Search: “safety”

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

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

DGX agent

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

safetyarxiv-cs-ai

1 Jun 2026

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

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

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),

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,

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

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

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

DOA: Training-Free Decoder-Only Attention Policy for Long-Form Simultaneous Translation with SpeechLLMs

arXiv:2605.31432v1 Announce Type: cross Abstract: Simultaneous speech-to-text translation (SimulST) generates translations while speech is still unfolding, requiring a streaming policy that decides wh

DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization

arXiv:2605.31455v1 Announce Type: cross Abstract: Large language models are increasingly deployed in multi-turn interactive settings where users or environments can iteratively provide lightweight fee

safetyarxiv-cs-cl

Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models

arXiv:2509.24319v4 Announce Type: replace-cross Abstract: Large language models can express values in two main ways: (1) intrinsic expression, reflecting the model's inherent values learned during tra

EchoRL: Reinforcement Learning via Rollout Echoing

arXiv:2605.31228v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards is an effective route for post-training to strengthen the reasoning capability of large language models

Efficient and Uncertainty-Aware Diffusion Framework for Offline-to-Online Reinforcement Learning

arXiv:2605.30776v1 Announce Type: new Abstract: Offline-to-Online Reinforcement Learning (O2O-RL) leverages an offline, pre-trained policy to minimize costly online interactions. Although data-efficie

ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation

arXiv:2605.30484v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown promise for robotic manipulation, yet most existing policies operate reactively by directly regressing ac

Enhancing Regime Shift Detection Using Unstructured Data: A Study on the Treasury Market

arXiv:2605.30363v1 Announce Type: cross Abstract: Regime shifts in financial markets reorganise the joint dynamics of asset prices and macro variables, breaking any single-regime calibration. They are

Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift

arXiv:2605.31250v1 Announce Type: cross Abstract: We propose a unified framework for addressing three key challenges of distribution shift: (1) estimating a model's performance on an unlabeled target

Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation

arXiv:2605.31266v1 Announce Type: cross Abstract: The layout-to-image (L2I) task enables fine-grained control over image generation via object categories and spatial layouts. However, existing L2I met

Equivariant Latent Alignment via Flow Matching under Group Symmetries

arXiv:2605.30705v1 Announce Type: new Abstract: Geometry-aware generative models and novel view synthesis approaches have shown strong potential in visual fidelity and consistency. In parallel, equiva

Extending the UXR Point of View Pyramid: A Generative AI-Augmented Methodology for Human-Centred AI Systems

arXiv:2605.31143v1 Announce Type: cross Abstract: Rising household debt and cost-of-living pressures in the United Kingdom have intensified the role of AI-driven financial technologies in mediating cr

Fair Decisions from Calibrated Scores: Achieving Optimal Classification While Satisfying Sufficiency

arXiv:2602.07285v2 Announce Type: replace Abstract: Binary classification based on predicted probabilities (scores) is a fundamental task in supervised machine learning. While thresholding scores is B

Feat2Go: Visual Feature-Grounded Value Estimation for Embodied Reinforcement Learning

arXiv:2605.30795v1 Announce Type: new Abstract: Reinforcement learning is a promising approach for improving the capabilities of vision-language-action (VLA) models while avoiding the heavy data requi

Feature-Optimized Vision for Adaptive 3D Scene Reconstruction

arXiv:2605.31534v1 Announce Type: cross Abstract: Three-dimensional scene reconstruction depends on local image evidence that is both visually discriminative and geometrically useful. Fixed feature th

FLAG: Flow Policy MaxEnt-RL by Latent Augmented Guidance

arXiv:2605.30749v1 Announce Type: new Abstract: Maximum entropy reinforcement learning (MaxEnt-RL) enables robust exploration, yet practical implementations often restrict policies to simple Gaussians

Forecasting with Hyper-Trees

arXiv:2405.07836v5 Announce Type: replace Abstract: We introduce Hyper-Trees as a novel framework for modeling time series data using gradient boosted trees. Unlike conventional tree-based approaches

GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring

arXiv:2605.30865v1 Announce Type: new Abstract: Continuous glucose monitoring (CGM) provides a dense view of daily metabolic physiology, yet existing generic time-series and CGM-specific foundation mo