The paper investigates how vision–language models (VLMs) diagnose failures in bimanual tool‑use tasks, where failures can arise from tool slip, timing mismatches between hands, or combined causes. It evaluates which sensing channels enable a diagnoser to identify each failure type, finding that tool slip is detectable on the tool hand, timing delays are observable on both arms, and some combined failures remain indistinguishable from passive sensing. The study also shows that a repair policy that escalates when the cause is unclear can match the performance of a policy with full cause knowledge, and that a VLM’s internal state captures the late hand almost perfectly, though the answer depends on question wording.
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)
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...
By Eshika Pathak, Leela Krishna
arXiv:2610.32941v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models perform well on robotic manipulation, yet remain brittle once their own actions carry them into states their trai...
By Thomas Landeg, Thierry Tambe
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.
By Justin Bronder
The study investigates why small language model agents tend to repeat a tool call that just failed. By recording the failed call and its error message in the transcript, the authors measure a negative corrective gain—agents are more likely to repeat the failed action, with a drop of about 1.03 nats per token. The problem is traced to the harness design rather than the model’s understanding of errors, and the authors show that replacing the verbatim call with a runtime-generated description of the failure can reduce this backfiring effect by 76%.
By Esmail Gumaan