WorkFactory: Turning Professional Intents into Executable Work for Scalable Agent TrainingWorkFactory 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.
huggingface.copaperAdded 30 Oct 2026π§ AI
Benchmarking color judgment in multimodal LLMs with CIE-computable ground truthA 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.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ 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.
x.comarticleAdded 30 Oct 2026, published 29 Oct 2026π§ 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.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ 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.
arxiv.orgpaperAdded 31 Oct 2026, published 28 Oct 2026π§ 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 (ΞRΒ²=.128) but not for Sweden-specific SwedishFacts (p=.242).
arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026π§ 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.
arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026π§ 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.
arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026π§ 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.
arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026π§ 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.
arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026π§ AI 
Revisiting ZMAC+: Birthday-Bound Attacks and a Beyond-Birthday-Bound RepairZMAC+ 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)}.
eprint.iacr.orgpaperPublished and added 26 Oct 2026π‘οΈ Security
Test-time training in robotics shows no clear gains in demo responseThis 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.
en.cryptonomist.charticlePublished and added 26 Oct 2026π§ AI
A new AI method for saliency-aware image-text alignment improves image searchRoSA 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.
en.cryptonomist.charticlePublished and added 26 Oct 2026π§ AI- Cryptanalysis and Repair of "Decentralized Multi-authority ABE for NC$^1$ from BDH"
Cryptology ePrint 2026/2642 presents a plaintext-recovery attack on the DBDH and CBDH variants of the Datta, Komargodski and Waters multi-authority ABE for NC1 (Journal of Cryptology, 2023). It needs one corrupt authority and two users, and succeeds with probability one in the static model. The flaw is an invalid extension of the Rouselakis-Waters LSSS matrix-normalization lemma. The paper also attacks two Datta-Pal (PKC 2023) attribute-based inner-product schemes and proves repaired versions secure in the random oracle model.
eprint.iacr.orgpaperPublished and added 26 Oct 2026π‘οΈ Security - Detecting and Understanding Ambiguity in Visualizations with Role-Playing Vision Language Model Interpretations
This paper presents a pipeline that uses persona-conditioned vision language models to surface interpretive ambiguity in visualizations. Topic-relevant personas elicit candidate takeaways, and a Target-Pattern-Scope framework lets LLM judges label statement pairs as Compatible, Disjoint or Opposing, which are summarized in an ambiguity profile. The authors benchmark VLMs on chart comprehension per chart type, check labeling against human annotations, and run case studies on public visualizations and a redesign.
arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026π§ AI - Chosen Commitments and Certified Decoding Regions in the Adaptive Threshold Schnorr Attack
This IACR ePrint 2026/2632 paper by DemirtaΕ and Yayla analyses the CritesβStewart adaptive forgery attack on threshold Schnorr signatures, which needs one call to a sparse linear search oracle (bounded distance decoding of a scaled ReedβSolomon 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, Ο) pairs, M stays below 2^24, so the bound is under 2^-100 after 2^128 challenges at p = 2^252.
eprint.iacr.orgpaperAdded 26 Oct 2026, published 24 Oct 2026π‘οΈ Security - Intervention-Certified Amplification: A Validity Gate for $Ο$-Weighted Post-Training
Intervention-Certified Amplification (ICA) is a validity gate for sigma-weighted hybrid SFT/RL post-training. The paper shows that DIAA, a per-sample sigma-weighted hybrid, emits unexecuted tool-integrated reasoning on 29% of MATH-500 prompts (three times vanilla GRPO) even with Python fences removed from its anchor. ICA gates sigma with paired-rollout evidence, cutting TIR to 13.8% (frozen) and 11.2% (online) and raising accuracy from 47.5% to 48.7% and 49.3%. It transfers to GSM8K.
arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026π§ AI - 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%).
arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026π§ 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.
arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026π§ 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.
arxiv.orgpaperAdded 28 Oct 2026, published 24 Oct 2026π§ AI - 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.
arxiv.orgpaperAdded 28 Oct 2026, published 24 Oct 2026π‘οΈ Security - When Elliptic-Curve Semiprimality Certificates Reveal Factors
The paper formalizes elliptic-curve semiprimality certificates by adapting the Goldwasser-Kilian primality criterion. For certificates built with complex multiplication, a norm-lattice reconstruction factors N from (N, S, D) alone in expected polynomial time. The author uses it, plus public-point and quadratic-twist attacks, to factor Reble's 1084-digit and Broadhurst's 5061-digit moduli, and shows that hidden-cofactor, withheld-CM-map and private-isogeny repairs also fail.
eprint.iacr.orgpaperAdded 26 Oct 2026, published 24 Oct 2026π‘οΈ 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.
arxiv.orgpaperAdded 27 Oct 2026, published 23 Oct 2026π‘οΈ Security - 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.
arxiv.orgpaperAdded 27 Oct 2026, published 23 Oct 2026π§ AI 
BringingΒ post-quantum cryptography to geographically fenced key managementCloudflare'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.
blog.cloudflare.comarticleAdded 29 Oct 2026, published 23 Oct 2026π‘οΈ Security- 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.
arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026π§ AI - 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.
arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026π‘οΈ Security - 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.
arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026π§ AI - 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.
eprint.iacr.orgpaperAdded 24 Oct 2026, published 23 Oct 2026π‘οΈ 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.
eprint.iacr.orgpaperAdded 24 Oct 2026, published 22 Oct 2026π‘οΈ Security - 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.
arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026π§ AI - 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.
eprint.iacr.orgpaperAdded 24 Oct 2026, published 22 Oct 2026π‘οΈ Security - 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.
arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026π§ AI 
PHP2Shell Exploitation Started Days Before PatchesPHP2Shell (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.
securityweek.comarticlePublished and added 22 Oct 2026π‘οΈ Security
Remotely exploitable PHP bug found on 70% of worldβs websitesPHP2Shell 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.
protos.comarticlePublished and added 21 Oct 2026π‘οΈ Security
PHP2Shell Patches Land as First Exploitation Attempts Hit SensorsPHP2Shell 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.
securityweek.comarticlePublished and added 21 Oct 2026π‘οΈ Security
PHP Patches Critical RCE Vulnerabilities, Organizations Urged to Take Unpatched Sites OfflinePHP2Shell 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.
socket.devarticlePublished and added 21 Oct 2026π‘οΈ Security
Browsers Block eMudhra Intermediate CAs Over Recoverable Private KeysEight 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.
securityweek.comarticlePublished and added 21 Oct 2026π‘οΈ Security- 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%.
arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026π§ 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.
arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026π§ AI - Beyond g-meanΒ²: complementary metrics for monitorability
Beyond g-meanΒ² 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-meanΒ². The fetched text is a single sentence, so the metric definitions, experiments and results are not available here.
alignment.openai.comarticleAdded 29 Oct 2026, published 20 Oct 2026π§ 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.
arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026π§ AI - 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.
arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026π‘οΈ Security - 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.
arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026π§ AI 
Siemens Software Center Vulnerability Exploited in ClickFix Malware AttacksCVE-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.
securityweek.comarticlePublished and added 19 Oct 2026π‘οΈ Security- 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.
eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026π§ AI - 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.
eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026π‘οΈ Security 
[Draft ERC] Swarm AI Framework β registry and charter model for purpose-bound agent swarmsEthereum 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.
ethereum-magicians.orgthreadPublished and added 18 Oct 2026π§ 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.
arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026π§ 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.
arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026π§ 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.
arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026π§ 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.
arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026π§ AI 
Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation PipelineAnnounces 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 4.98β9.82 points. The post also gives dataset loading code and notes limitations: automated judging, translator-as-judge, and no semantic dedup.
huggingface.coarticlePublished and added 16 Oct 2026π§ 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.
arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026π§ AI - 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.
arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026π‘οΈ Security - 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.
arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026π§ 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,β 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.
arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026π§ AI - 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.
arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026π‘οΈ Security - 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).
arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026π§ 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.
arcfu.comarticlePublished and added 14 Oct 2026π§ AI
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