Coverage–restraint trade-off for fixed-weight LLM agents

Investigate the coverage–restraint trade-off between reducing missed intervention opportunities and avoiding off-topic contributions in fixed-weight large language model agents.

Background

The CAPA architecture substantially reduces silent abstention by enabling the delegate to intervene more often, while maintaining low hallucination and redundancy rates. However, the results also show an increase in off-topic contributions relative to the transcript-only baseline. The authors identify the resulting balance between broader coverage and restrained, context-appropriate participation as an unresolved design issue for fixed-weight LLM agents. The problem concerns how such agents can increase recovery of relevant participant-owned ideas without sacrificing topical precision.

References

Whether the models fail to notice the problem or notice it and remain silent is an open question, and the matched-context protocol introduced here provides a way to study it.

— Full-Duplex Speech Models Take the Floor When Asked, Not When Needed  (2609.19596 - Peng et al., 17 Sep 2026) in Section Conclusion (after Section 4.3, page not specified)

Off-topic rises by $5.2\%$ per episode against $-48.9\%$ not-attempted, the coverage--restraint trade-off that remains an open design question for fixed-weight LLM agents \citep{zhu-etal-2025-grait}.

— Speak for Me: Giving LLMs the Situational Awareness to Participate in a Meeting  (2609.03923 - Khan et al., 3 Sep 2026) in Section 5.1, Results, subsection “Coverage and Output Quality,” paragraph “Grounding preserved”

These retrospective preferences still provide a meaningful target for when recipients consider an intervention appropriate, but they cannot establish whether a live intervention would improve the discussion, since it may alter what follows.

— When Should AI Chime In? Benchmarking Timely Contribution of Proactive Agents in Multi-Party Chat  (2610.25896 - Wang et al., 19 Oct 2026) in Section Discussion, Limitations, and Future Work; subsection “What does the retrospective reference measure?”