safety

LucidNFT: LR-Anchored Multi-Reward Preference Optimization for Flow-Based Real-World Super-Resolution

arXiv:2603.05947v3 Announce Type: replace Abstract: Generative real-world image super-resolution (Real-ISR) can synthesize visually convincing details from severely degraded low-resolution (LR) inputs

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MajinBook: An open catalogue of digitally mediated world literature

DGX agent

arXiv:2511.11412v5 Announce Type: replace Abstract: This data paper introduces MajinBook, an open catalogue designed to facilitate the use of shadow libraries-such as Library Genesis and Z-Library-for

safetyarxiv-cs-cl

13 May 2026

Maximin Robust Bayesian Experimental Design

arXiv:2603.14094v2 Announce Type: replace-cross Abstract: We address the brittleness of Bayesian experimental design under model misspecification by formulating the problem as a max--min game between

Metaphor Is Not All Attention Needs

arXiv:2605.12128v1 Announce Type: new Abstract: Large language models are increasingly deployed in safety-critical applications, where their ability to resist harmful instructions is essential. Althou

Missing Old Logits in Asynchronous Agentic RL: Semantic Mismatch and Repair Methods for Off-Policy Correction

arXiv:2605.12070v1 Announce Type: new Abstract: Asynchronous reinforcement learning improves rollout throughput for large language model agents by decoupling sample generation from policy optimization

MoCam: Unified Novel View Synthesis via Structured Denoising Dynamics

arXiv:2605.12119v1 Announce Type: new Abstract: Generative novel view synthesis faces a fundamental dilemma: geometric priors provide spatial alignment but become sparse and inaccurate under view chan

Model-based Bootstrap of Controlled Markov Chains

arXiv:2605.12410v1 Announce Type: cross Abstract: We propose and analyze a model-based bootstrap for transition kernels in finite controlled Markov chains (CMCs) with possibly nonstationary or history

More accurate statement IMHO would be: there won’t immediately be an AI jobpocalyspe. Saying there never will be one hardly seems plausible.…

More accurate statement IMHO would be: there won’t immediately be an AI jobpocalyspe. Saying there never will be one hardly seems plausible. Even less plausible is the claim that there will be an AI j

More Than Meets the Eye: A Semantics-Aware Traffic Augmentation Framework for Generalizable Website Fingerprinting

arXiv:2605.11402v1 Announce Type: new Abstract: Deep learning-based website fingerprinting has emerged as an effective technique for inferring the websites users visit. Although existing methods achie

Morphologically Equivariant Flow Matching for Bimanual Mobile Manipulation

arXiv:2605.12228v1 Announce Type: new Abstract: Mobile manipulation requires coordinated control of high-dimensional, bimanual robots. Imitation learning methods have been broadly used to solve these

Multimodal Abstractive Summarization of Instructional Videos with Vision-Language Models

arXiv:2605.11959v1 Announce Type: cross Abstract: Multimodal video summarization requires visual features that align semantically with language generation. Traditional approaches rely on CNN features

New paper in Nature. The more a government controls its domestic media, the more it dominates AI training data, the more pro-regime outputs …

New paper in Nature. The more a government controls its domestic media, the more it dominates AI training data, the more pro-regime outputs we get from AI. By scraping the open web, LLMs are unwitting

Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models

arXiv:2605.11102v1 Announce Type: new Abstract: Neural warm starts can sharply reduce the number of Newton-Raphson iterations required to solve the AC power flow problem, but existing supervised appro

@Nima292 LLMs are not AGI but will lead to some job losses; true AGI would likely lead to many more.

Gary Marcus argues that current large language models (LLMs) do not constitute artificial general intelligence (AGI), though they will cause some job displacement. He suggests that true AGI, if achiev

no remorse, just further evasion. so slick; so dangerous.

no remorse, just further evasion. so slick; so dangerous. 🚨 SEVEN OPENAI INSIDERS HAVE ACCUSED SAM ALTMAN OF LYING Today on cross, Musk's lawyer walked Altman through all of them: >Ilya Sutskever (co-

Off-Policy Learning with Limited Supply

arXiv:2603.18702v3 Announce Type: replace Abstract: We study off-policy learning (OPL) in contextual bandits, which plays a key role in a wide range of real-world applications such as recommendation s

Offline Constrained Reinforcement Learning under Partial Data Coverage

arXiv:2505.17506v2 Announce Type: replace-cross Abstract: We study offline constrained reinforcement learning with general function approximation in discounted constrained Markov decision processes. P

Offline Policy Evaluation for Manipulation Policies via Discounted Liveness Formulation

arXiv:2605.11479v1 Announce Type: new Abstract: Policy evaluation is a fundamental component of the development and deployment pipeline for robotic policies. In modern manipulation systems, this probl

OGLS-SD: On-Policy Self-Distillation with Outcome-Guided Logit Steering for LLM Reasoning

arXiv:2605.12400v1 Announce Type: new Abstract: We study {on-policy self-distillation} (OPSD), where a language model improves its reasoning ability by distilling privileged teacher distributions alon

OmniNFT: Modality-wise Omni Diffusion Reinforcement for Joint Audio-Video Generation

arXiv:2605.12480v1 Announce Type: new Abstract: Recent advances in joint audio-video generation have been remarkable, yet real-world applications demand strong per-modality fidelity, cross-modal align

On the Importance of Multistability for Horizon Generalization in Reinforcement Learning

arXiv:2605.12206v1 Announce Type: new Abstract: In reinforcement learning (RL), agents acting in partially observable Markov decision processes (POMDPs) must rely on memory, typically encoded in a rec

One Turn Too Late: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue

arXiv:2605.05630v2 Announce Type: replace Abstract: Hidden malicious intent in multi-turn dialogue poses a growing threat to deployed large language models (LLMs). Rather than exposing a harmful objec

OpenAI endorses the Kids Online Safety Act and Illinois SB 315, an AI safety bill to create requirements around transparency, incident reporting, and more (OpenAI Global Affairs)

OpenAI Global Affairs: OpenAI endorses the Kids Online Safety Act and Illinois SB 315, an AI safety bill to create requirements around transparency, incident reporting, and more — Welcome (back) to Th

safetytechmeme

Optimal Policy Learning under Budget and Coverage Constraints

arXiv:2605.12235v1 Announce Type: cross Abstract: We study optimal policy learning under combined budget and minimum coverage constraints. We show that the problem admits a knapsack-type structure and

Optimizing 4D Wires for Sparse 3D Abstraction

arXiv:2605.11977v1 Announce Type: new Abstract: We present a unified framework for 3D geometric abstraction using a single continuous 4D wire, parameterized as a B-spline with spatial coordinates and

ORCE: Order-Aware Alignment of Verbalized Confidence in Large Language Models

arXiv:2605.12446v1 Announce Type: cross Abstract: Large language models (LLMs) often produce answers with high certainty even when they are incorrect, making reliable confidence estimation essential f

Our evaluations show that frontier AI's cyber capabilities are advancing quickly. The length of cyber tasks frontier models can complete has…

Our evaluations show that frontier AI's cyber capabilities are advancing quickly. The length of cyber tasks frontier models can complete has been doubling every few months, and this rate has become fa

OverNaN: NaN-Aware Oversampling for Imbalanced Learning with Meaningful Missingness

arXiv:2605.11525v1 Announce Type: new Abstract: Missing values are routinely treated as defects to be eliminated through deletion or imputation prior to machine learning. In many applied domains, howe

Oversmoothing as Representation Degeneracy in Neural Sheaf Diffusion

arXiv:2605.11178v1 Announce Type: new Abstract: Neural Sheaf Diffusion (NSD) generalizes diffusion-based Graph Neural Networks by replacing scalar graph Laplacians with sheaf Laplacians whose learned

Persona-Conditioned Adversarial Prompting: Multi-Identity Red-Teaming for Adversarial Discovery and Mitigation

arXiv:2605.11730v1 Announce Type: new Abstract: Automated red-teaming for LLMs often discovers narrow attack slices, missing diverse real-world threats, and yielding insufficient data for safety fine-

Physics-Informed Graph Neural Networks for Frequency-Aware Optical Aberration Correction

arXiv:2512.05683v2 Announce Type: replace Abstract: Optical aberrations significantly degrade image quality in microscopy, particularly when imaging deeper into samples. These aberrations arise from d

PointGS: Semantic-Consistent Unsupervised 3D Point Cloud Segmentation with 3D Gaussian Splatting

arXiv:2605.11520v1 Announce Type: new Abstract: Unsupervised point cloud segmentation is critical for embodied artificial intelligence and autonomous driving, as it mitigates the prohibitive cost of d

Position: Universal Aesthetic Alignment Narrows Artistic Expression

arXiv:2512.11883v3 Announce Type: replace-cross Abstract: Over-aligning image generation models to a generalized aesthetic preference conflicts with user intent, particularly when 'anti-aesthetic' out

Post-ADC Inference: Valid Inference After Active Data Collection

arXiv:2605.11511v1 Announce Type: cross Abstract: The validity of statistical inference depends critically on how data are collected. When data gathered through active data collection (ADC) are reused

Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies

arXiv:2605.11453v1 Announce Type: cross Abstract: Practitioners deploying multi-agent large language model (LLM) systems must currently choose between communication topologies such as chain, star, mes

Pretraining Exposure Explains Popularity Judgments in Large Language Models

arXiv:2605.12382v1 Announce Type: new Abstract: Large language models (LLMs) exhibit systematic preferences for well-known entities, a phenomenon often attributed to popularity bias. However, the exte

Primal-Dual Policy Optimization for Linear CMDPs with Adversarial Losses

arXiv:2605.11535v1 Announce Type: new Abstract: Existing work on linear constrained Markov decision processes (CMDPs) has primarily focused on stochastic settings, where the losses and costs are eithe

Primal Generation, Dual Judgment: Self-Training from Test-Time Scaling

arXiv:2605.11299v1 Announce Type: cross Abstract: Code generation is typically trained in the primal space of programs: a model produces a candidate solution and receives sparse execution feedback, of

PriorZero: Bridging Language Priors and World Models for Decision Making

arXiv:2605.12289v1 Announce Type: new Abstract: Leveraging the rich world knowledge of Large Language Models (LLMs) to enhance Reinforcement Learning (RL) agents offers a promising path toward general

Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks

arXiv:2509.06701v2 Announce Type: replace Abstract: We develop a theory of intelligent agency grounded in probabilistic modeling for neural models. Agents are represented as outcome distributions with

probably correct, from @polynoamial: “with today’s AI models, intelligence is a function of inference compute.” but what about tomorrow’s mo…

probably correct, from @polynoamial: “with today’s AI models, intelligence is a function of inference compute.” but what about tomorrow’s models? never forget that humans are remarkably intelligent (t

Prototype Fusion: A Training-Free Multi-Layer Approach to OOD Detection

arXiv:2603.23677v2 Announce Type: replace Abstract: Deep learning models are increasingly deployed in safety-critical applications, where reliable out-of-distribution (OOD) detection is essential to e

Question Difficulty Estimation for Large Language Models via Answer Plausibility Scoring

arXiv:2605.12398v1 Announce Type: new Abstract: Estimating question difficulty is a critical component in evaluating and improving large language models (LLMs) for question answering (QA). Existing ap

Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning

arXiv:2605.11289v1 Announce Type: new Abstract: Average-reward reinforcement learning requires estimating the gain and the bias, which is defined only up to an additive constant. This makes direct dis

RACC: Representation-Aware Coverage Criteria for LLM Safety Testing

arXiv:2602.02280v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) face severe safety risks from jailbreak attacks, yet current safety testing largely relies on static datasets and

Rainbow Deep Q-Learning with Kinematics-Aware Design for Cooperative Delta and 3-RRS Parallel Robot Insertion

arXiv:2605.11697v1 Announce Type: new Abstract: This paper presents a kinematics-aware deep reinforcement learning framework based on Rainbow Deep Q-Networks (DQN) for cooperative peg-in-hole manipula

RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking

arXiv:2605.11151v1 Announce Type: cross Abstract: Offline-to-online reinforcement learning (RL) improves sample efficiency by leveraging pre-collected datasets prior to online interaction. A key chall

Real-Scale Island Area and Coastline Estimation using Only its Place Name or Coordinates

arXiv:2605.11267v1 Announce Type: new Abstract: Accurate measurement of island area and coastline length is crucial for coastal zone monitoring and oceanographic analysis. However, traditional measure