LLM meeting agents gain situational awareness to recognize when to speak
Meeting delegation remains a blind spot for LLM agents: they miss over half of contextually appropriate moments to contribute because they lack structured awareness of conversational dynamics. CAPA introduces a modular architecture that tracks meeting state through a Perceiver, forecasts conversational trajectory, and gates agent participation through a Controller that learns participant communication style. The system adds a feedback loop via dual judges and recalibration, moving beyond prompt-only approaches toward agents that understand floor control, topic coverage, and stance. This addresses a practical gap in agentic AI: real-time situational reasoning in multi-party settings where silence is often as costly as interruption.
Modelwire context
ExplainerThe paper's core contribution isn't just detecting when to speak, but learning *how* to speak like each participant. The Controller module that adapts to individual communication style is what separates this from prior work that treats meeting dynamics as a generic optimization problem.
This connects directly to two threads in recent coverage. The disclosure-gating work from early September showed that LLM agents fail when they don't understand realistic social constraints (user resistance, information withholding). CAPA extends that insight into group settings, treating floor control and stance alignment as learned constraints rather than hard rules. Separately, the HarnessDev benchmark from the same period measured whether agents could design their own execution infrastructure. CAPA does something adjacent: it builds infrastructure (the Perceiver, Controller, dual judges) that lets agents reason about *when* to execute, not just what to execute. Both papers treat agent autonomy as requiring structural self-awareness, not just better prompting.
If CAPA's performance holds when tested on real meeting corpora outside the AMI dataset (like ICSI or internal enterprise recordings), that confirms the learned communication patterns generalize. If performance drops significantly, the model is overfitting to AMI's specific participant dynamics, and the approach needs domain adaptation before deployment claims hold.
Coverage we drew on
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MentionsCAPA · AMI corpus · LLM agents
Modelwire Editorial
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Speak for Me: Giving LLMs the Situational Awareness to Participate in a Meeting”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.