arXiv:2608.14375v2 Announce Type: replace-cross Abstract: A solution can end with a wrong answer and still help another solver. In multi-agent reasoning, evaluating only a message's proposed answer can therefore miss its contribution to the receiver. We measure this contribution as trajectory value: the change in a fixed integrator's probability of answering correctly when a cached message is present rather than removed. Controlled replay across five benchmarks and two model families separates message effects from identical-input variation. The strongest evidence is for Gemma: in a pre-registered fresh-call re-test, 34 of 73 selected wrong-answer messages pass individual confirmation, exceeding control-calibrated upper bounds on expected false confirmations of 11.4--20.8. The finding is benchmark-dependent and the conservative criterion no longer passes without SciBench. A blinded two-author audit supports the selected answer labels, while neutral replacements show that the helpful effect is not explained solely by message length and position. On held-out replay blocks of the same problems, a message-specific removal rule improves accuracy over the strongest tested correctness-only rule by 1.61 percentage points [1.01, 2.25]. Post hoc analysis localizes most of this gap to pools containing no correct proposal. Evidence for gpt-oss is weaker, and the tested predictors do not establish a practical way to identify useful errors in advance. The results support measuring answer correctness and receiver benefit separately, with value understood as conditional on the message pool, receiver and generation format.