Search: “safety”

A Dual-Positive Monotone Parameterization for Multi-Segment Bids and a Validity Assessment Framework for Reinforcement Learning Agent-based Simulation of Electricity Markets

arXiv:2604.10252v1 Announce Type: new Abstract: Reinforcement learning agent-based simulation (RL-ABS) has become an important tool for electricity market mechanism analysis and evaluation. In the mod

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A Hessian-Free Actor-Critic Algorithm for Bi-Level Reinforcement Learning with Applications to LLM Fine-Tuning

arXiv:2601.16399v5 Announce Type: replace Abstract: We study a structured bi-level optimization problem where the upper-level objective is a smooth function and the lower-level problem is policy optim

A mathematical theory of evolution for self-designing AIs

arXiv:2604.05142v2 Announce Type: replace Abstract: As artificial intelligence systems (AIs) become increasingly produced by recursive self-improvement, a form of evolution may emerge, with the traits

A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction

arXiv:2407.10953v5 Announce Type: replace Abstract: The Mutual Reinforcement Effect (MRE) describes a phenomenon in information extraction where word-level and sentence-level tasks can mutually improv

safetyarxiv-cs-cl

A Proposed Biomedical Data Policy Framework to Reduce Fragmentation, Improve Quality, and Incentivize Sharing in Indian Healthcare in the era of Artificial Intelligence and Digital Health

arXiv:2604.11125v1 Announce Type: new Abstract: India generates vast biomedical data through postgraduate research, government hospital services and audits, government schemes, private hospitals and t

A Queueing-Theoretic Framework for Dynamic Attack Surfaces: Data-Integrated Risk Analysis and Adaptive Defense

arXiv:2604.10427v1 Announce Type: cross Abstract: We develop a queueing-theoretic framework to model the temporal evolution of cyber-attack surfaces, where the number of active vulnerabilities is repr

Active Diffusion Matching: Score-based Iterative Alignment of Cross-Modal Retinal Images

arXiv:2604.10084v1 Announce Type: new Abstract: Objective: The study aims to address the challenge of aligning Standard Fundus Images (SFIs) and Ultra-Widefield Fundus Images (UWFIs), which is difficu

Adaptive Bidding Policies for First-Price Auctions with Budget Constraints under Non-stationarity

arXiv:2505.02796v2 Announce Type: replace-cross Abstract: We study how a budget-constrained bidder should learn to adaptively bid in repeated first-price auctions to maximize her cumulative payoff. Th

AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence

arXiv:2604.10579v1 Announce Type: cross Abstract: Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations

Agentic Video Generation: From Text to Executable Event Graphs via Tool-Constrained LLM Planning

arXiv:2604.10383v1 Announce Type: new Abstract: Existing multi-agent video generation systems use LLM agents to orchestrate neural video generators, producing visually impressive but semantically unre

ASPIRin: Action Space Projection for Interactivity-Optimized Reinforcement Learning in Full-Duplex Speech Language Models

arXiv:2604.10065v1 Announce Type: cross Abstract: End-to-end full-duplex Speech Language Models (SLMs) require precise turn-taking for natural interaction. However, optimizing temporal dynamics via st

Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements

arXiv:2604.09628v1 Announce Type: cross Abstract: Explainable AI (XAI) has evolved in response to expectations and regulations, such as the EU AI Act, which introduces regulatory requirements on AI-po

Auto-regressive transformation for image alignment

arXiv:2505.04864v2 Announce Type: replace-cross Abstract: Existing methods for image alignment struggle in cases involving feature-sparse regions, extreme scale and field-of-view differences, and larg

Autonomous Diffractometry Enabled by Visual Reinforcement Learning

arXiv:2604.11773v1 Announce Type: cross Abstract: Automation underpins progress across scientific and industrial disciplines. Yet, automating tasks requiring interpretation of abstract visual informat

Belief-State RWKV for Reinforcement Learning under Partial Observability

arXiv:2604.09671v1 Announce Type: new Abstract: We propose a stronger formulation of RL on top of RWKV-style recurrent sequence models, in which the fixed-size recurrent state is explicitly interprete

Below-ground Fungal Biodiversity Can be Monitored Using Self-Supervised Learning Satellite Features

arXiv:2604.09818v1 Announce Type: new Abstract: Mycorrhizal fungi are vital to terrestrial ecosystem functioning. Yet monitoring their biodiversity at landscape scales is often unfeasible due to time

Beyond Compliance: A Resistance-Informed Motivation Reasoning Framework for Challenging Psychological Client Simulation

arXiv:2604.10507v1 Announce Type: new Abstract: Psychological client simulators have emerged as a scalable solution for training and evaluating counselor trainees and psychological LLMs. Yet existing

Beyond Message Passing: A Semantic View of Agent Communication Protocols

arXiv:2604.02369v3 Announce Type: replace-cross Abstract: Agent communication protocols are becoming critical infrastructure for large language model (LLM) systems that must use tools, coordinate with

Beyond Monologue: Interactive Talking-Listening Avatar Generation with Conversational Audio Context-Aware Kernels

arXiv:2604.10367v1 Announce Type: new Abstract: Audio-driven human video generation has achieved remarkable success in monologue scenarios, largely driven by advancements in powerful video generation

Beyond Reconstruction: Reconstruction-to-Vector Diffusion for Hyperspectral Anomaly Detection

arXiv:2604.11390v1 Announce Type: new Abstract: While Hyperspectral Anomaly Detection (HAD) excels at identifying sparse targets in complex scenes, existing models remain trapped in a scalar 'reconstr

Bidirectional Learning of Facial Action Units and Expressions via Structured Semantic Mapping across Heterogeneous Datasets

arXiv:2604.10541v1 Announce Type: new Abstract: Facial action unit (AU) detection and facial expression (FE) recognition can be jointly viewed as affective facial behavior tasks, representing fine-gra

Binary Flow Matching: Prediction-Loss Space Alignment for Robust Learning

arXiv:2602.10420v2 Announce Type: replace Abstract: Flow matching has emerged as a powerful framework for generative modeling, with recent empirical successes highlighting the effectiveness of signal-

bioLeak: Leakage-Aware Modeling and Diagnostics for Machine Learning in R

arXiv:2604.10965v1 Announce Type: cross Abstract: Data leakage remains a recurrent source of optimistic bias in biomedical machine learning studies. Standard row-wise cross-validation and globally est

Brain-Grasp: Graph-based Saliency Priors for Improved fMRI-based Visual Brain Decoding

arXiv:2604.10617v1 Announce Type: cross Abstract: Recent progress in brain-guided image generation has improved the quality of fMRI-based reconstructions; however, fundamental challenges remain in pre

Bridging Linguistic Gaps: Cross-Lingual Mapping in Pre-Training and Dataset for Enhanced Multilingual LLM Performance

arXiv:2604.10590v1 Announce Type: cross Abstract: Multilingual Large Language Models (LLMs) struggle with cross-lingual tasks due to data imbalances between high-resource and low-resource languages, a

Bridging the RGB-IR Gap: Consensus and Discrepancy Modeling for Text-Guided Multispectral Detection

arXiv:2604.11234v1 Announce Type: new Abstract: Text-guided multispectral object detection uses text semantics to guide semantic-aware cross-modal interaction between RGB and IR for more robust percep

Budget-Aware Uncertainty for Radiotherapy Segmentation QA Using nnU-Net

arXiv:2604.11798v1 Announce Type: cross Abstract: Accurate delineation of the Clinical Target Volume (CTV) is essential for radiotherapy planning, yet remains time-consuming and difficult to assess, e

C2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis

arXiv:2604.00013v2 Announce Type: replace-cross Abstract: Multimodal sentiment analysis aims to integrate textual, acoustic, and visual information for deep emotional understanding. Despite the progre

Calibration Collapse Under Sycophancy Fine-Tuning: How Reward Hacking Breaks Uncertainty Quantification in LLMs

arXiv:2604.10585v1 Announce Type: cross Abstract: Modern large language models (LLMs) are increasingly fine-tuned via reinforcement learning from human feedback (RLHF) or related reward optimisation s

CID-TKG: Collaborative Historical Invariance and Evolutionary Dynamics Learning for Temporal Knowledge Graph Reasoning

arXiv:2604.09600v1 Announce Type: new Abstract: Temporal knowledge graph (TKG) reasoning aims to infer future facts at unseen timestamps from temporally evolving entities and relations. Despite recent

CityGuard: Graph-Aware Private Descriptors for Bias-Resilient Identity Search Across Urban Cameras

arXiv:2602.18047v3 Announce Type: replace Abstract: City-scale person re-identification across distributed cameras must handle severe appearance changes from viewpoint, occlusion, and domain shift whi

Claim2Vec: Embedding Fact-Check Claims for Multilingual Similarity and Clustering

arXiv:2604.09812v1 Announce Type: new Abstract: Recurrent claims present a major challenge for automated fact-checking systems designed to combat misinformation, especially in multilingual settings. W

safetyarxiv-cs-cl

ComSim: Building Scalable Real-World Robot Data Generation via Compositional Simulation

arXiv:2604.11386v1 Announce Type: cross Abstract: Recent advancements in foundational models, such as large language models and world models, have greatly enhanced the capabilities of robotics, enabli

ConfigSpec: Profiling-Based Configuration Selection for Distributed Edge--Cloud Speculative LLM Serving

arXiv:2604.09722v1 Announce Type: cross Abstract: Speculative decoding enables collaborative Large Language Model (LLM) inference across cloud and edge by separating lightweight token drafting from he

Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration

arXiv:2601.07224v2 Announce Type: replace Abstract: While Hybrid Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become the standard paradigm for training LLM agents, effectiv

Context Matters: Vision-Based Depression Detection Comparing Classical and Deep Approaches

arXiv:2604.10344v1 Announce Type: new Abstract: The classical approach to detecting depression from vision emphasizes interpretable features, such as facial expression, and classifiers such as the Sup

Contour Refinement using Discrete Diffusion in Low Data Regime

arXiv:2602.05880v2 Announce Type: replace Abstract: Boundary detection of irregular and translucent objects is an important problem with applications in medical imaging, environmental monitoring and m

CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection

arXiv:2508.03447v2 Announce Type: replace Abstract: Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a si

Cost-optimal Sequential Testing via Doubly Robust Q-learning

arXiv:2604.11165v1 Announce Type: cross Abstract: Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies

CoSToM:Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models

arXiv:2604.10031v1 Announce Type: cross Abstract: Theory of Mind (ToM), the ability to attribute mental states to others, is a hallmark of social intelligence. While large language models (LLMs) demon

COXNet: Cross-Layer Fusion with Adaptive Alignment and Scale Integration for RGBT Tiny Object Detection

arXiv:2508.09533v2 Announce Type: replace-cross Abstract: Detecting tiny objects in multimodal Red-Green-Blue-Thermal (RGBT) imagery is a critical challenge in computer vision, particularly in surveil

CROP: Conservative Reward for Model-based Offline Policy Optimization

arXiv:2310.17245v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) aims to optimize a policy using collected data without online interactions. Model-based approaches are par

Cross-Cultural Bias in Mel-Scale Representations: Evidence and Alternatives from Speech and Music

arXiv:2604.10503v1 Announce Type: cross Abstract: Modern audio systems universally employ mel-scale representations derived from 1940s Western psychoacoustic studies, potentially encoding cultural bia

CSPO: Alleviating Reward Ambiguity for Structured Table-to-LaTeX Generation

arXiv:2604.10918v1 Announce Type: new Abstract: Tables contain rich structured information, yet when stored as images their contents remain 'locked' within pixels. Converting table images into LaTeX c

Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor

arXiv:2501.18490v3 Announce Type: replace-cross Abstract: This article introduces a novel sample-efficient curriculum learning (CL) approach for training an end-to-end reinforcement learning (RL) poli

DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning

arXiv:2503.11892v3 Announce Type: replace Abstract: Multimodal representation learning aims to capture both shared and complementary semantic information across multiple modalities. However, the intri

Model Releases

Decomposing and Reducing Hidden Measurement Error in LLM Evaluation Pipelines

model-releasesarxiv-cs-cl

arXiv:2604.11581v1 Announce Type: new Abstract: LLM evaluations drive which models get deployed, which safety standards get adopted, and which research conclusions get published. Yet these scores carr

Deep deterministic policy gradient with symmetric data augmentation for lateral attitude tracking control of a fixed-wing aircraft

arXiv:2407.11077v3 Announce Type: replace-cross Abstract: The symmetry of dynamical systems can be exploited for state-transition prediction and to facilitate control policy optimization. This paper l