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deploymentsafety.openai.compaper🛡️ Security

GPT-Rosalind-6 System Card Addendum

GPT-Rosalind-6 System Card Addendum is OpenAI's safety documentation for a life-sciences model that shares weights with GPT-6 Astra. It rates the model High in Biological and Chemical and Critical in Cybersecurity under the Preparedness Framework. It reports biology evaluations, trusted-access controls, biosecurity refusal pass rates, a historical cyber jailbreak regression (14.3% task success), and sabotage and monitor-evasion results.

GPT-Rosalind-6GPT-6 AstraOpenAI

Preparedness FrameworkGPT-Rosalind-5.5ExploitBench+5

AI Security, GRC· Published and added 30 Oct 2026

deploymentsafety.openai.compaper🧠 AI

GPT-Rosalind-6 System Card Addendum

GPT-Rosalind-6 System Card Addendum describes OpenAI's life-sciences reasoning model, which uses the same weights as GPT-6 Astra and is offered only through trusted access. It reports life-science benchmark gains over GPT-Rosalind-5.5 (MedChemBench v7 49.6% vs 28.9%), High-capability Biological and Chemical Preparedness results, and below-threshold Critical bio design evaluations. It also covers access controls, refusal evaluations, cyber jailbreak regression and alignment tables.

GPT-6 AstraGPT-Rosalind-6GPT-Rosalind-5.5

OpenAIPreparedness FrameworkMedChemBench v7+16

ai-safety, evals, models· Published and added 30 Oct 2026

arxiv.orgpaper🧠 AI

world2vec: Mechanistic world models through latent dynamics learning

world2vec is a method for learning mechanistic world models from observations and actions alone, without reward information. Its latent dynamics reproduce the true system over long prediction horizons using at least one order of magnitude fewer parameters than DreamerV3, TD-MPC2 and LeWM. The authors report state-of-the-art control of continuous environments from pixels and accurate system identification across a benchmark of 71 attractors.

world modellatent dynamics learning

world2vecDreamerV3TD-MPC2+2

research· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🛡️ Security

TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races

TextSeal2 is a distortion-free LLM text watermark that merges two or more keys into one virtual key and couples it to fresh noise through AutoMark's Gaussian copula. Under speculative decoding it verifies keyed drafts with an extra exponential race, so output matches the target model and acceptance rate is unchanged. On three LLMs at a 10^-3 false-positive rate, it detects 8 to 22 points more 100-token answers than TextSeal.

TextSeal2TextSealAutoMark+2

Gumbel-max schemespeculative decodingGaussian copula+1

AI Security· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races

TextSeal2 is a distortion-free watermark for LLM output that keeps its signal under speculative decoding. It merges two or more keys into one virtual key coupled to fresh noise through AutoMark's Gaussian copula, and verifies drafts with an extra exponential race. On three LLMs it detects 8 to 22 points more 100-token answers than TextSeal at a 10^-3 false-positive rate.

LLMspeculative decodingGumbel-max watermarking+1

TextSeal2TextSealAutoMark+2

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

arxiv.orgpaper🧠 AI

VeriCUA: Verification as Agentic Exploration for Computer-Using Agents

VeriCUA is a platform-agnostic framework that verifies task completion by computer-using agents through agentic exploration. The verifier inspects context to build a task rubric, then picks GUI or code checks for unresolved requirements and combines findings under explicit evidence rules. It works online and offline across desktop, mobile and web, and agrees better with reference labels than the baselines, with the largest offline gains on hard cases.

vision-language modelVeriCUA

arXiv

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

arxiv.orgpaper🧠 AI

Efficient Scalable Oversight via Rewinding

Rewindable interactive proofs (RIPs) are a new model of relativizing interactive proofs for scalable oversight, in which a weak verifier can rewind an AI prover to an earlier saved state. The paper builds an RIP for computations robust to changes in a small fraction of oracle answers. The honest prover runs in linear time, the verifier runs in nearly linear time in the input length after one-time preprocessing, and no cryptography is used. A preliminary MNIST experiment shows an RIP can be faster than a deployed proof system.

rewindable interactive proofsdebaterelativizing interactive proofs

arXivMNIST

research, ai-safety· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

Glance: The Generator Is Its Own Reranker

Glance is a RAG method in which the generator also reranks. It adds a lightweight decision adapter and pointer head that score candidate passages from one shared prefill, so a single resident 9B backbone does generation and reranking. After 39 minutes of MS MARCO fine-tuning it reaches nDCG@10 of 0.741 on SciFact and 0.345 on NFCorpus, against 0.721 and 0.326 for bge-reranker-v2-m3. On 300 HotpotQA questions it improves answer F1 by 0.045 over cross-encoder-selected context. Evaluation covers two retrieval datasets, one QA dataset and one backbone family.

RAGLLMbge-reranker-v2-m3

MS MARCOSciFactNFCorpus+1

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

arxiv.orgpaper🧠 AI

The Athlete as a Scientist of One: Agentic Bayesian Experimental Design for Individualized Physiology from Consumer Wearables

EpiCoach is an agentic system that treats one cross-country mountain biker as an N-of-1 experiment. Claims live in an evidence-gated hypothesis registry, and each must make a probabilistic prediction before a session. Predictions come from a digital twin fitted to heart rate, GPS and altitude. In a replay over eighteen months the twin beat standard forecasters on heart rate but not segment time, and the agent loop matched a static model. In the first three live weeks, the ledger sealed 20 predictions and scored 14, and a safety officer vetoed the agent twice. The registry, benchmark and protocol are released.

LLMBayesian experimental designdigital twin+1

EpiCoacharXiv

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

arxiv.orgpaper🧠 AI

A Unified Hessian-Guided Framework for Post-Training Quantization

This preprint proposes a unified post-training quantization framework for large language models. It derives a Hessian-guided approximation of the output change caused by joint weight and activation quantization, using a tractable Fisher approximation. Quantization ranges, linear transformations and weight rounding are optimized together under one output-sensitivity objective. The authors report gains over state-of-the-art transformation and rounding methods in channel-wise and block-wise settings.

LLMpost-training quantizationHessian-guided approximation+1

arXiv

research, inference· Added 31 Oct 2026, published 29 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

eprint.iacr.orgpaper🛡️ Security

Quasi-polynomial time algorithms for block basis reduction of linear codes

Kolesnikov and Novoselov give block basis reduction (BKZ) algorithms for linear codes that need polynomial or quasi-polynomial numbers of calls to a minimum weight codeword oracle at fixed block size. The earlier Ghentiyala and Stephens-Davidowitz bounds are exponential. The paper adds a truncated BKZ and a DeepBKZ variant, with an implementation for block sizes 2 to 24 and approximation-factor estimates on random codes.

BKZDeepBKZminimum weight codeword problem+4

other· Added 31 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

eprint.iacr.orgpaper🛡️ Security

Optimality and Efficiency of Higher-Order Rank-Based Side-Channel Attacks against Inner Product Masking Schemes

HO-ASPA (Higher-Order Adjusted Spearman Coefficient Analysis) is a rank-based, non-profiling side-channel distinguisher. The paper derives its optimal form against two-share Boolean masking and inner product masking under Hamming weight and LSB leakage. It finds no significant gap between higher and lower orders for HO-ASPA, unlike HO-CPA, and reports that it beats Kruskal-Wallis-based attacks in simulated and real-world experiments.

HO-ASPAHO-CPAKruskal-Wallis+2

Hardware & RF· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

RetroChessBench: A Benchmark for Reconstructing and Proving Chess Histories

RetroChessBench is a 12-task benchmark for retrograde chess: reconstructing legal game histories, exact-length constructions, unique endings and exhaustive answer sets, with references, rubrics and a terminal-reward RL environment. GPT 6 Astra High fails to give the required final answer in 22 of 24 attempts without tools and 18 of 24 with code execution. Mean terminal RL rewards are 14.75% and 27.19%. The authors link the failures to scarce worked derivations in web training data.

GPT-6 AstraLLM

RetroChessBench

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

eprint.iacr.orgpaper🛡️ Security

High Precision Noise Cleaning for CKKS with Application to Asynchronous Threshold CKKS

High Precision Noise Cleaning for CKKS is an IACR ePrint preprint (2026/2675) by Zhihao Li, Cheng Hong and Tao Wei. It composes lower-degree Hermite polynomials to reach output noise bounds such as 2^-300 for noise flooding, and uses this to build an asynchronous threshold CKKS scheme with strong simulation security in the random oracle model. In OpenFHE, binary-message cleaning falls from 9.63 s to 1.54 s.

CKKSOpenFHE

noise floodingHermite interpolationfunctional bootstrapping+2

other· Added 31 Oct 2026, published 29 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Beyond the Scope of This Paper: Practical Flash-Accelerator and Cache-Collision Timing Attacks on Microcontrollers

Colin O'Flynn's paper shows that the flash accelerator in most microcontrollers produces the same timing collisions as a cache, so cacheless Cortex-M devices remain open to cache-collision attacks on AES. AES keys were recovered from as few as 750 traces across six cryptographic libraries, eight architectures and over twenty devices. It also attacks SPI bootloaders, Ethernet, CTAP2 over USB and 802.15.4, and releases the open-source tool FlashWhisperer.

FlashWhispererARM Cortex-M4SPI+1

cache-collision timing attackColin O'FlynnCTAP2+2

Hardware & RF· Added 31 Oct 2026, published 29 Oct 2026

deploymentsafety.openai.compaper🛡️ Security

Addendum to GPT-6 Luna System Card: October 28, 2026 Update

Addendum to the GPT-6 Luna system card for the model update of 29 October 2026 (the title says October 28). OpenAI keeps the High rating for cybersecurity and biological/chemical capability under its Preparedness Framework. It reports ExploitBench 46.19% and SEC-Bench Pro 51.75% (GPT-6 Astra: 85.4%), higher multiturn jailbreak defender success than the October 7 version, and GPT-Red prompt-injection results. It also reports weaker factuality and dual-use biology refusal.

GPT-6 LunaGPT-6 AstraOpenAI+1

Preparedness FrameworkGPT-RedExploitBench+4

AI Security· Published and added 29 Oct 2026

arxiv.orgpaper🧠 AI

Leaving, Staying, or Living There? Duration Commonsense Reasoning with Human-LLM Co-annotations

This paper studies whether vision-language models can infer how long a person stays somewhere (living, staying, or leaving) from geotagged social media posts. The authors build a new corpus of geotagged tweets co-annotated by humans and LLMs. State-of-the-art models struggle even with multi-turn Chain-of-Thought prompting, often missing contextual cues and subtle hashtags while over-relying on prominent visual details.

vision-language modelLLM

arXiv

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

arxiv.orgpaper🧠 AI

Deogen: Automatic Benchmark Generation for Reasoning over Policies

DeoGen is an automatic, parametrizable benchmark generator for deontic reasoning, where a model applies policy rules to facts to reach a verdict. It autoformalizes rules into first-order logic, builds decision trees, and turns each tree path into a benchmark entry. Frontier models give the correct verdict 89-92% of the time, but only 22-40% on entries lacking enough information, even when told to abstain.

LLMfirst-order logic

DeoGenarXiv

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

arxiv.orgpaper🧠 AI

AskStream: When and What to Ask Proactively under Uncertainty in Streaming Video Understanding

AskStream is a multi-turn benchmark for streaming video understanding that adds a third proactive action, ask, alongside wait and answer, for cases where video or user information is incomplete. It has 438 videos, 2,714 manually annotated frame-level time windows (wait, ask, answer), four task categories and eight subtasks. The strongest model reaches only 52.18% on the proposed Ask-F1 metric, in both multiple-choice and open-ended settings.

multimodal large language model

AskStreamAsk-F1arXiv

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

x.compaper🛡️ Security

Cryptography and Security Papers (@FSFG) on X

Log-To-Leak is an arXiv paper (cs.CR, cs.AI) by Yuepeng Hu, Chongyu Fan, Sirat Samyoun and Jian Du on prompt injection attacks against tool-using LLM agents through the Model Context Protocol. The title points to a logging-based data leakage channel. The fetched page holds only the title, authors and arXiv link, with no abstract, so the method and results are not visible here.

Model Context ProtocolLLM

Log-To-Leakprompt injection

AI Security· Added 29 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

Quantize by Doing: Task-Guided Mixed-Precision Quantization of Vision-Language-Action Models

QbD-VLA is a mixed-precision post-training quantization method for vision-language-action models. It uses task success from short closed-loop evaluations to assign weight and activation bit widths, accounting for interactions between components. On four VLA models across all four LIBERO suites it holds task performance at about 4-bit weights and under 6-bit activations. The gain on SIMPLER is small. On a Franka Research 3 arm, X-VLA shows no detected loss, but π0.5 fails.

vision-language-action model

QbD-VLALIBEROSIMPLER+4

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

arxiv.orgpaper🧠 AI

When LLM Architectures Meet Smart Contract Vulnerability Detection: a Replication Study

This paper replicates Chen et al.'s study of LLM-based smart contract vulnerability detection, first on GPT-4 and then on Mixture-of-Experts, RNN-like and State Space Models. The exact replication confirms the high-recall, low-precision pattern on GPT-4. The differentiated replication finds the pattern does not transfer uniformly: GPT-4 and Gemma-3 behave alike despite a roughly 100x parameter gap, while two MoE models diverge.

LLMTransformerMixture of Experts+3

Chen et al.GPT-4Gemma 3+1

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

arxiv.orgpaper🛡️ Security

When LLM Architectures Meet Smart Contract Vulnerability Detection: a Replication Study

This arXiv paper (October 2026) replicates Chen et al.'s study of LLM-based smart contract vulnerability detection. It confirms the high-recall, low-precision pattern on GPT-4, then tests Mixture-of-Experts and RNN-like models plus a preliminary State Space Model run. Gemma-3 matches GPT-4's profile despite far fewer parameters, while two Mixture-of-Experts models diverge, so architecture family alone does not predict detection behavior.

GPT-4GemmaLLM

Chen et al.

AI Security, AppSec· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

Data Scaling Laws in Stereo Matching: From In-domain Fitting to Cross-domain Generalization

This paper studies data scaling laws in stereo matching and releases XStereo, a realistic synthetic dataset of 200 thousand samples. In-domain training on five large datasets shows two regimes: a standard power law, then saturation, with a transition point roughly fixed for a given model. Cross-domain performance depends only weakly on source data scale, and distribution and coverage diversity reduce domain gaps more.

XStereoarXiv

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

arxiv.orgpaper🛡️ Security

Session-Bound Behavioral Watermarking for Autonomous LLM Agents

AgentAnchor is a session-keyed behavioral watermark for attributing LLM agent trajectory logs to an owner. It derives per-step keys from a secret key, a fresh public session ID and semantic observation states, and embeds parity evidence through a KL-bounded exponential tilt. The paper formalizes learned cross-session forgery, and a trusted verifier checks disputed logs using wrong-key calibration and abstention.

LLM

AgentAnchorlearned forgery

AI Security· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

Session-Bound Behavioral Watermarking for Autonomous LLM Agents

AgentAnchor is a session-keyed behavioral watermark that lets an agent owner prove ownership of a trajectory log. Each step key comes from a secret key, a fresh public session identifier and semantic states of recent observations. Parity evidence is embedded through a KL-bounded exponential tilt of action choices. A trusted verifier checks disputed logs without rerunning the agent, using wrong-key calibration and abstention. The paper also formulates a learned cross-session forgery threat.

LLM

AgentAnchorarXiv

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

arxiv.orgpaper🧠 AI

CALM: Conflict-Aware Lifecycle Memory Governance for Long-Horizon Language Agents

CALM is a conflict-aware lifecycle memory governance framework for long-horizon language agents. It turns records into evidence-grounded memory cells, updates their validity and lifecycle states from support, conflict and dependency signals, and admits evidence at query time through training-free or learned gating based on temporal applicability. It beats baselines on LongMemEval-S and LoCoMo with Qwen and GPT backbones, and keeping superseded records helps earlier-state reasoning.

LLMQwen

CALMLongMemEval-SLoCoMo+1

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

arxiv.orgpaper🧠 AI

Does Chain-of-Thought Reliance Follow a Scaling Law?Larger Models Follow Their Steps More, Independent of Post-Training

This paper measures how much a model's final answer depends on its own written chain of thought, using a synthetic box-swap task with exact ground truth. It edits one step in the model's chain to a wrong but valid state and checks whether the answer follows. Across 25 open-weight models, reliance rises with size and saturates (Qwen3 from 16-18% at 0.6B to 97-100% at 14B). Reliance tracks capability (r = 0.99) and is unchanged by post-training.

Qwen3GemmaLlama+2

Gemma 3Llama 3arXiv

research, ai-safety, evals· Added 30 Oct 2026, published 28 Oct 2026

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