Coordinators that act on what they infer about teammates are usually certified by the accuracy of those inferences. We argue that accuracy is the wrong certificate, for two reasons. First, the accurac...
arXiv:2606. 29654v1 Announce Type: new Abstract: Multi-agent deliberation among LLMs can improve reasoning, but deployment requires deciding when the current answer is reliable enough to act on and when it should be escalated to human review.
By Mengdie Flora Wang, Haochen Xie, Guanghui Wang, Devin Zhang, Jae Oh Woo
The paper audits a developer‑accessible on‑device language model, revealing that it can confidently produce incorrect answers while refusing benign prompts, a phenomenon termed task‑asymmetric miscalibration. The model’s confident outputs are surface‑indistinguishable, with classifiers based on user‑visible features failing to separate correct from wrong responses. The authors propose a model‑agnostic audit protocol, a surface‑indistinguishability test, and a black‑box consistency wrapper that improves reliability without requiring model access.
By Shashwat Pandey, Satwik Pandey, Suresh Raghu
The study investigates how humans and AI collaborate on a puzzle task, focusing on referential uncertainty—when a description could refer to multiple objects. It finds that eliciting a belief distribution over candidate pieces yields better calibration and discrimination than raw action probabilities, and that precise descriptions or well‑targeted hedges significantly reduce the acceptance of wrong placements. However, the AI rarely externalizes uncertainty, and poorly targeted hedges can be counterproductive.
By Christian Poelitz, Finale Doshi-Velez, Si\^an Lindley
arXiv:2607. 17044v1 Announce Type: cross Abstract: Multi-step enterprise agent tasks fail in a characteristic way: single-pass inference has no checkpoint between deciding an answer and committing to it.
By Arunabh Dastidar (for the Leni Team)
The paper introduces TRUST, a token‑level reward model that predicts the correctness of partial chain‑of‑thought (CoT) traces in vision‑language‑action (VLA) policies, enabling monitoring and selective steering of reasoning. On driving and manipulation VLA benchmarks, TRUST improves reasoning accuracy and reduces collision rates and trajectory errors, though its impact on overall task performance varies across tasks. The study defines two evaluation axes—correctability and actionability—to assess when CoT can serve as a runtime safety interface.
By Sathwik Karnik, Joseph JR. Lee, Aryaman Gupta, Somil Bansal