LLM Agent Collaboration Needs Trust Framing

Trust is a cornerstone of human cooperation. Does it matter similarly when large language model (LLM) agents collaborate? We raise this question because of a surprising pattern in sequential multi-agent systems: receivers often ignore partner messages that contain exactly the information they lack. We call this pattern information neglect. In multi-hop question answering (QA) with distributed evidence, it accounts for $51.3$ of audited failures. We first tried to improve the message itself. Rewriting, compressing, structuring, and routing partner messages did not reliably help, and several variants hurt performance. This points to the receiver as the source of the problem. We therefore introduced Importance Framing (IF), which increases the weight a receiver gives to the partner message. IF helps on clean messages. Averaged across corrupted-message conditions, however, it falls slightly below the unframed baseline. We call this the importance dilemma. It arises because importance is a first-order notion: it scales attention to the whole message uniformly, so it amplifies correct and incorrect content alike. Drawing on social-science accounts of trust, we argue that trust is second-order. It governs how attention should be allocated by judging both whether the source is reliable and which parts of its message merit reliance. We operationalize this view in Trust Framing (TF), a training-free protocol that places source and task-context cues immediately before a partner message and requires no extra LLM calls. TF improves F1 by $$\begin{gathered} +9.5 \\ mathrmpp \end{gathered}$$ on multi-hop QA and task-completion rate by $$\begin{gathered} +7.6 \\ mathrmpp \end{gathered}$$ on the cooperative game Collab-Overcooked (both $p<.001$). In a factorial ablation, the trust cue alone accounts for $76$ of the full gain. TF also outperforms the unframed baseline under every message-corruption condition tested. These results suggest that the concept of trust itself shapes how LLM agents use partner information: effective collaboration requires appropriate reliance on partner messages, not just more attention to them.