ”RΒ²=.128) but not for Sweden-specific SwedishFacts (p=.242).

LLM
MMLUDeepLSkolprov+2

evals, researchΒ· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 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.

multimodal large language model
AskStreamAsk-F1arXiv

evals, multimodal, researchΒ· Added 30 Oct 2026, published 28 Oct 2026

eprint.iacr.orgpaperπŸ›‘οΈ Security

Shor's algorithm compiled on cat qubits: 22 593 qubits for RSA-2048 in 3.14 days

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

Bitcoin
Shor's algorithmRSA-2048secp256k1+5

otherΒ· Published and added 28 Oct 2026

arxiv.orgpaper🧠 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.

LLMQwen
CALMLongMemEval-SLoCoMo+1

agents, researchΒ· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

Complementing Text Based Moderation with Multimodal Grounding: Detecting Fraudulent Inducement Behaviors on Short Video Platforms

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

MLLMLLMOCR
arXiv

research, multimodal, trainingΒ· Added 30 Oct 2026, published 28 Oct 2026

huggingface.copaper🧠 AI

What Effort Level Should I Choose? On the Economics of Reasoning Effort in LLM Agents

This paper treats LLM reasoning effort as a credence good and measures the counterfactual effect of effort levels on proprietary and open-weight models across four benchmarks, including long-horizon agentic tasks. Higher effort often fails on tasks lower effort solved, and on easy tasks the top level costs up to 42x the lowest. Starting at the lowest effort and escalating only after a verified failure matches or beats fixed top effort, usually at lower cost for proprietary models.

LLM
Hugging Face

research, evals, agents, ai-policyΒ· Added 28 Oct 2026

arxiv.orgpaper🧠 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.

vision-language modeldiffusion modelTransformer
arXiv

research, multimodal, evalsΒ· Added 28 Oct 2026, published 27 Oct 2026

arxiv.orgpaper🧠 AI

Interpretable Meta-Policies for Neural Decision Making

NEXUS is a hierarchical reinforcement learning framework that pairs neural skills with symbolic meta-policies, so transparent reasoning sits at the level of high-level skills rather than atomic actions. It uses object-centric observations and LLM interaction to design reward functions and meta-policies. Agents learn disentangled skills, align better with goals in the game manual, resist reward hacking more, and cope better with game simplifications than deep RL. The paper also compares neural, symbolic and neuro-symbolic meta-policy architectures.

LLM
NEXUSarXiv

research, training, ai-safetyΒ· Added 28 Oct 2026, published 27 Oct 2026

eprint.iacr.orgpaperπŸ›‘οΈ Security

Radical isogenies on every model

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

CSIDH
Thomas Decruradical isogeniesVΓ©lu's formulae+1

otherΒ· Added 28 Oct 2026, published 27 Oct 2026

arxiv.orgpaper🧠 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.

LLM
LiveSearcharXiv

evals, agents, researchΒ· Added 28 Oct 2026, published 27 Oct 2026

arxiv.orgpaper🧠 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.

LLM
OpenGrayOpenKBP-OptSAGE+3

evals, agents, researchΒ· Added 28 Oct 2026, published 27 Oct 2026

eprint.iacr.orgpaperπŸ›‘οΈ Security

Alg\{Ossa\}: Decentralized Secure Aggregation with Rotating Committees and Eventual Delivery

AlgOssa 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.

Algorand
OssaAlgOssaOPA+2

otherΒ· Added 28 Oct 2026, published 27 Oct 2026

huggingface.copaper🧠 AI

Trial by Trace: Benchmarking AI Agent Harnesses for Reliability and Failure Attribution

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

LLMHugging Face
TraceTrial-Bench

evals, agents, researchΒ· Added 27 Oct 2026

arxiv.orgpaper🧠 AI

Your Wish Is My Commit: Over-Compliant Refactoring in AI Coding Agents

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

CodexClaude CodeLLM
arXiv

coding-agents, research, evalsΒ· Added 28 Oct 2026, published 26 Oct 2026

eprint.iacr.orgpaperπŸ›‘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.

Datta-Komargodski-Waters multi-authority ABEDatta-Pal attribute-based inner-product functional encryptionRouselakis-Waters LSSS matrix-normalization lemma+3

otherΒ· Published and added 26 Oct 2026

eprint.iacr.orgpaperπŸ›‘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.

Ducas, Engelberts and LoyerKirchner and FouqueBKW algorithm+1

otherΒ· Published and added 26 Oct 2026

eprint.iacr.orgpaperπŸ›‘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.

Gabidulin codes
Philippe GaboritGuruswami-Vardy theoremTwisted Square Membership+2

otherΒ· Added 26 Oct 2026, published 25 Oct 2026

arxiv.orgpaperπŸ›‘οΈ Security

The Policies of Programming Language Ecosystems: A "Qualitative" Analysis

The Policies of Programming Language Ecosystems is an October 2026 arXiv paper (cs.SE) that compares the policies of eight programming language ecosystems, including CRAN for R, across 38 policy categories. Three authors coded the policies with moderate agreement. Malware, intellectual property and content removal are covered everywhere, while package size limits, metric manipulation, typosquatting and binary uploads diverge.

CRAN
typosquatting

Supply Chain, GRCΒ· Added 28 Oct 2026, published 25 Oct 2026

arxiv.orgpaper🧠 AI

InteractiveWorld: A Verifiable Human-Centric Environment for Building Better Interactive MLLM Assistants

InteractiveWorld is an environment for closed-loop interaction between an AI assistant and a simulated human agent who sees the scene, follows instructions and acts independently. InteractiveBench, built on it, has 120 multi-turn tasks in six categories scored by verifiable, state-based rewards. Across 12 frontier MLLMs, the best model, Claude Fable 5.1, reaches at most 20% on long-horizon Guidance and 25% on Planning tasks.

multimodal large language model
InteractiveWorldInteractiveBenchClaude Fable 5.1+1

evals, multimodal, researchΒ· Added 27 Oct 2026, published 25 Oct 2026

arxiv.orgpaper🧠 AI

HerHealthGPT: An Evidence-Grounded Conversational AI System for Women's Reproductive Health Copy title

HerHealthGPT is a conversational AI system for women's reproductive health that combines retrieval-augmented generation, context-aware clarification and source validation in a web interface. Evidence retrieval raises concern-category accuracy from 54.1% to 59.3% over fine-tuning alone on 540 messages from 90 cases. The clarification router reaches 85.6% recall and 86.2% specificity. In a 30-adult study, 83.3% preferred the retrieval-enabled version. The authors say clinical validation is still needed.

RAGLLM
HerHealthGPTarXiv

research, llm-appsΒ· Added 27 Oct 2026, published 24 Oct 2026

eprint.iacr.orgpaperπŸ›‘οΈ Security

Faster Matrix Multiplication Leads to Faster Lattice Sieving

Gao and Hu's IACR ePrint 2026/2622 shows that if the matrix multiplication exponent satisfies Ο‰ ≀ 9/4 βˆ’ Ξ·, heuristic lattice sieving runs in (3/2 βˆ’ Ξ΄)^(n/2+o(n)) time under the spherical heuristic from the BDGL analysis. It builds on OpenAI's recent result that Ο‰ ≀ 9/4. With known matrix multiplication algorithms, the method already gives subquadratic search inside BDGL buckets. Only the abstract is available, so concrete parameters are not shown.

BDGLspherical heuristicmatrix multiplication exponent

otherΒ· Added 26 Oct 2026, published 24 Oct 2026

eprint.iacr.orgpaperπŸ›‘οΈ Security

When Elliptic-Curve Semiprimality Certificates Reveal Factors

The paper formalizes elliptic-curve semiprimality certificates by adapting the Goldwasser-Kilian primality criterion. For certificates built with complex multiplication, a norm-lattice reconstruction factors N from (N, S, D) alone in expected polynomial time. The author uses it, plus public-point and quadratic-twist attacks, to factor Reble's 1084-digit and Broadhurst's 5061-digit moduli, and shows that hidden-cofactor, withheld-CM-map and private-isogeny repairs also fail.

Goldwasser-Kilian primality criterionReble modulusBroadhurst modulus+4

otherΒ· Added 26 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

Hey, guess what: CUES lead to inferences about typicality

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

LLM
EventTypEventTypGoldarXiv

research, evalsΒ· Added 27 Oct 2026, published 23 Oct 2026

eprint.iacr.orgpaperπŸ›‘οΈ Security

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.

ECDSAML-DSARSA+1
GordieHoweLongfellowBCS transformation+3

Identity & AccessΒ· Added 24 Oct 2026, published 23 Oct 2026

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