arXiv Machine Learning
When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Reads
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When Should a Failing Robot Ask? Initiating Corrective Human-Robot Dialogue from Audited Sensor Evidence
arXiv:2609.21942v1 Announce Type: cross Abstract: A robot that fails at a task faces the first decision in corrective dialogue: act on its own diagnosis, consult another onboard sensor, or interrupt...
arXiv AI
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CoreSense: Traceable Failure Recall and Conflict-Aware Belief Gating for Auditable Robot Decisions
CoreSense is a robot‑system integration architecture that traces episodic evidence and uses a conflict‑aware belief gate to decide whether to proceed, re‑observe, abstain, or escalates. The gate evaluates scope, provenance, time, contradiction, and support before making a recommendation. Evaluation on public robot datasets, simulations, and a live cloud deployment shows that belief gating can eliminate protocol‑defined unsafe proceeds while maintaining auditability.
arXiv Machine Learning
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How Visible Are Silent Manipulation Failures? An Observability Study of False-Success Detection in Simulated Robot Episodes
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arXiv AI
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Agent-Safety Evaluations as Load-Bearing Evidence: A Vendor-Neutral, Cross-Harness Reconstructability Metric
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arXiv Computer Vision
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No Free Checker: A Survey of Verifiers for Robot Policies
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arXiv Computation and Language
Aug 31
Why Didn't It Check? Unsupported Final Claims and Their Repair in Two Tool-Equipped Language Models
The study investigates how language models equipped with tools can still produce unsupported final claims, even when a single tool call could resolve the uncertainty. It defines two metrics—occurrence (how often unsupported claims arise) and conditional repair (how often they are fixed when evidence is provided). Experiments on Qwen3-32B and Gemma 4 show that providing the missing evidence consistently repairs all unsupported claims in the Qwen3-32B setup, while the Gemma 4 model never produced unsupported claims under the tested conditions.