Search: “safety”

Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR

arXiv:2605.20164v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has made post-training highly effective when correctness can be checked automatically. However, many impo

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One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer

DGX agent

arXiv:2511.22940v3 Announce Type: replace Abstract: Recent advances in diffusion models have greatly improved pose-driven character animation. However, existing methods are limited to spatially aligne

safetyarxiv-cs-cv

20 May 2026

Optimal Representation Size: High-Dimensional Analysis of Pretraining and Linear Probing

arXiv:2605.20105v1 Announce Type: new Abstract: Learning to generalise from limited data is a fundamental challenge for both artificial and biological systems. A common strategy is to extract reusable

PAPO-VLA: Planning-Aware Policy Optimization for Vision-Language-Action Models

arXiv:2605.19580v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models show promising ability in language-guided robotic tasks. However, making VLA policies reliable remains challenging,

safetyarxiv-cs-ro

patches allow for trust boundaries > patches separate 'system wants to change state' from 'change is now accepted policy determines what hap…

Patches represent a mechanism for establishing trust boundaries in systems by decoupling the intent to change state from the acceptance and policy-driven implementation of that change. This separation

safetyyohei-nakajima--x

PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents

arXiv:2605.19932v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate over long and recurring external contexts, like document corpora and code repositories. Across in

Phase-Aware Mixture of Experts for Agentic Reinforcement Learning

arXiv:2602.17038v3 Announce Type: replace Abstract: Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a single

Physics-informed simulation framework for realistic sonar image generation and statistical validation

arXiv:2605.19712v1 Announce Type: new Abstract: Synthetic sonar datasets offer a scalable alternative to costly real-world acquisition, yet their utility remains limited by the absence of rigorous qua

Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance

arXiv:2605.18801v1 Announce Type: new Abstract: Data is fundamental to large language models (LLMs). However, understanding of what makes certain data useful for different stages of an LLM workflow, i

Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions

arXiv:2605.19208v1 Announce Type: cross Abstract: Physical activity (PA) plays an important role in maintaining and improving health. Daily steps have been a key PA measure that is easily accessible w

Prediction Is Not Physics: Learning and Evaluating Conserved Quantities in Neural Simulators

arXiv:2605.18883v1 Announce Type: cross Abstract: A diffusion model trained on Hamiltonian trajectories can achieve rollout MSE near 10^{-3}, but the standard deviation of its energy over time is betw

Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas

arXiv:2605.19685v1 Announce Type: cross Abstract: Accurately assessing financial risk requires capturing both individual asset volatility and the complex, asymmetric dependence structures that emerge

Progressive Autonomy as Preference Learning: A Formalization of Trust Calibration for Agentic Tool Use

arXiv:2605.19151v1 Announce Type: new Abstract: We formalize trust calibration for agentic tool use (deciding when an automated agent's proposed action may execute autonomously versus require human ap

PROWL: Prioritized Regret-Driven Optimization for World Model Learning

arXiv:2605.18803v1 Announce Type: cross Abstract: Modern action-conditioned video world models achieve strong short-horizon visual realism, yet remain unreliable on rare, interaction-critical transiti

Quantifying the Pre-training Dividend: Generative versus Latent Self-Supervised Learning for Time Series Foundation Models

arXiv:2605.19462v1 Announce Type: cross Abstract: The success of self-supervised learning (SSL) in vision and NLP has motivated its rapid adoption for time series. However, research has focused primar

Rapid patient-specific neural networks for intraoperative X-ray to volume registration

arXiv:2503.16309v2 Announce Type: replace-cross Abstract: Advanced navigation techniques in image-guided interventions and surgical robotics require the rapid and precise alignment of 3D preoperative

Reasoning Portability: Guiding Continual Learning for MLLMs in the RLVR Era

arXiv:2605.18903v1 Announce Type: cross Abstract: Vision-Language Models in Continual Learning (VLM-CL) aim to continuously adapt to new multimodal tasks while retaining prior knowledge. The emerging

Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design

arXiv:2602.04663v2 Announce Type: replace-cross Abstract: Reinforcement learning has been widely applied to diffusion and flow models for visual tasks such as text-to-image generation. However, these

Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agents

arXiv:2605.20061v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) is a promising paradigm for improving large language model (LLM) agents on long-horizon interactiv

safetyarxiv-cs-cl

RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning

arXiv:2605.19033v1 Announce Type: cross Abstract: Supervised open-loop training has been widely adopted for training traffic simulation models; however, it fails to capture the inherently dynamic, mul

RoboMD: Uncovering Robot Vulnerabilities through Semantic Potential Fields

arXiv:2412.02818v4 Announce Type: replace-cross Abstract: Robot manipulation policies, while central to the promise of physical AI, are highly vulnerable in the presence of external variations in the

RoHIL: Robust Human-in-the-Loop Robotic Reinforcement Learning Against Illumination Variations

arXiv:2605.19924v1 Announce Type: new Abstract: Human-in-the-loop reinforcement learning systems achieve near-perfect success on the workstation where they are trained, but collapse when the same robo

safetyarxiv-cs-ro

RoVLA: Multi-Consistency Constraints for Robust Vision-Language-Action Models

arXiv:2605.19678v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong performance on embodied manipulation, yet they remain brittle under visual observation changes, pa

safetyarxiv-cs-ro

SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection

arXiv:2605.20157v1 Announce Type: new Abstract: Music streaming fraud, where bad actors artificially inflate stream counts to manipulate chart rankings and royalty payments, poses a significant threat

SAGE: Shaping Anchors for Guided Exploration in RLVR of LLMs

arXiv:2605.18864v1 Announce Type: cross Abstract: Recent studies observe that reinforcement learning with verifiable rewards (RLVR) reliably improves pass@1 on reasoning tasks, yet often fails to yiel

Scene-Action Prompt Fusion for Coherent Text-to-Video Storytelling

arXiv:2503.06310v4 Announce Type: replace Abstract: Generating coherent long-form video sequences from discrete text prompts remains challenging due to difficulties in maintaining temporal coherence,

SceneCode: Executable World Programs for Editable Indoor Scenes with Articulated Objects

arXiv:2605.19587v1 Announce Type: new Abstract: Indoor scene synthesis underpins embodied AI, robotic manipulation, and simulation-based policy evaluation, where a useful scene must specify not only w

Self-Creative Text-to-Object Generation using Semantic-Aware Spatial Weighting

arXiv:2605.19554v1 Announce Type: new Abstract: Instilling creativity in text-to-image (T2I) generation presents a significant challenge, as it requires synthesized images to exhibit not only visual n

Set-Valued Policy Learning

arXiv:2605.19830v1 Announce Type: new Abstract: Conventional treatment policies map patient covariates to a single recommended intervention in order to maximize expected clinical outcomes. Although a

SimGym: A Framework for A/B Test Simulation in E-Commerce with Traffic-Grounded VLM Agents

arXiv:2605.19219v1 Announce Type: new Abstract: A/B testing remains the gold standard for evaluating modifications to e-commerce storefronts, yet it diverts traffic, requires weeks to reach statistica

Smooth Partial Lotteries for Stable Randomized Selection

arXiv:2605.20069v1 Announce Type: new Abstract: Competitive selection processes, from scientific funding to admissions and hiring, use evaluations to score candidates, and eventually choose a subset o

Spatially Prompted Visual Trajectory Prediction for Egocentric Manipulation

arXiv:2605.20085v1 Announce Type: new Abstract: Robotic manipulation is often specified through language instructions or task identifiers, yet cluttered environments with similar objects are better ha

Stitched Value Model for Diffusion Alignment

arXiv:2605.19804v1 Announce Type: cross Abstract: For practical use, diffusion- or flow-based generative models must be aligned with task-specific rewards, such as prompt fidelity or aesthetic prefere

Structural Energy Guidance for View-Consistent Text-to-3D Generation

arXiv:2605.19876v1 Announce Type: new Abstract: Text-to-3D generation based on diffusion models often suffers from the Janus problem, leading to inconsistent geometry across viewpoints. This work iden

StruMPL: Multi-task Dense Regression under Disjoint Partial Supervision and MNAR Labels

arXiv:2605.19931v1 Announce Type: cross Abstract: Estimating forest aboveground biomass (AGB) from Earth observation combines two structurally incompatible label sources: spaceborne lidar provides can

Swimming with Whales: Analysis of Power Imbalances in Stake-Weighted Governance

arXiv:2605.19264v1 Announce Type: new Abstract: Voting methods weighted by stakes are the fundamental governance paradigm in Proof-of-Stake (PoS) blockchains. Such a paradigm is known to be prone to p

Symmetry in the Wild: The Role of Equivariance in Neural Fluid Surrogates

arXiv:2605.18816v1 Announce Type: cross Abstract: Neural surrogates enable orders-of-magnitude acceleration of computational fluid dynamics (CFD) simulations, with the potential to transform engineeri

TEA-Time: Transporting Effects Across Time

arXiv:2603.07018v2 Announce Type: replace-cross Abstract: Treatment effects estimated from a randomized controlled trial are local not only to the study population but also to the time at which the tr

TEMPO: Temporal Enforcement via Mode-Separated Policy Optimization for Trustworthy LLM Backtesting

arXiv:2605.18843v1 Announce Type: new Abstract: Backtesting large language models on historical events requires reasoning exclusively from information available before a specified cutoff date. Yet mod

Text-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLP

arXiv:2605.20066v1 Announce Type: new Abstract: Knowledge graph question answering seeks to translate natural language questions into executable queries over knowledge graphs, but existing approaches

safetyarxiv-cs-cl

The Accessibility Capability Boundary: Operational Limits and Expansion Potential of AI-Generated Browser-Native Accessibility Systems

arXiv:2605.19638v1 Announce Type: cross Abstract: As large language models (LLMs) demonstrate increasing competence in synthesizing functional user interfaces, a fundamental question emerges in access

The consensus of analysts is that AI capital investments will rise by 20% a year for five years while revenues are expected to grow 15% resu…

The consensus of analysts is that AI capital investments will rise by 20% a year for five years while revenues are expected to grow 15% resulting in negative returns. 'The AI boom will become a story

the crazy part is that people are “clowning” me without knowing anything about the training or whether anything else other than scaled chang…

the crazy part is that people are “clowning” me without knowing anything about the training or whether anything else other than scaled changed or how the model does on anything else. (or what it costs

The Uncomfortable Truth About AI “Reasoning” | World Science Festival https://youtu.be/iFYF_e1GSGI?si=pFNjvhp48TMKp-PE via @YouTube @GaryMar…

This video from the World Science Festival, shared by cognitive scientist Gary Marcus, examines the gap between AI systems' claimed reasoning capabilities and their actual mechanisms, likely arguing t

think you know @garymarcus because read a few of his tweets? try watching this, to see the real deal, and why, for example, the US Senate in…

think you know @garymarcus because read a few of his tweets? try watching this, to see the real deal, and why, for example, the US Senate invited him to testify. Superb conversation between @bgreene a

ThoughtTrace: Understanding User Thoughts in Real-World LLM Interactions

arXiv:2605.20087v1 Announce Type: cross Abstract: Conversational AI has now reached billions of users, yet existing datasets capture only what people say, not what they think. We introduce ThoughtTrac

Toward an AI-Powered Computational Testbed for Workforce Policy

arXiv:2605.19064v1 Announce Type: cross Abstract: Workforce transformations are difficult to forecast and costly to mismanage. In particular, the integration of artificial intelligence into knowledge

Towards Distillation Guarantees under Algorithmic Alignment for Combinatorial Optimization

arXiv:2605.20074v1 Announce Type: new Abstract: Distillation transfers knowledge from a large model trained on broad data to a smaller, more efficient model suitable for deployment. In structured pred