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.
Multi-View MoE is a sparse mixture-of-experts design that uses several independently initialized routers to select experts, then reuses their relative scores to mix learned latent views into each selected expert's input. Each expert still runs one forward pass, so active-expert count, FLOPs and all-to-all volume are unchanged. On a 24-layer 10B-A1B model trained on 708B tokens, it gains 1.18 points on a nine-task mean; Multi-Router alone gains 0.6.
Mixture of ExpertsTransformerMulti-View MoE+1
arXiv
researchΒ· Added 31 Oct 2026, published 29 Oct 2026
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.
LLMBayesian experimental designdigital twin+1
EpiCoacharXiv
agents, research, evalsΒ· Added 31 Oct 2026, published 29 Oct 2026
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.
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.
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.
indirect prompt injection
AI SecurityΒ· Added 31 Oct 2026, published 29 Oct 2026
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.
RAGQwen3BM25
QASPER
research, llm-apps, evalsΒ· Added 28 Oct 2026, published 27 Oct 2026
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
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.
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.
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
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.
vision-language modelLLM
arXiv
research, multimodal, evalsΒ· Added 27 Oct 2026, published 25 Oct 2026
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.
RAGLLMQwen3
TriageRAGSimRAGCS-Bench+1
research, llm-apps, trainingΒ· Added 27 Oct 2026, published 24 Oct 2026
IQuest-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.
Mixture of ExpertsStable SplitNormMuon+3
IQuest-Q1Hugging Face
models, training, coding-agents, researchΒ· Added 24 Oct 2026
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
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.
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.
MuonLLM
SOMAarXiv
research, trainingΒ· Added 26 Oct 2026, published 23 Oct 2026
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.
LLMQwenGemma+1
MMLUarXiv
research, ai-safety, evalsΒ· Added 26 Oct 2026, published 23 Oct 2026
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.
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.
QwenLLM
prompt injection
AI SecurityΒ· Added 24 Oct 2026, published 21 Oct 2026
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.
HAETAEML-DSAARM Cortex-M4+3
Low-Stack HAETAEHAETAE-2CHES 2026
Hardware & RFΒ· Added 24 Oct 2026, published 21 Oct 2026
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.
LLM
DAR-MGSMarXiv
evals, researchΒ· Added 22 Oct 2026, published 20 Oct 2026
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%.
Cortex-MNTRU+
Hardware & RFΒ· Added 21 Oct 2026, published 20 Oct 2026
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.
Cortex-M4
ML-DSAFIPS 204WO2025261784A1+1
Hardware & RFΒ· Added 21 Oct 2026, published 20 Oct 2026
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.
Shimura class group actionKani's lemmaIbukiyama-Katsura-Oort correspondence+5
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.
RAG
E-LayoutarXiv
researchΒ· Added 21 Oct 2026, published 19 Oct 2026
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.
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).
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.
LLM
indirect prompt injectionprompt injection
AI SecurityΒ· Added 21 Oct 2026, published 19 Oct 2026
huggingface.copaperπ§ AI
