arXiv Machine Learning

RAPT: Model-Predictive Out-of-Distribution Detection and Failure Diagnosis for Sim-to-Real Humanoid Deployment

arXiv:2602. 01515v2 Announce Type: replace-cross Abstract: Deploying learned control policies is risky because policies that appear robust in simulation can confidently enter out-of-distribution (OOD) states after Sim-to-Real transfer, causing silent failures and potential hardware damage.

arXiv AI

Jun 9

ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies

arXiv:2606. 08508v1 Announce Type: cross Abstract: Generative robot policies fail unpredictably at deployment: they hesitate at critical moments, drift off-task, or commit to unrecoverable actions.

By Bingjia Huang, Xiangyu Li, Xiang Wang, Liang Mi, Zixu Hao, Weijun Wang, Hao Wu, Kun Li, Yunxin Liu, Ting Cao

reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jul 15

Tracing Agentic Failure from the Flow of Success

arXiv:2607. 12747v1 Announce Type: new Abstract: Failure attribution for LLM-based agentic systems, i.

By Samuel Yeh, Yiwen Zhu, Shaleen Deep, Sharon Li

llms agents diffusion

Hugging Face Trending Papers

Jul 14

OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning

Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environmental shifts. The detection of out-of-distribution conditions is pivotal to determining when an agent's observations, transitions, or trajectory dynamics deviate from the assumptions underpinning its policy training.

agents reinforcement-learning benchmarks

arXiv Machine Learning

Aug 4

Real-Time Detection and Repair of LLM Agent Failures

arXiv:2608. 02464v1 Announce Type: cross Abstract: LLM agents fail mid-episode -- they loop, cascade tool errors, drift off goal, fabricate results, or silently absorb corrupted content -- and the standard remedy, judging every step with a second LLM, costs more than the agent itself.

By Sunny Dubey

llms agents

arXiv AI

Jul 15

OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning

arXiv:2607. 12523v1 Announce Type: cross Abstract: Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environmental shifts.

By Emil Mittag, Richard Dazeley, Peter Vamplew

agents reinforcement-learning benchmarks

arXiv AI

Jul 16

When Agents Disagree With Themselves: Behavioral Consistency as an Uncertainty Signal for LLM Agents

arXiv:2602. 11619v2 Announce Type: replace Abstract: Running the same LLM agent on identical inputs yields 2.

By Aman Mehta

llms agents benchmarks

arXiv AI

Jul 14

Calibrated e-CUSUM Decoding for Quantized Reasoning Models: Why Token Log-Probability Is the Wrong Observable for Decoding Monitors

arXiv:2607. 11317v1 Announce Type: new Abstract: Low-bit quantization makes small reasoning models inexpensive to deploy but can degrade their chains of thought.

By El Hassane Ettifouri (Novelis Research, Paris, France), Ayoub Belfatmi (Novelis Research, Paris, France), Mahaman Sanoussi Yahaya Alassan (Novelis Research, Paris, France), Walid Dahhane (Novelis Research, Paris, France)

efficiency

arXiv Machine Learning

Jun 3

How Visible Are Silent Manipulation Failures? An Observability Study of False-Success Detection in Simulated Robot Episodes

arXiv:2606. 03134v1 Announce Type: cross Abstract: Imitation-learning policies for robot manipulation inherit the quality of the success labels attached to their training episodes, and those labels are usually produced by the robot's own success check.

By Aarav Bedi (University of California, Berkeley)

robotics

arXiv AI

Aug 11

AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems

arXiv:2604. 02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data.

By Jiyong Kwon, Ujin Jeon, Sooji Lee, Guang Lin

llms agents

arXiv AI

Aug 5

ValueFormer: A Causal Transformer Value Function with Stage-Aware Labels for Semi-Autonomous Vision-Language-Action Policies

arXiv:2608. 02958v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies trained by behavior cloning fail silently: from the action stream alone, a collapsing rollout looks much like one making clean progress, because imitation supplies no notion of progress.

By Inkyu Sa, Konstantin Stulov, Rajat Bhageria

llms agents reinforcement-learning robotics multimodal

arXiv AI

Jul 17

Interventional Causal Circuits for Safe Robot Action Testing and Failure Recovery

arXiv:2607. 14826v1 Announce Type: cross Abstract: Safe physical AI for robot actions are required not only likely to succeed but tested to be safe before execution.

By Naren Vasantakumaar, Tom Schierenbeck, Michael Beetz

agents robotics

arXiv Machine Learning

Jun 8

Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring

arXiv:2603. 25670v3 Announce Type: replace Abstract: Safety monitoring is essential for Cyber-Physical Systems (CPSs).

By John Ayotunde, Qinghua Xu, Guancheng Wang, Lionel C. Briand

benchmarks safety

Related stories