Figure 1: GUIDE overview. (a) Three participants interact with an AI facilitator. (b) Each group completes open-ended discussion, multi-party negotiation, and consensus-building tasks in random order. (c) Evaluation covers participant experience, facilitator ratings, and task-specific outcomes. In this study, we evaluate whether an LLM agent self-learn a generalized facilitation strategy capable of navigating diverse group dynamics across different types of collaborative tasks. We introduce GUIDE (Generalized Unified Intervention for Dynamic Exchanges): a self-learning framework that develops generalized facilitation strategies, informed by a corpus of large-scale real-world human conversations guided by task-specific expert facilitators. We evaluate the learned facilitation in a real-time empirical evaluation involving three types of tasks — open-ended discussion, multi-party negotiation, and consensus-building — each with distinct interaction goals and outcome structures. In addition to optimizing for (1) outcome-level metrics (such as whether a deal was achieved in the negotiation setting or whether participants could agree during consensus-building), the framework evaluates (2) subjective participant experience and (3) subjective perceptions around the AI facilitator (see Table [1](https://arxiv.org/html/#S1.T1 "Table 1 ‣ 1 Introduction ‣ Learning to GUIDE the Group: Self-Learning LLM Facilitation Across Multi-Party Tasks") for comparison with previous work). Concretely, the framework produces a *playbook*: an explicit, human-readable set of facilitation rules that an LLM revises on its own, generation after generation, informed by a corpus of real, facilitated multi-person discussions we collected as learning material. 出典: Learning to GUIDE the Group: Self-Learning LLM Facilitation Across Multi-Party Tasks（arXiv）Figure 2: Self-learning framework. (a) Real human conversations collected with the expert prompts forms the training corpus. (b) A conversation reflector extracts lessons from human experience; a curator edits the playbook; an aligned LLM judge compares the candidate with the preceding playbook; rule-level credit attributes each cited rule’s contribution; and a validation reflector converts validation failures into new lessons for the next cycle.Prior self-evolving approaches typically rely on an explicit reward, verifier, or other deterministic signal to determine whether a policy update is beneficial ([Zhang et al., 2026](https://arxiv.org/html/#bib.bib29); [Liu et al., 2023](https://arxiv.org/html/#bib.bib18); [Kong et al., 2026](https://arxiv.org/html/#bib.bib17)). Facilitation offers no such signal: whether and when an intervention is helpful depends on human perceptions, conversational dynamics, and downstream group outcomes. We therefore adapt context-evolution approaches to this setting by grounding self-improvement in a corpus of real human interactions. Our framework first reflects on these interactions to discover facilitation insights, then iteratively refines them using an LLM judge aligned with human preferences. 出典: Learning to GUIDE the Group: Self-Learning LLM Facilitation Across Multi-Party Tasks（arXiv）
