safety

Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations

arXiv:2511.05613v2 Announce Type: replace-cross Abstract: Foundation models are increasingly central to high-stakes AI systems, and governance frameworks now depend on evaluations to assess their risk

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Why things will eventually fall apart: 1. Everybody, even Google, seems to be treating AI as if it were some kind of winner take all competi…

DGX agent

Why things will eventually fall apart: 1. Everybody, even Google, seems to be treating AI as if it were some kind of winner take all competition like web search was, in which Google taking over 95% 2.

safetygary-marcus--x

2 Jun 2026

World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications

arXiv:2606.00133v1 Announce Type: new Abstract: World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artifici

World Models for Robotic Manipulation: A Survey

arXiv:2606.00113v1 Announce Type: new Abstract: Robotic manipulation depends on the ability to anticipate how actions reshape objects, contacts, and scene geometry before execution. Learned world mode

World-Task Factorization for Robot Learning

arXiv:2606.02027v1 Announce Type: cross Abstract: Robot learning must produce policies that generalize to new combinations of constraints, teammates, and environments. To achieve this, we must structu

X-Foresight: A Joint Vision-Action Causal Forecasting Network via Predictive World Modeling

arXiv:2605.24892v2 Announce Type: replace Abstract: Physical world knowledge resides mainly in videos. Equipping Vision-Language-Action (VLA) models with such knowledge is fundamental for safe and gen

A hitchhiker's guide to Poisson gradient estimation

arXiv:2602.03896v2 Announce Type: replace-cross Abstract: Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samp

A Lecture Note on Offline RL and IRL, Part II: Foundations of Inverse Reinforcement Learning and Dynamic Discrete Choice Models

arXiv:2605.30843v1 Announce Type: new Abstract: In the forward reinforcement-learning problem, the reward is fixed and known; the learner is asked to find a good policy or value function. Here we turn

A Persona-Based Evaluation Framework for Pluralistic Alignment in Generative AI

arXiv:2605.31021v1 Announce Type: new Abstract: Current alignment paradigms for generative artificial intelligence rely predominantly on monolithic benchmarking frameworks that reduce the plurality of

A study on a Real-Time VR-Based Teleoperation Framework for Manipulator in Dynamic Environment

arXiv:2605.30989v1 Announce Type: new Abstract: Robot teleoperation enables safe, non-contact task execution in hazardous environments where direct human access is difficult, and its application has e

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization

arXiv:2605.30452v1 Announce Type: cross Abstract: Many machine learning problems involve multiple inherent trade-offs that are best addressed by gradient-based multi-objective optimization (MOO) algor

Active Timepoint Selection for Learning Measure-Valued Trajectories

arXiv:2605.30625v1 Announce Type: cross Abstract: Inferring continuous probability paths from sparse snapshots is a fundamental challenge in domains like single-cell biology, where high-fidelity data

Adaptive Artificial Time-Delay Control with Barrier Lyapunov Constraints for Euler-Lagrange Robots

arXiv:2605.31405v1 Announce Type: new Abstract: This paper addresses the challenge of simultaneously compensating for state-dependent uncertainties and enforcing time-varying state constraints in Eule

AI Loss of Control Incident Management: Response & Resilience

arXiv:2605.30406v1 Announce Type: cross Abstract: Recent research demonstrating AI systems exhibiting deception and shutdown resistance suggests that AI loss of control (LOC) is an urgent policy conce

Annealed Softmax Greedy in Many-Armed Bayesian Bandits

arXiv:2605.31034v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) and group-based policy optimization methods such as GRPO update a stochastic policy by sampling

Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding

arXiv:2605.30742v1 Announce Type: new Abstract: This paper addresses the task of temporal sentence grounding (TSG). Although many respectable works have made decent achievements in this important topi

Are Full Rollouts Necessary for On-Policy Distillation?

arXiv:2605.31490v1 Announce Type: new Abstract: On-policy distillation (OPD) provides dense teacher feedback along rollouts generated by the student and has emerged as a promising post-training paradi

safetyarxiv-cs-cl

Benchmarking Machine Learning Uncertainty Quantification Methodologies for Predicting Turbine Gas Temperature Degradation

arXiv:2605.30585v1 Announce Type: cross Abstract: Effective prognostics and health management of modern engines relies on accurate turbine gas temperature predictions and robust uncertainty quantifica

BIAS-ID: A Framework for Analyzing Transformation Biases in AI-Generated Image Detectors

arXiv:2605.31153v1 Announce Type: new Abstract: Given the surge of harmful AI-generated imagery online, reliably distinguishing authentic images from generated ones has become an urgent research topic

Biases in the Blind Spot: Detecting What LLMs Fail to Mention

arXiv:2602.10117v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often provide chain-of-thought (CoT) reasoning traces that appear plausible, but may hide internal biases. We cal

BiSegMamba: Efficient Bidirectional Tri-Oriented Mamba for 3D Medical Image Segmentation

arXiv:2605.30972v1 Announce Type: new Abstract: Accurate 3D medical image segmentation requires both long-range volumetric context and fine boundary preservation. CNN-based methods have limited global

Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion Large Language Models

arXiv:2510.11683v3 Announce Type: replace-cross Abstract: A key challenge in applying reinforcement learning (RL) to diffusion large language models (dLLMs) is the intractability of their likelihood f

Breaking Information Cocoons: A Hyperbolic Framework for Balancing Exploration and Exploitation in Recommender Systems

arXiv:2411.13865v4 Announce Type: replace-cross Abstract: Modern recommender systems often create information cocoons, restricting users' exposure to diverse content. The central challenge is to balan

Building Generalization Into Behavior Generation Via Adaptive Compositions of Regularities

arXiv:2605.31110v1 Announce Type: new Abstract: Generalization in robotics requires prior knowledge about how the world is structured, yet this structure changes from one situation to the next. This p

Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture

arXiv:2601.22412v2 Announce Type: replace Abstract: Video-based human movement analysis holds potential for movement assessment in clinical practice and research. However, the clinical implementation

Can Aerial VLA Models Cooperate? Evaluating Closed-Loop Air-Ground Coordination with CARLA-Air

arXiv:2605.31066v1 Announce Type: new Abstract: Recent aerial vision-language-action (VLA) models show promising single-UAV capabilities, such as tracking moving objects and navigating to language-spe

Causal Evaluation of Membership Inference Attacks

arXiv:2602.02819v3 Announce Type: replace Abstract: Membership Inference Attacks (MIAs) aim to distinguish training points (members) from unseen data (non-members), and are widely used to quantify mem

CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment

arXiv:2605.30635v1 Announce Type: new Abstract: Inferring dynamics from population snapshots is a fundamental challenge in machine learning and biology. In scRNA-sequencing (scRNA-seq), destructive me

COFT: Counterfactual-Conformal Decoding for Fair Chain-of-Thought Reasoning in Large Language Models

arXiv:2605.30641v1 Announce Type: cross Abstract: Large language models (LLMs) can reveal and amplify societal biases during chain-of-thought (CoT) generation. We present COFT (Chain of Fair Thought),

COMPASS: Cognitive MCTS-Guided Process Alignment for Safe Search Agents

arXiv:2605.30838v1 Announce Type: new Abstract: LLM-powered search agents enable multi-step reasoning and tool use. However, these capabilities introduce retrieval-induced safety degradation, as harmf

Configurable Reward Model for Balanced Safety Alignment

arXiv:2605.30487v1 Announce Type: new Abstract: Aligning large language models (LLMs) to heterogeneous and rapidly evolving safety requirements remains a critical challenge. Existing instruction-tuned

safetyarxiv-cs-cl

ConSensus: Multi-Agent Collaboration for Multimodal Sensing

arXiv:2601.06453v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly grounded in sensor data to perceive and reason about human physiology and the physical world. However,

ConsisGuard: Aligning Safety Deliberation with Policy Enforcement in LLM Guardrails

arXiv:2605.31073v1 Announce Type: new Abstract: Reasoning-based LLM guardrails improve safety moderation by generating explicit rationales before issuing final decisions. However, their rationales do

safetyarxiv-cs-cl

Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion

arXiv:2605.31388v1 Announce Type: new Abstract: Multi-Objective Reinforcement Learning (MORL) extends standard RL by optimizing policies with respect to multiple, often conflicting, objectives. While

Contextual Scalarisation Thompson Sampling for multi-objective decisions in public media

arXiv:2605.31291v1 Announce Type: cross Abstract: Recommender systems may operate under multiple, competing objectives. For example, audience reach, cultural values, public service mandate, and operat

Cost-aware Stopping for Bayesian Optimization

arXiv:2507.12453v5 Announce Type: replace Abstract: In automated machine learning, scientific discovery, and other applications of Bayesian optimization, deciding when to stop evaluating expensive bla

Counterfactual Evaluation Reveals Hidden Capability Profiles in Clinical LLMs and Agents

arXiv:2605.30590v1 Announce Type: cross Abstract: Two clinical AI systems can score nearly identically on coverage-based rubrics yet behave radically differently when their patient inputs change: one

Cross-Modal Attention Calibration for LVLM Hallucination Mitigation

arXiv:2501.01926v3 Announce Type: replace-cross Abstract: Large vision-language models (LVLMs) have shown remarkable capabilities in visual-language understanding. Despite their success, LVLMs still s

Current AI is bandaids all the way down; LLMs can’t play nicely with basic tools like databases and knowledge graphs, and you never know wha…

Current AI is bandaids all the way down; LLMs can’t play nicely with basic tools like databases and knowledge graphs, and you never know what you are going to get. It’s long time to face facts: LLMs h

safetygary-marcus--x

dashi: A Python library for Dataset Shift Characterization to Support Trustworthy AI Development and Deployment

arXiv:2605.31360v1 Announce Type: cross Abstract: The Artificial Intelligence (AI) life cycle requires a thorough understanding of the underlying data dynamics for robust, safe and cost-effective AI d

Decoding the Surgical Scene: A Scoping Review of Scene Graphs in Surgery

arXiv:2509.20941v2 Announce Type: replace Abstract: As surgical AI transitions from pixel-level detection to complex reasoning, Scene Graphs (SGs) offer the structured, relational representations nece

Detect in Any Scene: An Agentic Framework for Object Detection with Experience-Aware Reasoning

arXiv:2605.31174v1 Announce Type: new Abstract: Object detection in real-world scenarios remains challenging due to diverse image degradations and heterogeneous object distributions, which significant

Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents

arXiv:2605.31354v1 Announce Type: new Abstract: Modular visual reasoning systems increasingly rely on shared working memory for multi-step collaboration, yet the failure dynamics of intermediate state

Diagnosing the Reliability of LLM-as-a-Judge via Item Response Theory

arXiv:2602.00521v2 Announce Type: replace Abstract: While LLM-as-a-Judge is widely used in automated evaluation, existing validation practices primarily operate at the level of observed outputs, offer

Differentially Private Preference Data Synthesis for Large Language Model Alignment

arXiv:2605.30808v1 Announce Type: cross Abstract: Preference alignment is a crucial post-training step for large language models (LLMs) to ensure their outputs align with human values. However, post-t

DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlation

arXiv:2506.11653v3 Announce Type: replace-cross Abstract: Dataset bias often leads deep learning models to exploit spurious correlations instead of task-relevant signals. We introduce the Standard Ant

Distilling LLM Feedback for Lean Theorem Proving

arXiv:2605.30861v1 Announce Type: new Abstract: Post-training for reasoning models typically combines supervised fine-tuning with reinforcement learning from verifiable rewards, most commonly with GRP

DiTTo: Scalable Order-aware All-in-One Image Restoration Agent

arXiv:2605.30915v1 Announce Type: new Abstract: Real-world images rarely suffer from a single degradation, and the order in which degradations are removed substantially affects the final restoration q