Multi-agent large language model (LLM) systems often rely on independent solvers, critique, debate, or answer aggregation. While these designs increase diversity and feedback, they do not necessarily create effective group reasoning: information may be poorly shared, expertise underused, discussions may drift, and consensus may emerge before unresolved issues are examined. We introduce AgendaFlow, a meeting-science-inspired framework that treats multi-agent reasoning as a structured meeting rather than parallel answering or generic debate. AgendaFlow routes each task to a suitable agenda, instantiates complementary functional roles, and uses a moderator to manage phases, speaking order, shared artifacts, unresolved issues, and completion checks. This enables agents to pool information, surface constraints, compare alternatives, repair errors, and converge through a managed collective decision process. Across five reasoning, planning, and rule- or evidence-based benchmarks, AgendaFlow consistently outperforms single-agent, sampling-based, debate-style, and multi-agent orchestration baselines. Ablations, token-matched comparisons, and process-level analyses suggest that the gains come from meeting-level organization rather than agent diversity or additional computation alone, highlighting meeting design as a missing dimension in multi-agent LLM systems. Code is available at: https://github.com/sunming-code/agendaflow.

The keyphrase generation task is a challenging work that aims to generate a set of keyphrases for a piece of text. Many previous studies based on the sequence-to-sequence model were used to generate keyphrases, and they introduce a copy mechanism to achieve good results. However, we observed that most of the keyphrases are composed of some important words (seed words) in the source text, and if these words can be identified accurately and copied to create more keyphrases, the performance of the model might be improved. To address this challenge, we propose a DualCopyNet model, which introduces an additional sequence labeling layer for identifying seed words, and further copies the words for generating new keyphrases by dual copy mechanisms. Experimental results demonstrate that our model outperforms the baseline models and achieves an obvious performance improvement.