Search: “safety”

The physics of AI weather models

arXiv:2605.23778v1 Announce Type: cross Abstract: Could it be that AI weather models are solving physical equations, although they may not be the equations used by conventional NWP models? We compute

Through the Stealth Lens: Attention-Aware Defenses Against Poisoning in RAG

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arXiv:2506.04390v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) systems are vulnerable to attacks that inject poisoned passages into the retrieved context, even at low c

safetyarxiv-cs-ai

25 May 2026

Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling

arXiv:2603.01655v2 Announce Type: replace Abstract: Ray tracing has become a standard for accurate radio propagation modeling, but suffers from exponential computational complexity, as the number of c

Understanding Goal Generalisation in Sequential Reinforcement Learning

arXiv:2605.23565v1 Announce Type: cross Abstract: Reinforcement learning agents often exhibit unintended goal-directed behaviour outside their training distribution, but we currently lack a principled

UniEmo: Unifying Emotional Understanding and Generation with Learnable Expert Queries

arXiv:2507.23372v2 Announce Type: replace Abstract: Emotional understanding and generation are often treated as separate tasks, yet they are inherently complementary and can mutually enhance each othe

safetyarxiv-cs-cv

UniReg: A Universal Model for Controllable CT Image Registration

arXiv:2503.12868v2 Announce Type: replace Abstract: Learning-based medical image registration has matched the accuracy of conventional methods while offering superior computational efficiency. However

safetyarxiv-cs-cv

V-VLAPS: Value-Guided Planning for Vision-Language-Action Models

arXiv:2601.00969v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models provide strong action priors for robotic manipulation, but their reactive behavior can fail under distribu

VI-CuRL: Stabilizing Verifier-Independent RL Reasoning via Confidence-Guided Variance Reduction

arXiv:2602.12579v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a dominant paradigm for enhancing Large Language Models (LLMs) reasoning,

Vision Transformers Need Better Token Interaction

arXiv:2605.23868v1 Announce Type: new Abstract: Vision Transformers (ViTs) can learn strong image-level representations while their patch representations become less effective for dense prediction dur

safetyarxiv-cs-cv

When Determinants Are Not Enough: Private Rare Switching

arXiv:2605.23131v1 Announce Type: new Abstract: In this note, I would like to share a small research moment where Codex helped me find the right way to adapt rare switching to the private setting. The

Whose Good, Whose Place? The Moral Geography of Agentic AI for Social Good

arXiv:2605.22995v1 Announce Type: cross Abstract: Agentic AI systems are increasingly proposed for social-good domains, often invoking the United Nations Sustainable Development Goals (SDGs) as a voca

A note on convergence of Wasserstein policy optimization

arXiv:2605.22622v1 Announce Type: new Abstract: Wasserstein Policy Optimization (WPO) is a recently proposed reinforcement learning algorithm that leverages Wasserstein gradient flows to optimize stoc

A Tale of Two Cities: Pessimism and Opportunism in Offline Dynamic Pricing

arXiv:2411.08126v2 Announce Type: replace-cross Abstract: We study offline dynamic pricing when historical data provide incomplete coverage of the price space such that some candidate prices, includin

Algebraic Machine Learning for Small-to-Medium Datasets Is Competitive against Strong Standard Baselines

arXiv:2605.22155v1 Announce Type: new Abstract: Symbolic methods are generally not considered competitive with strong modern learners on realistic supervised tasks. We evaluate Algebraic Machine Learn

ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models

arXiv:2605.22222v1 Announce Type: new Abstract: Partial differential equation (PDE) foundation models are pretrained networks that forecast how physical fields like velocity and pressure evolve from a

Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models

arXiv:2603.02938v2 Announce Type: replace Abstract: Graph-based tasks in the zero-shot setting remain a significant challenge due to data scarcity and the inability of traditional Graph Neural Network

BioFormer: Rethinking Cross-Subject Generalization via Spectral Structural Alignment in Biomedical Time-Series

arXiv:2605.22468v1 Announce Type: new Abstract: Cross-subject generalization in biomedical time-series refers to training on data from some subjects and testing on unseen subjects.The key challenge is

Causal Discovery in Structural VAR Models Under Equal Noise Variance

arXiv:2605.21846v1 Announce Type: cross Abstract: Causal discovery from multivariate time series is challenging when causal effects may occur both across time and within the same sampling interval. Th

CCLab: Adversarial Testing of Learning- and Non-Learning-Based Congestion Controllers

arXiv:2605.21915v1 Announce Type: cross Abstract: Congestion controllers (CCs) are critical to network performance, and yet their robustness under adverse conditions remains insufficiently understood.

Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates

arXiv:2509.08933v2 Announce Type: replace Abstract: We study the problem of learning the optimal policy in a discounted, infinite-horizon reinforcement learning (RL) setting in the presence of adversa

Cross-Species RSA Reveals Conserved Early Visual Alignment but Divergent Higher-Area Rankings Across Human fMRI and Macaque Electrophysiology

arXiv:2605.22401v1 Announce Type: new Abstract: Does the relationship between learning rules and brain alignment generalize across species? We extend our prior finding that untrained CNNs match backpr

DecepChain: Inducing Deceptive Reasoning in Large Language Models

arXiv:2510.00319v2 Announce Type: replace Abstract: Large Language Models (LLMs) have been demonstrating strong reasoning capability with their chain-of-thoughts (CoT), which are routinely used by hum

Don't Forget the Critic: Value-Based Data Rehearsal for Multi-Cyclic Continual Reinforcement Learning

arXiv:2605.22454v1 Announce Type: new Abstract: Data rehearsal has emerged as a leading approach for mitigating catastrophic forgetting in Continual Reinforcement Learning (CRL). However, existing wor

ECPO: Evidence-Coupled Policy Optimization for Evidence-Certified Candidate Ranking

arXiv:2605.21993v1 Announce Type: cross Abstract: Ranking systems used in decision-support settings should not only order candidates but also expose evidence that can be independently checked. We stud

Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction

arXiv:2605.21563v1 Announce Type: new Abstract: Recent reviews find that the vast majority of published healthcare federated learning (FL) studies never reach real-world deployment. We developed an em

extit{BlockFormer} : Transformer-based inference from interaction maps

arXiv:2605.21617v1 Announce Type: new Abstract: Inference from interaction maps, such as centromere identification from genome-wide chromosome conformation capture techniques -- notably Hi-C -- can be

Factored Diffusion Policies:Compositionally Generalized Robot Control with a Single Score Network

arXiv:2605.22596v1 Announce Type: new Abstract: Robotic tasks are typically specified by a tuple of factors, such as the object to be grasped, the obstacles to be avoided, the color of the target, and

From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching

arXiv:2605.22340v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) provides high-dimensional profiles of cellular states, enabling data-driven modeling of cellular dynamics over ti

Generative Modeling by Value-Driven Transport

arXiv:2605.22507v1 Announce Type: new Abstract: We propose a new framework for generative modeling based on a discrete-time stochastic control formulation of measure transport. Adapting classic result

Harnesses for Inference-Time Alignment over Execution Trajectories

arXiv:2605.21516v1 Announce Type: new Abstract: Harness engineering has emerged as an important inference-time technique for large language model (LLM) agents, aiming to improve long-term performance

HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Prediction

arXiv:2602.05286v3 Announce Type: replace Abstract: Healthcare facility visit prediction is essential for optimizing healthcare resource allocation and informing public health policy. Despite advanced

Heterogeneous Agent Collaborative Reinforcement Learning

arXiv:2603.02604v2 Announce Type: replace Abstract: We introduce Heterogeneous Agent Collaborative Reinforcement Learning (HACRL), a new Reinforcement Learning from Verifiable Reward (RLVR) problem th

Live Music Diffusion Models: Efficient Fine-Tuning and Post-Training of Interactive Diffusion Music Generators

arXiv:2605.22717v1 Announce Type: cross Abstract: Interactive streaming music generation promises the use of generative models for live performance and co-creation that is impossible with offline mode

Long-term Fairness with Selective Labels

arXiv:2605.22291v1 Announce Type: new Abstract: Long-term fairness algorithms aim to satisfy fairness beyond static and short-term notions by accounting for the dynamics between decision-making polici

MemReward: Graph-Based Experience Memory for LLM Reward Prediction with Limited Labels

arXiv:2603.19310v3 Announce Type: replace Abstract: Reinforcement learning has emerged as a powerful paradigm for improving large language model (LLM) reasoning, where rollouts are sampled from the po

MetaDNS: Enhancing Exploration in Discrete Neural Samplers via Well-Tempered Metadynamics

arXiv:2605.21722v1 Announce Type: cross Abstract: Sampling from discrete distributions with multiple modes and energy barriers is fundamental to machine learning and computational physics. Recent disc

Objective-Induced Bias and Search Dynamics in Multiobjective Unsupervised Feature Selection

arXiv:2605.21561v1 Announce Type: new Abstract: Unsupervised feature selection is commonly formulated as a multiobjective optimisation problem that jointly optimises subset quality and subset size. Ye

On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents

arXiv:2605.21763v1 Announce Type: new Abstract: We study risk-sensitive reinforcement learning in finite discounted MDPs, where a generative model of the MDP is assumed to be available. We consider a

One-Way Policy Optimization for Self-Evolving LLMs

arXiv:2605.22156v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a promising paradigm for scaling reasoning capabilities of Large Language Models (LLMs)

PEARL: Unbiased Percentile Estimation via Contrastive Learning for Industrial-Scale Livestream Recommendation

arXiv:2605.21752v1 Announce Type: new Abstract: Recommender systems trained on user interaction data are susceptible to behavioral intensity imbalance--a systematic distortion arising from heterogeneo

Post-Training is About States, Not Tokens: A State Distribution View of SFT, RL, and On-Policy Distillation

arXiv:2605.22731v1 Announce Type: new Abstract: Large language model post-training methods such as supervised fine-tuning (SFT), reinforcement learning (RL), and distillation are often analyzed throug

Proxy-Based Approximation of Shapley and Banzhaf Interactions

arXiv:2605.22738v1 Announce Type: new Abstract: Shapley and Banzhaf interactions capture the complex dynamics inherent in modern machine learning applications. However, current estimators for these hi

[Re] FairDICE: A Fair Tradeoff in Multi-objective Offline RL

arXiv:2603.03454v2 Announce Type: replace Abstract: Offline Reinforcement Learning (RL) is an emerging field of RL in which policies are learned solely from demonstrations. Within offline RL, some env

Revisiting Regularized Policy Optimization for Stable and Efficient Reinforcement Learning in Two-Player Games

arXiv:2602.10894v2 Announce Type: replace Abstract: Two-player games such as board games have long been used as traditional benchmarks for reinforcement learning. This work revisits a policy optimizat

SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression

arXiv:2511.04838v2 Announce Type: replace Abstract: Molecular property regression struggles with cases in chemically relevant target ranges that are underrepresented in datasets. Standard average erro

Support-aware offline policy selection for advertising marketplaces

arXiv:2605.21736v1 Announce Type: cross Abstract: Logged advertising auctions make offline reserve-price evaluation attractive but risky. Replay tables can identify policies with large apparent yield

Tailoring Teaching to Aptitude: Direction-Adaptive Self-Distillation for LLM Reasoning

arXiv:2605.22263v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) is an emerging LLM post-training paradigm in which the model serves as its own teacher: conditioned on privileged inf

Target-Aligned Bellman Backup for Cross-domain Offline Reinforcement Learning

arXiv:2605.22376v1 Announce Type: new Abstract: Cross-domain offline reinforcement learning (CDRL) aims to improve policy learning in a target domain by leveraging data collected from a source domain.