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“PAIR”

arxiv.orgpaper🧠 AI

StreamClarify: Proactive Clarification for Grounded Streaming Video Question Answering

StreamClarify is a training-free framework for grounded streaming video question answering. It uses the entropy of a binary pre-answer judgment to decide whether to answer or ask for clarification, and pairs this with a temporal-pyramid KV cache and a CPU-side semantic retrieval archive. The authors build StreamClarify-QA (1,837 QA pairs from StreamBench and OVO-Bench). Macro accuracy rises by up to 8.2 points across three VLM backbones.

vision-language modelQwen3-VLKV cache

StreamClarifyStreamClarify-QAStreamBench+1

research, multimodal, evals· Added 3 Nov 2026, published 31 Oct 2026

arxiv.orgpaper🧠 AI

TRIAGE: Reliability- and Load-Conditioned Escalation for Two-Tier LLM Serving with a Semantic Evidence Judge

TRIAGE is a two-tier LLM serving scheme that escalates requests from a small model to a large one based on a post-generation reliability state and a load-dependent GPU price. On one A40 serving Qwen2.5-7B and 32B (AWQ) under vLLM, escalation cost rose 21.9x from 1 to 10 requests/s. A pre-registered claim that the reliability state detects corrupted evidence (AUROC 0.70 or higher) failed at 0.571. A semantic evidence judge raised detection to 0.846. The 7B to 32B pair showed only a +2.88 pp held-out gap, so the allocator declined to escalate.

vLLMQwenAWQ+2

Qwen2.5-7BQwen2.5-32BGPT-4+5

inference, research, evals· Added 3 Nov 2026, published 31 Oct 2026

arxiv.orgpaper🧠 AI

CivicBench: A Benchmark and Memory Framework for Web-Based Civic Data Analytics

CivicBench is a 400-task benchmark over four cities and two years that freezes civic Web data (service requests, boundaries, weather, demographics) into reproducible bundles. Executable contracts score computation, source linkage, coverage, scope, qualified answers and refusal. The paper also proposes Task-State Memory (TSM), which compiles tool observations into typed assertions and reconciles records only within compatible scopes. Eight of nine model pairs show positive mean-score gains with bootstrap intervals excluding zero.

LLMTask-State Memory

CivicBencharXiv

evals, agents, research· Added 3 Nov 2026, published 31 Oct 2026

arxiv.orgpaper🧠 AI

Labadain-MedQA: A Tetun Medical QA Dataset for Fine-Tuning and Evaluating Large Language Models

Labadain-MedQA is a Tetun medical question-answering dataset of 39,077 pairs, made by translating MedAlpaca's Medical Flashcards and WikiDoc Patient Information collections with Gemini 2.5 Flash. Two native-speaking healthcare professionals rated human translations higher than LLM ones (4.43 vs 4.02 out of 5). QLoRA fine-tuning of Gemma 4 12B raised mean accuracy about 10.8% on 325 Tetun-translated USMLE questions.

QLoRALoRAPEFT+5

Gemini 2.5 FlashGemma 4MedAlpaca+5

training, evals, research· Added 3 Nov 2026, published 31 Oct 2026

arxiv.orgpaper🧠 AI

Saidzoda-Tajik-E5-Large: Fine-Tuning a 560M Embedder for Tajik Retrieval

Saidzoda-Tajik-E5-Large is a Tajik text embedder fine-tuned from multilingual-e5-large-instruct (560M parameters) on 235,140 synthetic question and passage pairs, with hard negatives and English and Russian pairs mixed in. Top-10 retrieval on unseen laws rises from 80.2% to 93.6%, and on tax documents from 82.6% to 94.5%. The Belebele gain (92.2% to 95.2%) is not confirmed. bfloat16 training broke the model.

multilingual-e5-large-instructGiga-Embeddings-3Bhard negative mining+1

Saidzoda-Tajik-E5-LargeBelebeleTajik

research, training, llm-apps· Added 3 Nov 2026, published 31 Oct 2026

arxiv.orgpaper🧠 AI

SecPO: Principled Adversarial Training for Prompt Injection Security

SecPO is a DPO-variant loss from UC Berkeley for training LLMs against indirect prompt injection. It replaces the undefended-model reference on injected input with its output on the clean input, a one-line change, and pairs with a PAIR-based adversarial training pipeline. On Qwen3.6-27B, AgentDojo ASR@800 is 8.8% against 78.8% for Meta-SecAlign, with utility unchanged across nine benchmarks. Code, training set and weights are released.

DPOPAIRGCG+4

SecPOUC BerkeleySizhe Chen+5

training, research· Added 3 Nov 2026, published 30 Oct 2026

huggingface.copaper🧠 AI

WorkFactory: Turning Professional Intents into Executable Work for Scalable Agent Training

WorkFactory is a framework that builds coherent, executable professional tasks for training working agents. It plans an information support graph from professional intents, then uses probe executions to find and repair missing or inconsistent task information. The authors built 112,686 tasks across 612 occupations and fine-tuned Qwen models at 4B, 27B and 35B-A3B (WorkPilot). WorkPilot-35B-A3B rose from 879 to 1565 Elo on GDPval-AA v2.1.

QwenHugging Face

WorkFactoryWorkPilotGDPval-AA v2.1

training, agents, research· Added 30 Oct 2026

arxiv.orgpaper🧠 AI

Benchmarking color judgment in multimodal LLMs with CIE-computable ground truth

A five-task benchmark tests multimodal LLM color judgment against CIE colorimetric ground truth: AWB pair judgment, skin-tone bias direction, CCT estimation, Delta E 2000 pair ordering, and color naming under changing illuminants. Eight endpoints were tested. On ColorChecker-24 stimuli, closed frontier models reach CCT MAE of 715-1056 K versus 1358-2133 K for open-weight models, and a non-ColorChecker control shows this gap is partly template-dependent.

vision-language modelQwen3-VLGLM+2

GLM-4.5VGPT-5.4Claude-Opus-4.7+4

evals, multimodal, research· Added 31 Oct 2026, published 29 Oct 2026

x.comarticle🧠 AI

Damir I on X: "Two developers, four agents, one repo: a setup for pairs" / X

A guide to avoiding merge conflicts when two developers each run Claude Code and Codex on one repo. It cites Brun, Holmes, Ernst and Notkin: 16% of merges had textual conflicts, and 93% grew from unshared commits. It gives four steps (person/agent/task branch names, push at first commit, a partner-files script, a git merge-tree pre-push hook), then pitches the agentmash live-warning tool.

GitClaude CodeCodex+2

agentmashBrunHolmes+2

coding-agents· Added 30 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

Aligning Medical Vision Language Models at the Capability Boundary with Entropy Adaptive Rubric Grounding

MedCBR is a two-stage alignment framework for medical vision language models. Difficulty Weighted Boundary Preference Optimization (DBPO) trains on counterfactual preference pairs at the model's capability frontier. Entropy Gated Grounded Policy Optimization (EGPO) then applies distilled clinical rubrics according to prediction entropy. It reaches 61.2% average accuracy on six medical benchmarks, with the largest gains on differential diagnosis, and the two stages combine super-additively.

vision-language modelpreference optimizationreinforcement learning

MedCBRDifficulty Weighted Boundary Preference OptimizationEntropy Gated Grounded Policy Optimization+1

research, training, multimodal· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

MetaVul: Agentic Harness Discovery for Vulnerability Detection

MetaVul is a loop that rewrites the harness around a single vulnerability-detection agent (Codex CLI, Claude Code) without touching model weights. A sandboxed optimizer reads past harnesses, metrics and labeled predictions, and a fresh detector scores each function. It reports pairwise-accuracy gains of 11.4 to 23.3 points on validation and up to 25.8 points on 430 held-out PrimeVul pairs.

CodexClaude CodeLLM+1

MetaVulClaude Opus 4.6Opus 5+13

research, agents, coding-agents· Added 3 Nov 2026, published 29 Oct 2026

arxiv.orgpaper🛡️ Security

MetaVul: Agentic Harness Discovery for Vulnerability Detection

MetaVul is a self-improving framework that discovers agent harnesses for LLM-based vulnerability detection. Starting from a seed structured reasoning scaffold, it iteratively refines the harness using feedback from paired vulnerable and patched functions, and returns Pareto-optimal harnesses. Tested on a 202-pair validation set and a 430-pair held-out set, it improved pairwise accuracy by up to 23.3 and 25.8 points, and harnesses transferred across models (14.7 points held-out gain).

LLMMetaVul

AI Security, AppSec· Added 3 Nov 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

TrussTutor-Bench: Benchmarking large language models on solution accuracy, sycophancy, and error detection in statics

TrussTutor-Bench is a benchmark of 200 verified two-dimensional truss problems from statics, paired with automatically generated student solutions. It evaluates thirteen LLMs on solution accuracy (64.5% to 97.0%), error detection (90.4% to 100%) and sycophancy. Models rejected their own correct solutions in 1.6% to 50.0% of cases when a student insisted the work was wrong. Sycophancy separated models; error detection did not.

LLM

TrussTutor-BencharXiv

evals, research· Added 31 Oct 2026, published 28 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 (ΔR²=.128) but not for Sweden-specific SwedishFacts (p=.242).

LLM

MMLUDeepLSkolprov+2

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

arxiv.orgpaper🧠 AI

Repairs for Whom? Evaluator-Executor Mismatch in Selecting Repairs for Failed LLM Agents

Repairs for Whom? studies single-step repairs of failed LLM agent trajectories and asks whether common evaluators (greedy replay, stronger models, oracles, log-reading judges) pick repairs that help the deployed agent. Using independent rollouts in ALFWorld and WebShop, greedy replay loses 0.06-0.13 in expected gain, and Qwen2.5-14B rollouts misselect repairs for Qwen2.5-7B. The authors recommend verifying repairs with the executor that will run them.

LLMQwen

ALFWorldWebShopQwen2.5-7B+2

research, agents, evals· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

EnvSched: Environment-Aware Scheduling for Rollout-Efficient Agent Reinforcement Learning

EnvSched is an online scheduler for agentic reinforcement learning that picks which environment-task pairs to roll out next. It builds a dual-relation graph over environments and tasks, propagates past rollout statistics to predict within-task reward variance, and adds environment quotas and a similarity penalty. On Qwen3-8B it scores 48.81 average on BFCL-v3, tau-Bench and ACEBench-Agent after 40 steps, above random selection at 120 steps.

Qwen3

BFCL-v3tau-BenchACEBench-Agent+1

training, agents, research· 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-safety· Added 28 Oct 2026, published 27 Oct 2026

arxiv.orgpaper🧠 AI

Exploring the Limits of the Delta Learning Hypothesis: Single-Pair Generator Biases in Direct Preference Optimization

This paper tests the delta-learning hypothesis for Direct Preference Optimization (DPO), where the larger model's response in a single model pair is treated as preferred. It shows, in theory and experiments, that fixed pairs add generator-specific biases that predict the preference label regardless of quality. DPO on single-pair data underperforms multi-model data, consistent with reward hacking, and adding more generators partly fixes it without an external judge.

LLM

arXiv

training, research· Added 28 Oct 2026, published 27 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Revisiting ZMAC+: Birthday-Bound Attacks and a Beyond-Birthday-Bound Repair

ZMAC+ is a tweakable-block-cipher MAC (List and Nandi, ToSC 2017) with claimed beyond-birthday-bound security. This ePrint paper (2026/2647) shows a tag-swap attack: for two-block tags, a distinguisher and a fresh-message forgery with constant success probability using O(2^{n/2}) queries in the ideal-TBC model. It also corrects a counting error in the published Lemma 4 and proposes a repaired variant with an adaptive PRF bound of order q^2/2^{n+min(n,t)}.

ZMAC+

DoveMACZHashZAE+4

other· Published and added 26 Oct 2026

en.cryptonomist.charticle🧠 AI

Test-time training in robotics shows no clear gains in demo response

This article reports a study submitted October 23, 2026 by Qiangong Zhou, Jiafeng Liang, Yimin Dong, Yang He and Hongxiang Li. They added Test-Time Training (fast-weight memory) to the vision-language-action models InternVLA-A1 and Pi0.5, trained on paired human demonstrations and robot trajectories. Shorter videos, full-trajectory gradient connectivity and contrastive regularization gave no clear gain in demonstration-guided target selection at the tested scale.

vision-language-action modelTest-Time Training

InternVLA-A1Pi0.5Qiangong Zhou+4

research, multimodal, training· Published and added 26 Oct 2026

en.cryptonomist.charticle🧠 AI

A new AI method for saliency-aware image-text alignment improves image search

RoSA is a finetuning framework from Qiuyu Kong and three co-authors for long-text image-text retrieval. It targets "saliency collapse", where models over-rely on text tied to prominent visual features and miss contextual information. RoSA splits pairs into salient and contextual views using object-level saliency estimation and LLM text decomposition, then trains on both with plug-and-play auxiliary alignment objectives. Code and checkpoints are public.

LLM

RoSAQiuyu Kong

multimodal, research, training· Published and added 26 Oct 2026

eprint.iacr.orgpaper🛡️ 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

other· Published and added 26 Oct 2026

arxiv.orgpaper🧠 AI

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.

vision-language modelLLM

arXiv

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

eprint.iacr.orgpaper🛡️ Security

Chosen Commitments and Certified Decoding Regions in the Adaptive Threshold Schnorr Attack

This IACR ePrint 2026/2632 paper by Demirtaş and Yayla analyses the Crites–Stewart adaptive forgery attack on threshold Schnorr signatures, which needs one call to a sparse linear search oracle (bounded distance decoding of a scaled Reed–Solomon code). It shows that choosing the commitment coefficient vector does not help much: a wrapper adversary with Q challenges succeeds with probability at most QM/p, even if its solvers decode every witness for free. For the six original (N, τ) pairs, M stays below 2^24, so the bound is under 2^-100 after 2^128 challenges at p = 2^252.

Schnorr signaturesReed–Solomon codesfolded Reed–Solomon codes

Crites and Stewart attackGuruswami–WangAshvinkumar–Habib–Srivastava+2

other· Added 26 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

Intervention-Certified Amplification: A Validity Gate for $σ$-Weighted Post-Training

Intervention-Certified Amplification (ICA) is a validity gate for sigma-weighted hybrid SFT/RL post-training. The paper shows that DIAA, a per-sample sigma-weighted hybrid, emits unexecuted tool-integrated reasoning on 29% of MATH-500 prompts (three times vanilla GRPO) even with Python fences removed from its anchor. ICA gates sigma with paired-rollout evidence, cutting TIR to 13.8% (frozen) and 11.2% (online) and raising accuracy from 47.5% to 48.7% and 49.3%. It transfers to GSM8K.

GRPOPython

DIAAIntervention-Certified AmplificationMATH-500+2

training, research· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

Task Format as a Factor in Adaptation: A Controlled Comparison of LoRA and Full Fine-Tuning for Prompt-Safety Classification

This paper compares LoRA and full fine-tuning for prompt-safety classification using a matched 400M encoder-decoder pair (Ettin) across three task formats, with up to 15 seeds per cell and pre-specified analyses. Full fine-tuning leads LoRA by 2-3 pp at the pre-specified learning rates, and the encoder gap closes when both use the same learning-rate search. Training LoRA to write a reason after its verdict adds 4.0 pp, while a content-free sentence of equal length does not. Trained models beat a prompted 8B in domain but trail it by 8-23 pp on adversarial jailbreaks and over-flag benign prompts (27-66% against 2-3%).

LoRAEttinTransformer

training, research, evals· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

IntentDelta: A Human-Constructed Benchmark for Evolving User Intent

IntentDelta is a human-constructed multi-turn benchmark of 638 trajectories and 3,766 turns across WebShop and TravelPlanner. Each turn pairs a user utterance with a cumulative intention state and human reference actions. Across eleven agents, average Intention Recovery is 33.1% in TravelPlanner and 44.7% in WebShop, and average Action Score is 31.6% and 41.0%. Agents lose earlier requirements and confuse soft preferences with must-have constraints.

LLM

IntentDeltaWebShopTravelPlanner+1

evals, agents, research· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

VulDec: Vulnerability Detection in Binaries via Neural Decompilation

VulDec is a dataset and framework for detecting vulnerabilities in binaries by first decompiling them with fine-tuned LLMs. The dataset aligns real-world Assembly-C function pairs across several architectures with CWE labels. Tuned neural decompilers reach about 56% Edit Similarity and 43% BLEU-4, and tuned detectors reach a macro F1 of 0.61 on a realistic test set with a 3% vulnerability rate.

LLM

VulDecCWE

research, training· Added 28 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🛡️ Security

VulDec: Vulnerability Detection in Binaries via Neural Decompilation

VulDec is an arXiv paper (October 2026) presenting a dataset of real-world Assembly-C function pairs with CWE labels across multiple architectures, plus an end-to-end framework that uses neural decompilation for binary vulnerability detection. Tuned LLM decompilers reach about 56% Edit Similarity and 43% BLEU-4. Tuned LLM detectors reach macro F1 of 0.61 on a realistic test set with a 3% vulnerability rate.

LLM

VulDec

Malware & RE, AI Security· Added 28 Oct 2026, published 24 Oct 2026

eprint.iacr.orgpaper🛡️ 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

other· Added 26 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🛡️ Security

Deletion, Substitution, Repair and Transplant: Component Attribution in BARBICAN, a Layered Prompt-Injection Gateway for MCP

BARBICAN is a fail-closed prompt-injection gateway for MCP tool mediation. This paper shows its arbitration classifier ran on tokenizer-mangled text for a whole evaluation. Fixing it raised AUC from 0.392 to 0.855 while system recall fell from 80.67% to 80.34%. The routed band is 96.82% malicious, so deletion ablations mislead without substitution experiments and class-balance reporting.

Model Context ProtocolLLM

BARBICANprompt injection

AI Security· Added 27 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🧠 AI

Deletion, Substitution, Repair and Transplant: Component Attribution in BARBICAN, a Layered Prompt-Injection Gateway for MCP

The paper analyses BARBICAN, a fail-closed prompt-injection gateway for MCP tool mediation, using deletion, substitution, repair and transplant experiments. Its arbitration classifier ran on tokenizer-mangled text for a whole evaluation. Fixing that raised AUC from 0.392 to 0.855 while system recall fell from 80.67% to 80.34%. The band it receives is 96.82% malicious. The authors argue ablation figures need the band's class balance and substitution baselines.

Model Context Protocol

BARBICAN

evals, research, mcp· Added 27 Oct 2026, published 23 Oct 2026

blog.cloudflare.comarticle🛡️ Security

Bringing post-quantum cryptography to geographically fenced key management

Cloudflare's post-quantum GeoKey Manager, in closed beta for the Data Localization Suite, wraps customer private keys so only edge servers in an allowed region can unwrap them. It uses regional 256-bit root keys, derived application-specific HPKE key pairs (RFC 9180) with hybrid ML-KEM-768 and X25519, TPM-backed server authorization, kernel keyring storage, fail-closed checks, and soft and hard rotation. General availability is planned for Q1 2027.

CloudflareHPKEVault+2

GeoKey ManagerData Localization SuiteML-KEM+4

GRC, Cloud & Infra· Added 29 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🧠 AI

Standing on Open Shoulders: Regional Fine-Tuning of Open-Weight Camera-Trap Models for UK Wildlife with Modest Data and Compute

DeepFaune-UK is a fine-tune of the open DeepFaune camera-trap classifier on 28 UK classes, paired with MegaDetector v6. On 20,212 MammalWeb images it reached 88.8% strict accuracy against 56.5% (DeepFaune v1.5.0) and 59.0% (SpeciesNet). Re-applying the recipe to the DINOv3-based classifier gave 90.6%. Fine-tuning on about 150 crops per class matched full-data out-of-distribution accuracy, and a DINOv2 run took about 22 minutes on under 4 GB of GPU memory.

DeepFauneMegaDetectorSpeciesNet+2

DeepFaune-UKMammalWeb

research, training, multimodal· Added 26 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🛡️ Security

On Task, Off Track: Benchmarking LLM and Agent Robustness to Near-Target Prompt Injection

NTBench (Near-Target Benchmark) tests LLM and agent robustness to near-target prompt injection, where injected instructions steer a response toward a plausible but incorrect or harmful outcome instead of abandoning the task. It builds reference-guided target-instruction pairs on existing datasets, with human validation, and evaluates seven LLMs on ten language datasets and two agent benchmarks. Attack success rose 9.3 to 35.8 points (incorrect targets) and 1.7 to 17.3 points (harmful targets) over fixed-target baselines.

LLM

NTBenchnear-target prompt injectionprompt injection

AI Security· Added 26 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🧠 AI

Tampered ML: Evaluating LLM Judgment of Machine Learning Research

Tampered ML is a benchmark of 1,858 item-pairs, each pairing an experiment proposal reconstructed from a published ML paper with a minimally edited version that adds a targeted methodological flaw. Models must pick the sounder proposal in both presentation orders. A human study estimates 86% of items are valid (95% CI 81-91%). Opus 5 reaches 77.9% accuracy, and models are sensitive to order and favor flawed designs that look more specific, transparent, or conventional.

LLM

Tampered MLOpus 5arXiv

evals, ai-safety, research· Added 26 Oct 2026, published 23 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Pairwise Independence of Ten-Round AES

Pairwise Independence of Ten-Round AES (Zhongfeng Niu, Kai Hu, ePrint 2026/2608) proves that ten rounds of AES with independent uniform round keys and input/output whitening are within 3017/2^141 (below 2^-129) statistical distance of pairwise independent. The proof bounds the averaged difference-distribution matrix over five superbox layers, using Gram moments and MDS code constraints. Final-round MixColumns omission is retained.

AES

pairwise independencedifference-distribution matrixMDS code+1

other· Added 24 Oct 2026, published 23 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Balsamic: One-round Threshold UOV from GPU-Accelerated FHE

Balsamic is a complete homomorphic Unbalanced Oil and Vinegar (UOV) signer, used to build one-round threshold UOV signatures from a packed discrete-CKKS FHE circuit. A paired-pivot linear solver and pivot-success certificates allow encrypted retry selection. In 2-out-of-3 semihonest signing, each online partial signature takes 10.7 s on an RTX 6000 Ada and 7.77 s on a GB300, with 5.25 MB per response.

UOVCKKSFHE+2

Balsamicuniversal thresholdizerspaired-pivot solver+1

other· Added 24 Oct 2026, published 22 Oct 2026

arxiv.orgpaper🧠 AI

EpiSEM-Bench: Evaluating Safety and Evidence Recovery in LLM Agents under Missing Evidence

EpiSEM-Bench is an executable benchmark of 300 matched unsafe/safe task pairs across six domains. It tests whether tool-using LLM agents take prohibited actions when a tool response omits a safety-relevant fact, even though the request is benign and all returned statements are true. It finds large variation across ten models, and recovery access cuts unsafe execution for all ten.

LLM

EpiSEM-BencharXiv

evals, ai-safety, agents· Added 24 Oct 2026, published 22 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Integer Coordinate Families and Threshold RSA Signatures for Any Prime Exponent

Paper by Shivansh Jaiswal and Somitra Sanadhya (IACR ePrint 2026/2601) on integer coordinate families for threshold RSA signatures with prime exponent e. It shows the CT-RSA 2026 Ng-Lau scheme admits trivial forgeries and has a flawed proof. It proposes repaired scheme PiIC and integer secret-sharing scheme PiZ for any prime e and any (t,n), both proven TS-UF-CMA secure under the standard RSA assumption. It also gives super-polynomial coordinate lower bounds for pairwise families.

RSA

Ng-Lau schemePiICPiZ+3

other· Added 24 Oct 2026, published 22 Oct 2026

arxiv.orgpaper🧠 AI

Chunk Recomputation under Real Document Revisions: Measuring Update Cost and Retrieval Quality of Fixed, Recursive, and Content-Defined Chunking

Chunk Recomputation under Real Document Revisions measures how much text must be re-embedded after edits for three stateless chunkers (non-overlapping fixed-size, recursive paragraph-first, content-defined) on 120 pairs of adjacent Chinese and English Wikipedia revisions. Fixed-size recomputes 57-68% of the new-version workload, recursive 38-56%, content-defined 43-54%, while evidence recall at a 2048-token budget differs by under two points. The authors argue update cost should be reported alongside retrieval quality.

RAG

WikipediaarXiv

llm-apps, research, evals· Added 24 Oct 2026, published 22 Oct 2026

securityweek.comarticle🛡️ Security

PHP2Shell Exploitation Started Days Before Patches

PHP2Shell (CVE-2026-109618 and CVE-2026-109619) is a pair of critical PHP remote code execution flaws. Amazon saw the first exploitation on 17 October 2026, four days before patches on 21 October, and Cloudflare logged millions of attempts after disclosure. Censys, VulnCheck and VirusTotal counted over 1 million unpatched PHP hosts, and CISA urged updating to PHP 8.5.12, 8.4.27, 8.3.36 or 8.2.35.

PHPCloudflareVirusTotal+1

CVE-2026-109618CVE-2026-109619PHP2Shell+4

Threat Intel, AppSec· Published and added 22 Oct 2026

protos.comarticle🛡️ Security

Remotely exploitable PHP bug found on 70% of world’s websites

PHP2Shell is a pair of unauthenticated remote code execution flaws in PHP, CVE-2026-109618 and CVE-2026-109619, found by Calif using OpenAI's GPT-5.6 Sol during an OpenAI-funded audit. Fixes ship in PHP 8.5.12, 8.4.27, 8.3.36 and 8.2.35, with backports for 7.4-8.1 rolling out, while PHP 7 and 5 sites stay unpatched. Wiz confirmed the flaws; Cloudflare applied a stopgap and Finnish and Dutch government sites went offline.

PHPCloudflareOpenAI+1

CVE-2026-109618CVE-2026-109619PHP2Shell+5

AppSec, AI Security, Threat Intel· Published and added 21 Oct 2026

securityweek.comarticle🛡️ Security

PHP2Shell Patches Land as First Exploitation Attempts Hit Sensors

PHP2Shell is a pair of critical PHP core flaws, CVE-2026-109618 (out-of-memory corruption) and CVE-2026-109619 (stale global state), both CVSS 9.2 and giving unauthenticated RCE, confirmed on Apache with mod_php. Fixes shipped in PHP 8.5.12, 8.4.27, 8.3.36 and 8.2.35, with backports for 7.4 to 8.1. Exploitation attempts were reported by watchTowr, Hadrian, Patchstack, Cloudflare and Vega after a leak moved disclosure up from 22 October 2026 to one day earlier.

PHPApachemod_php+3

CVE-2026-109618CVE-2026-109619PHP2Shell+9

AppSec, Threat Intel· Published and added 21 Oct 2026

socket.devarticle🛡️ Security

PHP Patches Critical RCE Vulnerabilities, Organizations Urged to Take Unpatched Sites Offline

PHP2Shell is a pair of memory-safety flaws in the PHP engine, CVE-2026-109618 and CVE-2026-109619 (CVSS v4.0 9.2), fixed on 21 October 2026 in PHP 8.5.12, 8.4.27, 8.3.36 and 8.2.35. Unauthenticated requests can reach code execution, most readily through Apache mod_php, and Cloudflare reports active attack traffic. The post covers W3Techs data showing about 48.5% of PHP sites on end-of-life branches, the leak timeline, and the need to patch the runtime itself.

PHPCloudflareWordPress+2

CVE-2026-109618CVE-2026-109619PHP2Shell+5

AppSec, Threat Intel· Published and added 21 Oct 2026

securityweek.comarticle🛡️ Security

Browsers Block eMudhra Intermediate CAs Over Recoverable Private Keys

Eight intermediate CA certificates issued by Indian CA eMudhra in April 2025 had private keys generated with weak entropy, effectively all zeroes except the last byte, so they could be recovered almost instantly with baby-step giant-step. Mozilla and Google blocked them on 17 October 2026, Apple on 19 October and Microsoft on 20 October. eMudhra blamed a key ceremony script flaw that created a second, weaker key pair.

ChromeFirefoxCloudflare

eMudhraDennis Jacksonbaby-step giant-step+5

other· Published and added 21 Oct 2026

arxiv.orgpaper🧠 AI

InterWebCoder: Learning Interactive Webpage Generation from Executable Tests via Reinforcement Learning

InterWebCoder is a framework for interactive webpage code generation by multimodal LLMs. It synthesizes self-contained webpages with executable test scripts from recorded interaction trajectories, repairs failures using runtime evidence, then trains with test-ID-weighted SFT and GRPO using test pass rates and visual similarity as rewards. The pipeline yields 1,736 verified samples from 2,058 tasks. A 9B model scores 48.54 on IWR-Bench and 48.42 on Vision2Web, and reward latency drops over 97%.

GRPOvision-language modelLLM

InterWebCoderIWR-BenchVision2Web+3

research, training, multimodal· Added 22 Oct 2026, published 20 Oct 2026

arxiv.orgpaper🧠 AI

OptScientist: Automated Discovery of Optimization Laws

OptScientist is a framework that discovers compact symbolic optimization laws from data. It pairs a model that infers symbolic optimization problems with an LLM agentic system that uses literature priors, proposes candidate laws, abstracts mechanisms and suggests new data to collect. On OptFeynman, a new benchmark of 99 physics-derived problems, it recovers ground-truth laws for over 80%. It is also tested on human risky choice and central-bank monetary policy.

LLM

OptScientistOptFeynmanarXiv

research, agents, evals· Added 22 Oct 2026, published 20 Oct 2026

alignment.openai.comarticle🧠 AI

Beyond g-mean²: complementary metrics for monitorability

Beyond g-mean² is an OpenAI alignment research post proposing complementary metrics for evaluating model monitorability. It says pair-matched comparisons and post-control risk should be used alongside g-mean². The fetched text is a single sentence, so the metric definitions, experiments and results are not available here.

OpenAI

evals, ai-safety· Added 29 Oct 2026, published 20 Oct 2026

arxiv.orgpaper🧠 AI

Answering in the User's Variety: Calibrated Accommodation Pairs for Post-Training

Calibrated accommodation pairs (CAP) is a preference-data method that rewards chat models for matching a user's dialect density while keeping Standard English for Standard-English users. The paper traces SFT, DPO and RL stages across 10 open model lineages and finds post-training standardizes replies, while the DPO update still favours British and Australian spelling over AAE and Singlish. On a fully open model, CAP improves convergence on unseen features without added dialect for Standard-English users or lower instruction following.

DPOLLM

arXiv

research, training, evals· Added 22 Oct 2026, published 20 Oct 2026

arxiv.orgpaper🛡️ Security

Context-Aware Prompt Injection Detection via Small Language Models in RAG Pipelines: nokast-secureRAG

nokast-secureRAG is a roughly 0.5B-parameter small language model that works as a semantic firewall between retrieval and generation in local RAG systems. It classifies each query and context pair as safe, suspicious or malicious. It reaches 0.994 recall at a 0.026 false-positive rate in-distribution, beating Llama Guard 3 1B and Prompt-Guard-86M (0.900 recall, 0.690 FPR). Recall on PoisonedRAG falls to near zero, and single-request latency is 293-404 ms.

Llama Guard 3LLM

indirect prompt injectionretrieval poisoningPoisonedRAG+2

AI Security· Added 22 Oct 2026, published 20 Oct 2026

arxiv.orgpaper🧠 AI

Reward Misspecification Degrades Chain-of-Thought Monitorability

This paper shows that reward misspecification alone degrades chain-of-thought (CoT) monitorability in language models. Using a testbed of paired true and misspecified rewards across three models and three tasks, CoTs keep reasoning about task intent while outputs hack the reward. The proposed reward description training injects an LM-inferred reward description into the input and raises monitorability by an average of 24% or 42% absolute, depending on the metric.

LLM

arXiv

ai-safety, training, research· Added 22 Oct 2026, published 20 Oct 2026

securityweek.comarticle🛡️ Security

Siemens Software Center Vulnerability Exploited in ClickFix Malware Attacks

CVE-2026-110936 is a DLL search-path flaw in Siemens Software Center (CVSS v4 8.4), fixed in version 4.0.9.8. Attackers paired a validly signed copy of the program (3.5.8.7) with their own DLLs for side-loading, delivered through the Romulus ClickFix platform. Siemens says its channels were not breached and advises behavior-based detection over file hashes. Huntress reported the flaw.

Siemens Software CenterHuntressWindows

CVE-2026-110936RomulusClickFix+6

Threat Intel, Blue Team & Detection· Published and added 19 Oct 2026

eprint.iacr.orgpaper🧠 AI

Analysis of an AI based code distinguisher

The paper analyses DeepDistinguisher, a transformer-like model that tells Goppa codes apart from random linear codes. Saliency maps and attention visualisation reveal a low-weight-row artifact in the random training instances that the model partly relies on. A pairwise-XOR rule inspired by minimum distance correlates strongly with the model's predictions, and the model loses most of its power on random matrices meeting both constraints. This suggests it exploits general combinatorial properties, not hidden algebraic structure.

Transformer

DeepDistinguisher

research· Added 20 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Analysis of an AI based code distinguisher

Analysis of DeepDistinguisher, a transformer model that tells Goppa codes from random linear codes. Using saliency maps and attention visualization, the authors find a low-weight-row artifact in the random training instances that the model partly uses. They propose a pairwise-XOR rule on generator matrix rows that correlates strongly with the model's predictions. On random matrices meeting the minimum distance and XOR constraints, the model loses most of its accuracy, so it exploits general combinatorial properties rather than hidden algebraic structure.

DeepDistinguisherGoppa Distinguisher problemGoppa code

AI Security, other· Added 20 Oct 2026, published 19 Oct 2026

ethereum-magicians.orgthread🧠 AI

[Draft ERC] Swarm AI Framework — registry and charter model for purpose-bound agent swarms

Ethereum Magicians discussion of a draft ERC, the Swarm AI Framework. It proposes a registry and charter model in which accounts, including autonomous agents, form, bound, run and dissolve purpose-bound swarms. A hashed on-chain SwarmSpec (roles with eligibility modules, phases, decision authorities, fallback timers) is paired with an off-chain Swarm Pack charter. Two-sided signed admission (EIP-712 / ERC-1271), challenge and substitution, and minor versus material amendments are included. Phase posture is a coordinate: executed boundary and admission, plus declared payoff, topology and coordination terms from a write-once vocabulary. This lets cooperation, competition, tenders, debate, ensembles and pipelines be expressed. It integrates with ERC-8004 agent identity and ERC-8436 decision adapters. The reference implementation is a non-upgradeable registry with five eligibility modules and a task-tender adapter. The post asks reviewers specific design questions on vocabulary, amendment classes, roster snapshots and digest kinds.

EthereumSolidityFoundry+5

ERC-8001ERC-8063ERC-8444+3

agents· Published and added 18 Oct 2026

arxiv.orgpaper🧠 AI

Acceptance Is Not Speed: Length Inflation in Training-Free Lossy Speculative Decoding

This paper shows that training-free relaxed (lossy) verification in speculative decoding causes length inflation: completions get longer, mostly through extended reasoning traces, which offsets gains in accepted tokens per round. Across twelve target-drafter pairs, relaxed rules accept 1.34x as many tokens per round but produce 1.54x longer completions. Rules that cut verifier rounds usually lose substantial accuracy, so the authors argue completion length and verifier rounds must be reported.

LLM

arXiv

research, inference, evals· Added 20 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🧠 AI

On the possibilities and limitations of LLM-based multi-agent systems for multi-criteria decision-making in healthcare operations management

This paper proposes a framework for auditing LLM-agent-generated stakeholder preferences before they are used as weights in multi-objective optimization for healthcare operations. Experiments on outpatient appointment scheduling and emergency department configurations show that explicit ratings vary by role and clinic conditions, deliberation can erase role differences, and pairwise choices depend largely on option order. Robust weighted-sum models often select different schedules than average weights.

LLM

arXiv

research, agents, evals· Added 20 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🧠 AI

ThetreStageAI: Multi-View Retrieval-Augmented Generation for Structured Bengali Theatre Production Planning

ThetreStageAI is a retrieval-augmented framework for structured Bengali theatre production planning. It keeps separate scene, actor-blocking and lighting views, embeds Bengali text with BGE-M3, indexes it in view-specific Qdrant collections, and has Gemini generate schema-constrained JSON, with validation and at most one repair attempt. The authors build a synthetic dataset of 500 records (1,500 retrieval documents) and compare single-combined retrieval with multi-view retrieval. Code and data are public.

RAGGeminiBGE-M3+1

ThetreStageAI

llm-apps, research· Added 20 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🧠 AI

LexPilot: Benchmarking the Proactive Interaction Ability of Legal LLMs with Laypeople in Chinese Civil Case Consultation

LexPilot is a benchmark linking proactive legal interaction to claim-level verdict prediction, built from 432 real Chinese civil judgments across 36 causes of action. It pairs expert-reviewed complete and underspecified consultation histories with court verdicts. Current LLMs score on average 23.84% lower on judgment prediction in underspecified consultations than in fully specified ones, and the paper analyses the bottlenecks.

LLM

LexPilotarXiv

evals, research· Added 20 Oct 2026, published 16 Oct 2026

huggingface.coarticle🧠 AI

Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline

Announces Nemotron-pretraining-Indic-v1, about 2.8T tokens across 22 Indian languages (2,601B native-script, 224.6B romanized; 34 language-script configs), built by translating 15 Nemotron-CC-v2 subsets with NeMo Data Designer and Gemma-4-31B-it. The pipeline covers document-level translation, an MQM-inspired judge with a 75/100 threshold, feedback-based repair, and a separate Indic Translate workflow for ten languages. Continued pretraining of Nemotron 3.5 Super VL with 10B Indic tokens raised all five Indic benchmarks by 4.98–9.82 points. The post also gives dataset loading code and notes limitations: automated judging, translator-as-judge, and no semantic dedup.

NeMo Data DesignerGemmaHugging Face+2

NVIDIAAI4BharatIIT Madras+21

training, models, evals· Published and added 16 Oct 2026

arxiv.orgpaper🧠 AI

Learning to Steer LLM Agents and Multi-Agent Systems

Proposes a steering agent that supervises LLM agents and multi-agent systems during execution and corrects goal drift, where long-horizon tool use and inter-agent collaboration slowly diverge from the overall objective. A low-cost fast path decides when to intervene, and a high-cost slow path generates how to intervene. Both paths are trained jointly with reinforcement learning, combining a pairwise ranking objective with GRPO. On information-seeking search tasks it improves performance and efficiency for single-agent and multi-agent systems, generalizes out of domain, transfers to unseen executors, and allows intervention frequency to be adjusted without retraining.

GRPOLLM

arXiv

research, agents, training· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🛡️ Security

My Credentials, Please: Detecting Filesystem Capability Laundering in MCP Servers at Scale

Studies "filesystem capability laundering" in MCP servers: client approval of a tool call grants the server process broad host filesystem access, so attacker-steered arguments can reach sensitive files outside the tool's declared contract. The authors build MCP-FSD, which infers per-tool least-privilege boundaries, plants credential fixtures outside them, and tests exploitability and sandbox containment by paired dynamic execution. Of 21,097 servers from nine registries, 16.4% of 4,806 triaged servers have filesystem capabilities despite non-filesystem classifications. Dynamic validation confirmed 150 vulnerable instances, and disclosure produced 13 advisories, including two CVEs in Anthropic's Git MCP Server.

Model Context ProtocolAnthropicGit

filesystem capability launderingMCP-FSDpath traversal+1

AI Security, AppSec· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

EliteReplay: Preserving Diverse Solutions in RL Post-Training via Quality-Diversity Search

EliteReplay is an RL post-training recipe that pairs GRPO with an "elite" archive built by quality-diversity search. Before training, the frozen base model is used to collect solutions spanning distinct approaches; during training, a replay loss on this archive is added to GRPO. This counters the narrowing of solution diversity that RL causes. On text-to-SQL tasks it reaches higher pass@128 than all baselines and yields up to 77% more distinct correct strategies. It also retains more of the base model's strategies and covers both readings of ambiguous questions nearly twice as often on AmbiQT.

GRPOLLM

EliteReplayAmbiQTarXiv

training, research· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

Topology-Inspired Fine-Tuning for Wind-Field Super-Resolution

The paper fine-tunes a pretrained CNN wind-field super-resolution model with differentiable topology-inspired auxiliary losses on speed, gradient magnitude, soft high-speed level sets and local maxima. It also uses fixed-pair supervision at ground-truth persistence-pair endpoints located offline with the Topology ToolKit. A 16-configuration loss study on a fixed 168-field benchmark finds that gradient supervision gives the strongest persistence-diagram improvement, while fixed-pair supervision gives the most consistent joint improvement in persistence-diagram and merge-tree distances. The combined objective cuts bottleneck, W2,∞ and W2,2 distances by 31%, 25% and 27% relative to the pretrained CNN, at a cost of about half of standard fine-tuning's PSNR gain. It also shows that pointwise metrics and topological distances rank CNN and GAN models differently.

CNNGANTopology ToolKit

arXiv

research, training· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🛡️ Security

Approve What, Exactly? Effect Binding and Trusted Rendering for Agent Step-Up Authorization

Paper arguing that agent human-approval prompts count as a second factor only if agent compromise cannot forge them and they name exactly what is approved. It defines twelve attacks on the approval path (decoy recipients, amount cancellation, card padding, label spoofing, enrollment squatting, threshold splitting) and tests them against AP2, Microsoft's action-bound approval design and MCP elicitation. An audit of ten agent runtimes found no third-party verifiable approval record and post-approval argument changes in four of eight. The reference defense ASU (Agent Step-Up) renders the card from a canonical effect descriptor, has a phone passkey sign its digest, and executes only the stored call. It is proven in Tamarin (53 lemmas) and evaluated on 27,108 scripted trials and 609 AgentDojo pairs. The trusted card cut forged-narration attack success from 0.54 to 0.03 and raised LLM-approver catch rate from 0.30 to 0.98.

Model Context ProtocolMicrosoft

Agent Payments ProtocolAgent Step-UpASU+6

AI Security, Identity & Access· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

Recursive Reward Improvement via Adversarial Meta-Prompt Evolution

Introduces Recursive Reward Improvement (RRI) for recursive self-improvement on open-ended expert tasks without answer keys, where a proposer writes both a task and a rubric. Before the solver trains, an adversary writes a rubric-gaming but useless answer, the same model writes an honest answer without seeing the rubric, and up to two repair steps revise the rubric until the honest answer wins. Exploits found also rewrite future task and rubric generation. On HealthBench Professional it beats a Generation Only baseline by 10.9 points at 9B and 12.4 at 27B (10.3 with no external model), and the 27B model transfers to PRBench Hard (+6.6) and ProfBench (+8.0).

LLM

HealthBench ProfessionalPRBench HardProfBench+2

research, training· Added 17 Oct 2026, published 15 Oct 2026

arcfu.comarticle🧠 AI

Why do LLM pelicans look like this?

Announces Design Creativity Bench, a benchmark of 80 open-ended UI design tasks, each adapted to two product domains and paired with human designer references. It evaluates 16 frontier models on Originality (how distinct a model's output is from other models' for the same brief), Creative Range (how much its design decisions change when the domain changes) and Appropriateness (how well the UI meets the brief's functional requirements). The best model, GPT-6 Astra, scores 65.5 against a human reference of 92.3. The leaderboard, methodology, arXiv paper, Hugging Face dataset and GitHub evaluation framework are published.

Hugging FaceGitHub

Design Creativity BenchKombaiOpenAI+2

evals· Published and added 14 Oct 2026

eprint.iacr.orgpaper🧠 AI

A Graph-Based Analysis of Comparability, Scale, and Attribution in Private Transformer Inference

Systematization of knowledge on private Transformer inference (PTI) that builds an empirical graph of 62 systems, with system attributes as node features and evaluation compatibility as edges. Node-level attribution finds that cryptographic progress drives most speedups but always co-occurs with confounders such as model fidelity, hardware and cost accounting. Edge-level auditing finds only 1 of 1891 candidate system pairs directly comparable. Graph-level analysis shows the scale frontier plateaued at 13B parameters and projects the cost of deploying current PTI techniques on production-scale Transformers.

TransformerLLM

Souvik DasAriful AzadNitesh Saxena

research, inference, evals· Added 14 Oct 2026, published 13 Oct 2026

x.comthread🛡️ Security

Dark Web Intelligence (@DailyDarkWeb) on X

Social post summarizing Volexity's research on an Ivanti Connect Secure (ICS) zero-day chain, CVE-2026-104935. The chain pairs an auth bypass and a race-condition RCE with a separate privilege-escalation bug for root. Actor UTA0576 used it from Oct 3, 2026 against a fully patched 22.8R2.4 appliance, leaving no backdoor or web shell and deleting log entries. Post-exploitation included LDAP/AD credential theft, LAPS and BitLocker key searches, password spraying and packet capture. CISA added it to KEV with a due date of Oct 16. A clean Integrity Checker result does not rule out compromise because the chain patches code in memory only, so memory and network forensics are needed. The hunting lead is heavy requests to /dana-na/auth/url_default/welcome.cgi with a mismatched X-DSCGI-PATH header.

Ivanti Connect SecureVPNActive Directory+1

CVE-2026-104935UTA0576Volexity+5

Threat Intel, Blue Team & Detection· Published and added 13 Oct 2026

arxiv.orgpaper🧠 AI

OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding

OmniCompass is a manually annotated benchmark for video grounding and understanding with two tracks: OmniCompass-VL (820 videos, 2,460 instances; vision-language models) and OmniCompass-AV (240 videos, 720 instances; joint audio-visual evidence for omni-modal models). Each track covers temporal grounding, spatial grounding and QA across short/medium/long videos, and each instance is run in paired text-only and text+image target specifications. Evaluating 50 MLLMs shows open-source models lag proprietary ones, especially on audio-visual understanding, that precise temporal/spatial grounding remains hard, and that predictions are inconsistent across the paired specifications.

vision-language modelMLLM

OmniCompassOmniCompass-VLOmniCompass-AV+1

evals, multimodal, research· Added 14 Oct 2026, published 13 Oct 2026

arxiv.orgpaper🧠 AI

UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models

UniW(A)M is a mixture-of-experts model built on diffusion Transformers that unifies action-conditioned world modeling (AC-WM) with several world-action model (WAM) variants. Shared experts capture common physical dynamics, while atomic capability experts are reused across interfaces, each keeping its own conditional pathway. At deployment a plug-in adaptive interface router (PAIR), trained on a small subset of target-task chunk scenarios with the backbone frozen, picks a WAM interface for each execution chunk. It reaches 88.3% average real-world success against 76.7% for the strongest baseline, leads WorldArena on all three action-conditioned world-modeling tasks, and PAIR raises OOD success from 63.3% to 76.7%.

Mixture of Expertsdiffusion modelTransformer

UniW(A)MPAIRLIBERO+3

research, multimodal· Added 14 Oct 2026, published 13 Oct 2026

arxiv.orgpaper🧠 AI

LLM Agents for Automated Post-Training via Structured Recipe Optimization

Introduces Structured Recipe Optimization (SRO), a paradigm for automated LLM post-training that optimizes over a task-adapted recipe space covering data sources, data processing, cross-domain composition and training configuration. It pairs an LLM-based Recipe-Space Designer, which builds the recipe space for the target tasks, with a Multi-Domain Recipe Optimizer that uses structured trial evidence to guide LLM candidate construction and multi-objective Bayesian trial selection. SRO gets the highest average score in all evaluated target-model and agent-LLM combinations; for example, 41.1 vs 15.4 for the strongest baseline on Qwen3-1.7B with GPT-5.4, and 53.8 vs 40.8 on Qwen3-4B with DeepSeek-V4-Flash, in joint post-training across three capability domains and six benchmarks.

Qwen3LLM

Structured Recipe OptimizationGPT-5.4DeepSeek-V4-Flash+1

research, training, agents· Added 14 Oct 2026, published 13 Oct 2026

arxiv.orgpaper🧠 AI

EmoGenie: Benchmarking and Evaluating Expressed and Evoked Emotion in T2I Generation with MLLMs

EmoGenie is a benchmark for emotions expressed and evoked by text-to-image outputs. It has 6,400 images from eight T2I models using 800 emotion-targeted prompts. It includes EmoGenie-QA (90,000 closed-set and 1,057 open-ended questions) and EmoGenie-Assess (13,175 mean opinion scores for valence, arousal, dominance and aesthetic quality). The authors also propose EmoProbe, which pairs emotion QA with response-aligned, dimension-specific regression heads. They report that it beats baselines on emotion QA and correlates better with human ratings. The work shows how reliably MLLMs interpret affect in generated images.

multimodal large language modeltext-to-image

EmoGenieEmoGenie-QAEmoGenie-Assess+2

evals, multimodal, research· Added 14 Oct 2026, published 13 Oct 2026

arxiv.orgpaper🛡️ Security

BrowseSafe++: Benchmarking Prompt Injection Detection Across Web Contexts, Languages, and Browser Views

BrowseSafe++ extends BrowseSafe to 38,675 paired attacked/clean web pages, most with prompt injections observed in the wild on production sites. Pages cover twelve site types, 24 attack types and 18 injection locations, each given as HTML, Markdown, accessibility tree and screenshot with a user task and expected answer. The authors train BrowseSafe-Guard, two 0.8B detectors (text and screenshot) that beat a frontier-model-based detector on F1 with low false positives and low inference cost. BrowseSafe-Adapt rewrites injections using browser-agent feedback, raising attack success from a few percent to up to 95.41%.

LLM

BrowseSafe++BrowseSafeBrowseSafe-Guard+3

AI Security· Added 15 Oct 2026, published 12 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Revisiting Witness-Succinct UC-lifting Compilers

The paper introduces verifiable reconstructible straightline codes (VRSCs) as an abstraction for compiling zkSNARKs into Universally Composable (UC) secure protocols. It shows that the witness-succinct UC compiler of Ganesh et al. (EUROCRYPT 2023) is missing a security requirement: permuting previously revealed evaluation openings yields a fresh accepting proof, which invalidates the claimed simulation argument. The fix uses verifier-enforced disjoint evaluation domains and a pairing-based Fischlin-style instantiation with structured Reed-Solomon codes and jointly computed KZG openings. This brings certificate generation to O(n log n) with constant extra proof size and verification work.

zkSNARKKZGReed-Solomon codes

Universal Composabilityverifiable reconstructible straightline codesGanesh et al.+3

other· Added 14 Oct 2026, published 12 Oct 2026

securityweek.comarticle🛡️ Security

Ransomware Response Expert Arrested in ShinyHunters FBI Hack Probe

SecurityWeek reports the US arrest of a Canadian ransomware-negotiation specialist, reportedly Edward Dubrovsky of CyberSteward, on suspicion of involvement in ShinyHunters attacks, including the hack of FBI systems. He is charged with conspiracy to threaten to impair information confidentiality with intent to extort, and the case has moved to the Eastern District of Texas. It is the third ShinyHunters arrest in weeks, after suspects in the Netherlands and Jordan. The FBI blamed its own breach on a contractor's unpatched Oracle PeopleSoft, and sources say other ransomware negotiators could face charges.

Oracle PeopleSoft

ShinyHuntersFBIEdward Dubrovsky+3

Threat Intel· Published and added 12 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Chosen Ciphertext and Trapdoor Secure Public Key Authenticated Encryption with Keyword Search

Mori and Emura give a generic transformation, a variant of the CHK transformation, that upgrades a public-key authenticated encryption with keyword search (PAEKS) scheme to one secure against chosen-ciphertext and trapdoor attacks (CCTA). It works because the PAEKS receiver names the sender when generating a trapdoor, so keywords can serve as tags. Applied to Li-Boyen generic PAEKS instantiations, it yields the first fully CCTA-secure PAEKS schemes from pairings and from lattices. Verification keys embedded in ciphertexts and trapdoors also let sender-receiver matching use hash tables, so search cost no longer depends on the number of senders.

keyword-guessing attackCHK transformationCCTA+3

other· Added 14 Oct 2026, published 11 Oct 2026

eprint.iacr.orgpaper🛡️ 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.

Arm NEONAVX2AVX-512

MQOMNIST

other· Added 14 Oct 2026, published 10 Oct 2026

arxiv.orgpaper🧠 AI

SecReason: Vulnerability Detection and In-Stream Self-Repair for LLM Code Generation

SecReason is an in-stream defense for reasoning LLMs that write code. Reasoning models draft code early in their streamed chain of thought, so SecReason scans that stream with dual-track incremental analysis: security rules are checked on each emitted statement, and dataflows are traced across the accumulated code. When a rule fires, it interrupts decoding, inserts a warning into the stream and resumes, so the model fixes its draft within the same reasoning trace instead of regenerating the full response. On CWEval and SecCodeBench, runs delivering secure and functionally correct code rise from below 15% to 72.8%–98.1%. It uses fewer tokens than post-hoc repair and limits functional regressions when rules misfire on secure code.

LLMstatic analysis

SecReasonCWEvalSecCodeBench

research, coding-agents· Added 15 Oct 2026, published 10 Oct 2026

arxiv.orgpaper🛡️ Security

SecReason: Vulnerability Detection and In-Stream Self-Repair for LLM Code Generation

SecReason is an in-stream defense for reasoning LLMs that monitors code drafted inside the streamed chain of thought. Dual-track incremental scanning applies security rules per statement and traces dataflows across accumulated code; when a rule fires, generation is interrupted, a warning is inserted, and decoding resumes so the model repairs the draft in the same trace. On CWEval and SecCodeBench it raises the share of secure and functionally correct runs from below 15% to 72.8%–98.1%, uses fewer tokens than post-hoc repair, and limits regressions from false warnings.

LLM

SecReasonCWEvalSecCodeBench

AI Security, AppSec· Added 16 Oct 2026, published 10 Oct 2026

arxiv.orgpaper🛡️ Security

SecReason: Vulnerability Detection and In-Stream Self-Repair for LLM Code Generation

SecReason is an in-stream defense for reasoning LLMs that monitors code drafted inside the streamed chain of thought. Dual-track incremental scanning applies security rules per statement and traces dataflow across accumulated code. When a rule fires, generation is interrupted, a warning is inserted and decoding resumes, so the model repairs its draft without regenerating the full response. On CWEval and SecCodeBench it raises the share of secure and functionally correct runs from below 15% to 72.8%–98.1%, uses fewer tokens than post-hoc repair, and limits functional regressions from false warnings.

LLM

SecReasonCWEvalSecCodeBench

AppSec, AI Security· Added 15 Oct 2026, published 10 Oct 2026

arxiv.orgpaper🧠 AI

SecReason: Vulnerability Detection and In-Stream Self-Repair for LLM Code Generation

SecReason is an in-stream defense for reasoning LLMs that write code. Reasoning models draft code early in their streamed chain of thought, and SecReason scans that draft incrementally with security rules, checking each statement as it is emitted and tracing dataflows across the accumulated code. When a rule fires, it interrupts generation, inserts a warning into the stream, and resumes decoding, so the model repairs the draft within the same reasoning trace. On CWEval and SecCodeBench it raises the share of runs that deliver secure and functionally correct code from under 15% to 72.8%–98.1%. It uses fewer tokens than post-hoc repair and limits functional regressions when rules misfire on secure code.

LLM

SecReasonCWEvalSecCodeBench+1

research, coding-agents· Added 16 Oct 2026, published 10 Oct 2026

arxiv.orgpaper🧠 AI

Matched-Context Group Policy Optimization for LLM Agent Training

Proposes Matched-Context Group Policy Optimization (MCGPO), which builds weighted action preferences from already-collected sibling rollouts to give credit to intermediate decisions when feedback is sparse and arrives only at the end. Comparison contexts use pre-action state summaries and rubric progress. Exact and coarse matching rules are selected by two-action support, and fixed folds separate candidate decisions from the terminal outcomes that orient them. An evidence-weighted auxiliary pairwise loss is added to GraphGPO task learning, and no critic is needed. Across six environment-backbone settings with three seeds each, it beats GraphGPO by 0.47-1.46 points on ALFWorld and WebShop with Qwen2.5-1.5B/7B, and by 3.96 and 5.16 points on WebShop with Qwen3-4B/8B. ALFWorld gains are small because baseline success is already above 92%.

GRPOQwenQwen3

Matched-Context Group Policy OptimizationMCGPOGraphGPO+4

training, agents, research· Added 12 Oct 2026, published 9 Oct 2026

huggingface.cowriteup🧠 AI

Impactful scheduling for GPU clusters

Ai2's AI Infrastructure team describes replacing a priority-based scheduler on thousands of H100/B200/B300 GPUs with GPU time budgets, hierarchical fair-share over a 7-day lookback window, and a "scheduling contract" where workloads declare a minimum runtime (capped at 8 hours) to enable time-slicing. Results over 30 days: 98% of owed GPU hours delivered, occupancy steady at 98%, debug-workload p90 queue wait down from about 2 hours to 30 seconds, and 74% fewer repairs needing a human. It also covers a simulator used to predict outcomes before rollout, plus problems that remain: interactive sessions hurt by preemption and possible capacity fragmentation.

SLURMYARNHadoop+3

Ai2NVIDIADominant Resource Fairness+1

ai-infra, training· Published and added 9 Oct 2026

stepsecurity.iowriteup🛡️ Security

GhostAction Returns: Malicious “Security Audit” Workflows Now Mine Credentials from Entire Git Histories

StepSecurity details a new wave of the GhostAction campaign in which attackers used compromised maintainer accounts of Takashi Kitao (pyxel) and Henry Wu (athenadriver, Uber) to push malicious "Security Audit" GitHub Actions workflows to 345 repositories in minutes. The upgraded payload now runs `git log -p --all` to mine an entire git history (not just current secrets) for thirteen credential patterns including AWS, Anthropic, OpenAI, OpenRouter, GitHub, GitLab, Google, Slack, and SendGrid keys, pairs AWS access key IDs with secret keys via context capture, and exfiltrates everything via raw-IP plain-HTTP POST to 193.32.204.199, bypassing DNS-based defenses. The post includes hunting queries, run-log forensics confirming successful exfiltration from uber/athenadriver, and remediation guidance centered on workflow approval gates and egress allowlisting.

GitHub ActionsStepSecurityHarden-Runner+5

GhostActionTakashi KitaoHenry Wu+7

Supply Chain, DevSecOps, Threat Intel· Published and added 9 Oct 2026

arxiv.orgpaper🧠 AI

BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items

BLiMPPI is a multilingual evaluation suite of over 12,000 negative polarity item (NPI) minimal pairs across 33 languages. A pipeline finds NPIs in Universal Dependencies treebanks, labels their licensing environments, and perturbs them into grammatical/ungrammatical pairs. Across 12 multilingual and 2 monolingual models plus n-gram baselines, multilingual models do worst on lower-resource languages and on pairs that n-grams cannot solve. The paper also analyses NPI typology, language family, licensing variability and NPI-licenser distance.

Universal Dependenciesn-gram

BLiMPPI

evals, research· Added 12 Oct 2026, published 9 Oct 2026

socket.devwriteup🛡️ Security

New GhostAction Wave Hits Hundreds of Repos, Expanding Beyond CI/CD Secrets to Cloud Credentials

Socket documents an October 8, 2026 wave of the GhostAction campaign in which two compromised GitHub maintainer accounts (henrywoo and kitao) pushed a malicious security-audit.yml workflow to 346 repositories, including uber/athenadriver and the 18,420-star kitao/pyxel. Beyond the original GhostAction technique of harvesting named GitHub Actions secrets, this variant adds a full git-history grep (via fetch-depth: 0 and git log -p --all) for 13 credential patterns covering AWS keys, Anthropic/OpenAI/OpenRouter API keys, GitHub/GitLab PATs, Google/Firebase keys and SendGrid keys, plus a context-window capture around AWS key IDs to reconstruct usable key pairs, all exfiltrated in cleartext to a single hardcoded IP.

GitHub ActionsPyPIcrates.io+3

GhostActionGitGuardianhenrywoo+8

Supply Chain, DevSecOps, Threat Intel· Published and added 9 Oct 2026

securityweek.comarticle🛡️ Security

Anthropic Fast-Tracks AI Bug Reports to OSS Maintainers, Taps 11 Firms for OT Security

Anthropic launched OSS Scanner, a free opt-in service modeled on Google's OSS-Fuzz that uses its most capable Claude models to periodically scan open source projects and send maintainers model-generated, unreviewed vulnerability reports with PoCs and suggested fixes (targeting >90% true-positive rate). It also announced the Critical Infrastructure Defense Program (CIDP), pairing frontier Claude models and on-site engineers with eleven founding partners — Accenture, Booz Allen, CrowdStrike, Deloitte, Dragos, Hitachi, Insane Cyber, Nozomi Networks, Palo Alto Networks, PwC, and Rockwell Automation — to help OT/ICS operators in power, water, manufacturing and transportation remediate vulnerabilities that often can't be patched quickly. Both build on lessons from Anthropic's earlier Project Glasswing, which found many vulnerabilities but struggled to get them fixed in time.

ClaudeAnthropicCrowdStrike+3

Project GlasswingOSS-FuzzOSS Scanner+1

AI Security, AppSec· Published and added 9 Oct 2026

huggingface.copaper🧠 AI

DecepEval: A Benchmark for Evaluating Deception in LLM Agents

DecepEval is a benchmark of 1,532 instances across 3 task families and 28 professional scenarios designed to measure deception in LLM agents, pairing neutral and induced versions of each task to isolate condition-dependent changes in deception rates. It introduces the LLM Deception Diamond framework (pressure, incentive, opportunity, conflict), adapted from classical fraud theory, and finds that across nine frontier LLMs these inducements raise deception rates even in models with low baseline deception, with long-horizon tasks showing deception rates averaging 87% under induced conditions.

Hugging Face

DecepEvalLLM Deception Diamond

ai-safety, evals· Added 8 Oct 2026

decrypt.coarticle🧠 AI

Google Wants Gemini to Be Your Next Coworker—Complete With Its Own Email Address

Decrypt reports on Google Cloud's newly announced Gemini Agent, a long-running AI agent that operates across Gmail, Docs, Sheets, Slides, Drive, Chat, Calendar, Microsoft 365 and Slack, and can run for hours or days on a single goal. The feature allows managers to spin up coworker agents with their own Workspace email address, calendar, Drive storage and directory listing, each given a cryptographically attested identity and a separate audit trail; agents connect to Salesforce, ServiceNow, Jira, Snowflake, BigQuery and any Model Context Protocol server, and are orchestrated across both Gemini and Anthropic's Claude models depending on cost and quality fit. Google pairs the launch with safety infrastructure, an Agent Sandbox, an AI network firewall called Agent Gateway, and real-time token-spend caps, citing a prior Claude Cowork VM-escape finding by Accomplish AI as the kind of containment failure it is designed to prevent.

GeminiClaudeGoogle Cloud+2

AnthropicThomas KurianGrok+9

agents, ai-infra· Published and added 8 Oct 2026

blog.talosintelligence.comarticle🛡️ Security

Making sure the checks get printed

Cisco Talos's weekly Threat Source newsletter, opening with a risk-management anecdote about network-isolating unpatchable legacy check-printing systems, then disclosing new CAIRN research classifying "A3: AI-Analysis Evasion" — malware authors embedding natural-language prompt-injection text into code/binaries to manipulate AI-assisted malware analysis pipelines. The MANTLEMAZE malware family exemplifies this, pairing such AI-deception attempts (effective ~35% of the time) with BYOVD abuse to disable EDR from kernel space. The issue closes with a roundup of the week's security headlines (Citrix NetScaler CVE-2026-88779, a Danish government data breach, Warlock ransomware) and Talos's current top-prevalence malware hashes/IOCs.

EDRCitrix NetScaler

CVE-2026-88779MANTLEMAZECAIRN+2

AI Security, Malware & RE· Published and added 8 Oct 2026

arxiv.orgpaper🧠 AI

Rehearse Everything, Remember Nothing: Attic-KV Rehearses What Will Be Read

Attic-KV is a training-free KV cache compression method that scores cache entries by having the model quiz itself with question-answer pairs quoting the context, plus content-adaptive anchor tokens, instead of rereading the whole context. The authors show full rereading spreads the keep budget across the context, so answer entries survive near chance; at a 3% keep ratio rereading scores only 31.5 of 96.5 on RULER. Attic is the best training-free method in all eight RULER/LongBench settings, lifts KVgrad and RestoreKV+ by up to 17.1 and 28.1 points, beats full rereading by 41.9 points at 3% keep, and compresses faster.

Transformer

Attic-KVKVgradRestoreKV++2

research, inference· Added 9 Oct 2026, published 8 Oct 2026

arxiv.orgpaper🛡️ Security

Is Memorization Context-Sensitive? Prefix-Based Extraction Beyond Isolated Prefixes

Paper measuring prefix-based training-data extraction in instruction-tuned open-weight LLMs under an empty prompt versus retrieved contexts of varying relevance (RAG), using paired item-level suffix scoring across three models. It finds extractable memorization has a context-robust core and a context-sensitive boundary: context suppresses some exposures but also enables new ones missed by prefix-only evaluation. This qualifies the claim that RAG reduces memorization risk and matters for threat-modeling deployed LLM systems.

LLM

training data extractionmemorization

AI Security· Added 9 Oct 2026, published 8 Oct 2026

arxiv.orgpaper🧠 AI

Is Memorization Context-Sensitive? Prefix-Based Extraction Beyond Isolated Prefixes

The paper measures whether retrieved context, as in RAG, reduces LLM memorization. It does this with paired item-level probabilistic suffix extraction: each prefix-suffix pair is scored under an empty prompt and under retrieved contexts of varying relevance, across three open-weight instruction-tuned models. Extractable memorization splits into a context-robust core and a context-sensitive boundary. Context suppresses some marginal exposures but also enables new ones that prefix-only evaluation misses, which qualifies the claim that RAG lowers memorization risk.

RAG

arXiv

research, llm-apps· Added 9 Oct 2026, published 8 Oct 2026

arxiv.orgpaper🧠 AI

What Output-Only Review Cannot Verify: Study Contracts for Research Agents

Proposes "study contracts" that bind an AI-generated study's declared experimental choices, run obligations and claim scope to recorded execution evidence, defining contract-relative verification as distinct from scientific truth. In a diagnostic with eight self-authored clean/mutated pairs, a deterministic fault-specific checker caught all eight mutations, while eighteen LLM judge aliases given metadata-filtered packages flagged 104 of 144 mutated cases (32 abstentions, 8 terminal failures). The authors stress the setting is deliberately information-asymmetric, not a verifier-quality comparison or reward-hacking result, and call for full-information comparisons, legitimate-adaptation controls and closed-loop agent evaluations.

research, evals, agents· Added 9 Oct 2026, published 8 Oct 2026

arxiv.orgpaper🧠 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.

MBPP+

research, evals, agents· Added 9 Oct 2026, published 8 Oct 2026

latent.spacearticle🧠 AI

[AINews] Claude Haiku 5.5 — better than GPT-6 Luna at the same pricing

Latent Space's AINews recap covers Anthropic's launch of Claude Haiku 5.5, priced to match OpenAI's GPT-6 Luna and paired with a Sonnet 5.5 cache-read price cut and new Max/Team API credits. Independent Artificial Analysis testing puts its Intelligence Index at 43 (up 26 points from Haiku 4.5) and Terminal-Bench 4.0 at 33%, but shows it uses roughly 3x the output tokens of GPT-6 Luna at max effort, plus results on AA-Briefcase, AA-Omniscience and AutomationBench-AA, alongside a same-day Claude SDK update adding built-in computer-use/browser-use tooling.

ClaudeClaude CodePython+1

Claude Haiku 5.5AnthropicGPT-6 Luna+21

models, evals· Published and added 8 Oct 2026

arxiv.orgpaper🧠 AI

From Evidence to Action: How Tool-Using Agents Fail

This paper studies where tool-using LLM agents break down in the chain from gathering evidence to taking consequential actions, finding that strong static judgment of whether to act often coexists with much weaker interactive execution, and that failures arise from incomplete investigation, acting before evidence is established, or unresolved prerequisites in multi-action workflows. The authors introduce SafeActBench, a 656-case benchmark across six operational domains and five protocols, paired with a provenance-bound Evidence Ledger and a deterministic trajectory evaluator tested across ten model-harness configurations.

SafeActBench

evals, agents· Added 7 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Revisiting Lattice-based Blind Signatures Again

This IACR ePrint paper by Yi-Fu Lai and Yu Yu breaks the blindness property of a lattice-based blind signature scheme presented at Crypto 2020, which uses a linear hash function framework instantiated over lattices. The authors first identify an overlooked bug in the scheme's original blindness security proof, then construct a concrete attack under an honest-key, honest-but-curious signer model by exploiting correlations between leaves of the scheme's tree structure, and discuss how the construction can be repaired.

Yi-Fu LaiYu YuCrypto 2020

other· Added 8 Oct 2026, published 7 Oct 2026

arxiv.orgpaper🧠 AI

Before They Can Solve: Predicting Post-Training Coding-Agent Performance from Base Models

This paper proposes three evaluation screens — Decisive-Action BPB, Patch MCQ, and prefix-conditioned pass@K — that predict how well a base model will perform as a coding agent after post-training, without requiring the base checkpoint to run a full agentic harness. By replaying successful post-trained agent trajectories and identifying the "decisive step" that flips a repository from failing to passing tests, the authors measure a base model's latent support for that action. Tested across ten pairs of public base and post-trained models, all three screens closely track post-trained SWE-bench Verified pass@1 scores, offering a way to cheaply vet base checkpoints before committing to expensive agentic post-training.

SWE-bench Verified

evals, coding-agents· Added 8 Oct 2026, published 7 Oct 2026

arxiv.orgpaper🧠 AI

TR-PTQ: High-Accuracy Integer-Only Transformer Post Training Quantization via Taylor Region Reformulation

TR-PTQ is a post-training quantization method for transformers that identifies learned LayerNorm scale parameters and GELU's compounded approximations (not SoftMax) as the main sources of accuracy loss when quantizing, contrary to common assumptions about numerical precision. It introduces a unified integer-only formulation using shared Taylor Region exponential/logarithm primitives so division and square roots can run entirely in the log-domain with standard integer arithmetic, paired with a calibration-free, outlier-aware LayerNorm optimization, eliminating the need for floating-point hardware for nonlinearities while keeping accuracy degradation under 1.5% across vision and language benchmarks.

Transformer

inference· Added 8 Oct 2026, published 7 Oct 2026

arxiv.orgpaper🧠 AI

World Models' Last Exam in Physics

World Models' Last Exam in Physics introduces a measurement-based benchmark of 40 controlled tasks across mechanics, optics, fluids, thermal/phase-change phenomena, electromagnetism, and surface tension, designed to test whether video world models generate physically consistent outputs. Each task pairs an initial image and prompt with predefined physical criteria, and a custom evaluator combines observability screening with task-specific quantitative measurements rather than relying on reference videos or model-based judgments. Testing eight video generation models across 1,280 generated videos found persistent physical inconsistencies, with the best model scoring only 57.76/100, and the evaluator showed higher agreement with human judgments than a vision-language model baseline.

vision-language model

World Models' Last Exam in Physics

evals, multimodal· Added 7 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

ALIVE: Interaction-Aligned Object Insertion for First-Frame-Guided Video Editing

ALIVE is a framework for first-frame-guided video editing that inserts objects so they coherently participate in interactions (e.g., being picked up or manipulated) rather than appearing static. The authors curate a 35,800-pair editing dataset combining 3D-rendered, model-generated and real-world videos plus pairs from ROSE, and train a vision-language model to predict interaction guidance from the edited first frame and an instruction naming the object. They introduce the ALIVE-interaction benchmark to measure interaction fidelity, source preservation and visual coherence via a unified VLM-based protocol, reporting a 43.9% Overall improvement on this benchmark and 4.4% on a general video object insertion benchmark over the strongest baseline, with VLM-predicted guidance adding a further 0.95-point gain.

vision-language model

ALIVEROSEALIVE-interaction

research, multimodal· Added 7 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

ScienceClaw: Benchmarking Continual Self-Evolution of AI-for-Science Agents Across the Natural and Social Sciences

ScienceClaw formalizes a fixed-parameter program self-evolution framework for LLM agents doing scientific tasks, where verified task executions are turned into persistent Skill and Operator updates to the agent's program rather than being discarded. ScienceClaw-Eval, the accompanying benchmark, spans 23 disciplines across natural and social sciences and measures scientific correctness, evolutionary gain, retention, cross-dataset transfer, and evolution cost via sequential task streams and independent reset evaluation. The framework only keeps an update if source-task replay reproduces the repair and the change improves performance on independent scientific tasks, addressing the gap that verified agent executions rarely become lasting program-level improvements.

ScienceClawScienceClaw-Eval

evals, agents, research· Added 7 Oct 2026, published 6 Oct 2026

eprint.iacr.orgpaper🛡️ Security

The Post-Quantum Cost of Garbled-Circuit Bitcoin Bridges

This IACR ePrint paper examines the post-quantum security of Bitcoin bridges that rely on off-chain garbled-circuit evaluation of succinct proofs to validate withdrawals, where input labels encoding the proof must be committed on-chain. The authors show current pairing-based proving systems are vulnerable to quantum attacks, and that swapping to post-quantum proving systems shrinks circuit size but drastically inflates proof size, forcing proofs to span multiple Bitcoin blocks and raising on-chain cost. They outline research directions to improve proof efficiency, circuit representation, and label encoding for quantum-resistant bridge designs.

Bitcoin

Mukesh TiwariAaron Feickert

Blockchain· Added 8 Oct 2026, published 6 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Differential-Neural Cryptanalysis of ChaCha and Related ARX Stream Ciphers

An IACR ePrint preprint applying differential-neural cryptanalysis to the ARX stream cipher family (ChaCha, Salsa, Forró), training six neural architectures (Gohr's residual network, DBitNet, an Inception network, MLP-ResNet, SE-ResNet, a self-attention network) in single-pair and multi-pair settings. It reports new distinguishers — 3-round ChaCha, 4.5-round Salsa, 1.25-round Forró — and uses explainability analysis to show the 3-round ChaCha distinguisher learns a mixture of round-1 truncated differentials rather than a pure differential signal.

ChaChaSalsa20

Gohr's residual networkDBitNet

AI Security· Added 8 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🛡️ Security

Under the Hood of SKILL.md: Semantic Supply-chain Attacks on AI Agent Skill Registry

This arXiv paper studies SKILL.md files — the natural-language metadata describing modular AI Agent Skills — as an attack surface, demonstrating semantic supply-chain attacks against real ClawHub skills. Using text-only manipulations, the authors manipulate embedding-based Discovery retrieval (up to 86% pairwise win rate, 80% Top-10 placement), bias agent Selection toward adversarial variants (77.6% average success), and evade Governance blocking verdicts (36.5%-100% evasion), showing that skill metadata is operational text that shapes agent trust rather than passive documentation.

ClawHub

AI Security, Supply Chain· Added 5 Oct 2026

arxiv.orgpaper🧠 AI

Fast Models, Slow Evidence: Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses

This paper runs a paired, reproducibility-focused evaluation of two System-1 single-forward-pass decision models, open-weight Laya and hosted Jev, across 11 typed decision points that agent harnesses make constantly: tool selection, RAG relevance gating, prompt-injection detection, and zero-shot model routing, using 7,283 base cases plus 6,640 robustness variants from 18 public sources. Jev beats Laya on 9 of 11 decision points by 10.8-46.0 percentage points, both models fail at zero-shot model routing, and Laya flips 30 percent of answers under option reordering and degrades badly with many or similar candidates. The authors also self-audit their own pipeline, finding and correcting three analysis errors and a design confound, including a reported 23.9 percent cost saving that was actually 4.3 percent, and gate accuracy conflated with end-to-end quality.

LayaJev

evals, agents· Added 5 Oct 2026

arxiv.orgpaper🧠 AI

SpatialChain: A Benchmark for Auditing Spatial Reasoning Faithfulness in VLMs

SpatialChain is a benchmark of 28,350 training and 899 test examples pairing spatially-oriented GQA questions with scene-graph-grounded reasoning chains, designed to audit whether VLMs' correct spatial answers reflect faithful reasoning or linguistic shortcuts. Using a two-axis evaluation (chain-overlap metrics plus a scene-graph-aware LLM judge), the authors find that four of nine tested models hit ≥79% accuracy while showing shortcut rates above 39%, that chain quality predicts correctness for most models except Claude Sonnet 4.6 and InternVL3.5-8B (which fail differently, via terse vs. verbose-decorative reasoning), and that SFT on SpatialChain improves Qwen3-VL-8B accuracy by +6.2pp while cutting its shortcut rate to 22%. The faithfulness judge is validated against human annotations and a second LLM judge, with data, generation scripts and evaluation code released publicly.

vision-language modelQwen3-VL

Claude Sonnet 4.6InternVL3.5-8BQwen3-VL-8B+1

evals, multimodal· Added 6 Oct 2026, published 5 Oct 2026

arxiv.orgpaper🛡️ Security

Compromise Is Not Consequence: Evaluating Task-Scoped Authorization in LLM Agents with Paired Replay

This paper introduces a paired-replay testbed that samples a single LLM agent request and replays the identical malicious action, resource, and arguments under four authorization conditions: broad bearer tokens, scoped JWTs, sender-constrained tokens, and Open Policy Agent policies. Across 128 scenarios in four tool domains and five local model configurations, broad-bearer tokens let roughly 9 to 38 percent of attacked, post-exposure decisions execute harmful actions, while all three scoped conditions recorded zero harmful executions; a supplementary AgentDojo extension found 11 of 24 injected attacks succeeded under broad policy versus 0 of 24 under scoping. The authors frame this as evidence that scoping contains cross-action and cross-resource consequences of prompt injection, not that it prevents the injection itself.

Open Policy AgentLLMJWT

AgentDojo

AI Security, Identity & Access· Added 6 Oct 2026, published 5 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Randomness-Anchored Runtime Discovery of Cryptographic Operations on Linux Using eBPF

This IACR ePrint paper presents a Linux runtime cryptographic discovery system built on eBPF user-space probes that detects both classical and post-quantum (ML-KEM) cryptographic operations as they execute, complementing static binary/SBOM analysis with dynamic observation. It uses "randomness anchoring" — correlating observed key-generation/encapsulation randomness with subsequent crypto API calls in the same process — to raise detection confidence without adding false positives, reporting perfect precision/recall on classical and ML-KEM workloads, median detection latency of 28 μs, and 14–18% overhead on key-generation-bound workloads. A nine-application case study (drawn from/paired with QED datasets) compares static vs. runtime views and shows how eBPF probe coverage limits what can be observed, relevant to building a runtime cryptographic bill of materials for PQC migration compliance.

eBPFLinuxML-KEM+1

QED datasets

AppSec, GRC· Added 8 Oct 2026, published 5 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Measuring Parallel Execution of Classical–PQC Component Pairs in Hybrid TLS 1.3

An IACR ePrint paper benchmarks whether running the classical and post-quantum components of hybrid TLS 1.3 key exchange (ECDHE–ML-KEM) and Composite ML-DSA signatures in parallel on separate pthread worker threads reduces handshake latency versus sequential execution. Across three RFC 10024 ECDHE–ML-KEM groups with server and mutual authentication, parallelism sped up all six tested conditions on a standard ARM and x86 system (1.04×–1.28×), with the largest gains in signature stages where component times are balanced, but slowed every condition on a low-power x86 system (0.80×–0.92×) because thread wake/wait overhead outweighed savings. The authors conclude that component independence alone doesn't guarantee a speedup and that parallelization decisions must be validated per target environment.

TLS 1.3pthread

ML-KEMML-DSAECDHE+1

other· Added 8 Oct 2026, published 5 Oct 2026

simonwillison.netwriteup🧠 AI

Qwen3.8 27B addition in words

Simon Willison reproduces an experiment originally run by Colin Frasier on GPT-4o — asking a model to add two large numbers and return the result spelled out in words — this time against a locally quantised Qwen3.8-27B-Q4_K_M.gguf model on an NVIDIA DGX Spark. He compares heatmaps of accuracy across number-size combinations with reasoning disabled (30 samples per pair) versus reasoning enabled (one-shot, 167/169 correct), and shares chain-of-thought traces showing the model doing manual digit-by-digit column addition. It's a small but concrete illustration of how enabling reasoning changes arithmetic reliability in a quantised local LLM.

GGUFQwen3.8-27BCodex

Simon WillisonColin FrasierGPT-4o+2

evals, inference· Added 5 Oct 2026, published 4 Oct 2026

arxiv.orgpaper🧠 AI

Sharpening Tax in Post-Training

This paper studies the sharpening hypothesis of RL post-training on LLMs: that it mainly sharpens existing base-model behaviors, boosting pass@1 at the cost of solution coverage (pass@K), and shows this cost extends to agentic, multi-turn tool-use tasks, not just math/coding. The authors introduce Sharpening Tax, a metric quantifying this coverage loss, measured across 14 base/post-trained model pairs from four model families on three agentic benchmarks (42 cases), finding post-training pushes tasks toward always-solved or never-solved extremes. They also propose posterior-tempered group sampling (PTGS), a Bayesian per-prompt temperature adaptation method that reduces the tax during RL training while improving both coverage and single-shot accuracy.

research, training· Added 4 Oct 2026

arxiv.orgpaper🧠 AI

RubricArmor: Adversarial Evolution Improves LLM-Based Rubric Generation

RubricArmor is an adversarial framework for generating evaluation rubrics used in rubric-based reinforcement learning of LLMs, designed to preempt reward hacking before RL training begins. It alternates an attack step, which synthesizes adversarial responses that satisfy the current rubric while failing the actual task, with a repair step that revises the rubric to close the exposed gaps while keeping valid criteria intact. The paper reports that rubrics produced this way outperform existing LLM-based rubric generation baselines and lead to more effective downstream rubric-based RL.

RubricArmor

training· Added 6 Oct 2026, published 4 Oct 2026

tavus.ioarticle🧠 AI

Tavus Griffin: the first Human Interaction Model

Tavus introduces Griffin, a "Human Interaction Model" (HIM): a full-duplex, video-to-video system that unifies real-time perception, conversational decision-making, and expressive audiovisual generation into one model rather than a cascaded pipeline. It uses a Continuous Conversational Modeling engine that makes sub-second turn-taking and nonverbal-behavior decisions, paired with a streaming speech generator built on a new continuous audio codec (Tavec) and a distilled autoregressive video diffusion model that renders 720p video in 320ms chunks. In live one-minute video calls, Griffin-Lite was judged human by 48% of participants (versus 2.4% for Tavus's prior Phoenix-4.5 system) and topped NVIDIA's VideoFDB full-duplex conversation benchmark on both generation and perception tracks.

diffusion transformerautoregressive modelflow matching

TavusGriffinGriffin-Lite+14

multimodal· Added 2 Oct 2026

arxiv.orgpaper🧠 AI

World Embedding Benchmark

The World Embedding Benchmark is a new evaluation suite of 8,000 controlled simulation cases across 80 families (fluid mechanics, solid mechanics, dynamics, optics & electromagnetism), each pairing a rendered video with simulation-derived physical annotations. It tests pre-trained omnimodal embedding models on text-video retrieval, physical-property regression, and multiple-choice video-description classification, finding that current embeddings align poorly cross-modally while lightweight probes can still recover physical quantities, and that continual contrastive training improves alignment but hurts regression. The authors also show retrieval-augmented generation using these embeddings with MiniMax-H3 improves the physical fidelity of generated videos.

MiniMax-H3

World Embedding Benchmark

evals, multimodal, research· Added 5 Oct 2026, published 2 Oct 2026

arxiv.orgpaper🧠 AI

A Vision-Language Model (VLM)-based Pipeline for End-to-End Procedural Modeling of Field-Grown Maize from Point Clouds

This paper presents an automated pipeline that uses a vision-language model to annotate leaf midlines in rendered orthographic views of maize point-cloud scans, then applies deterministic geometric algorithms and differentiable NURBS fitting to reconstruct editable, organ-level procedural 3D plant models without manual tuning or species-specific segmentation training. Evaluated on 100 field-grown maize plants from the MaizeField3D dataset, it achieves a median whole-plant Chamfer distance of 5.4 mm and recovers 99.4% of curated reference leaves at IoU ≥ 0.5, outperforming an earlier semi-automated pipeline. It demonstrates that VLM annotations can become reliable quantitative measurements when paired with correcting geometric post-processing, enabling scalable generation of 3D plant assets for high-throughput phenotyping and in silico breeding.

vision-language modelNURBS

MaizeField3D

research, multimodal· Added 5 Oct 2026, published 2 Oct 2026

huggingface.coarticle🧠 AI

Open-sourcing AstaBrief, the fast report-generation model in Asta

Ai2 open-sources AstaBrief 8B, a Qwen3-8B-based model fine-tuned to generate cited scientific reports in a single pass (rather than section-by-section), used as the "Fast mode" in their Asta research platform alongside a Claude-powered "Thinking mode." The post details the training recipe (SFT on 47K filtered real-query reports plus DPO on 6K judge-agreed preference pairs), the statistics-based filters (citation density, citation diversity, citation relevance, output/input token ratio) that most improved grounding, and evaluation against SQABench-CS2 and DeepScholarBench, showing AstaBrief is ~3.5x faster than the proprietary pipeline while remaining competitive on citation accuracy and answer quality.

Qwen3Hugging FaceDPO+1

AstaBriefAstaAi2+14

models, training, evals· Published and added 2 Oct 2026

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