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arXiv — NLP / Computation & Language · · 4 min read

Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages

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Computer Science > Artificial Intelligence

arXiv:2608.14375 (cs)
[Submitted on 14 Aug 2026]

Title:Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages

View a PDF of the paper titled Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages, by Chih-Hsuan Yang and 9 other authors
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Abstract:Multi-agent reasoning systems often use agreement, confidence, or automated scores to decide which messages should shape a final answer. Such filtering assumes that a message likely to be correct is also worth keeping. Yet a wrong answer can contain a useful decomposition, constraint, or scientific principle. We test this distinction with Diverse Hypothesis Deliberation (DHD), a controlled measurement protocol that caches five independently generated messages and replays the same downstream solver, called the integrator, with each message available or hidden. The replay comparison measures a message's trajectory value: whether making the message available helps or harms subsequent reasoning. Across five mathematics and science benchmarks and two openly available model families, gpt-oss-120b and gemma-4-31B-it, wrong-helpful messages appear in every benchmark-model combination. Among wrong-answer messages that change final correctness, more than four in ten changes are helpful in each model. Controlled repeats show that the number of repeatable message effects is unlikely to arise from replay variation alone (p=0.0002). A focused intervention on repeatable wrong-helpful messages finds that the complete message works best, while retaining its reasoning preserves more success than retaining only its answer; the source of the complete-message advantage remains open. Within the same problem, repeated trajectory-value evidence also identifies a better keep-or-remove choice than answer correctness alone. Answer correctness is therefore informative but does not determine trajectory value. DHD measures this missing property and produces reusable labels for learning when agents should listen.
Comments: 24 pages, 9 figures. Includes an appendix and an ancillary reproducibility artifact
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2608.14375 [cs.AI]
  (or arXiv:2608.14375v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2608.14375
arXiv-issued DOI via DataCite (pending registration)

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From: Chih-Hsuan Yang [view email]
[v1] Fri, 14 Aug 2026 15:16:57 UTC (6,010 KB)
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