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arXiv:2610.34809v1 Announce Type: new Abstract: Language models now answer questions alongside other claims: a note in the system prompt, a message from another agent, their own earlier answer. We as...
The study investigates the role of rationales in role‑specialized question‑answering pipelines by fixing evidence and candidate answers while varying only the rationale sent from a reasoner to a verifier. Experiments on 400 examples from MuSiQue, HotpotQA, and 2WikiMultiHopQA show that faithful rationales add almost no answer accuracy, whereas corrupted rationales significantly alter support judgments, especially under explicit rationale‑checking prompts. Human audits reveal that many corrupted rationales lead to mistrust or rejection, highlighting the importance of evaluating rationale sharing as a verification mechanism rather than merely a means to improve accuracy.
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%.
The paper introduces TalkMesh, a decentralized network of small language model agents that learn to communicate effectively during inference. Each agent proposes an answer, scores it with a confidence head, and the most confident agent broadcasts a hint; lower‑confidence agents revise their proposals if a new suggestion scores higher. This gossip‑based consensus, trained via group relative policy optimization, enables a mesh of three agents to match the accuracy of majority voting over 32 samples, and scales to larger meshes to significantly boost performance on benchmarks like GSM8K and MATH-500.
arXiv:2608. 14375v1 Announce Type: new Abstract: Multi-agent reasoning systems often use agreement, confidence, or automated scores to decide which messages should shape a final answer.
arXiv:2610.35694v1 Announce Type: new Abstract: Conversational QA systems should fix errors when users push back and signal which answers to trust. We split each follow-up into its speech act (re-ask...