菜单
Talk-frequency awareness, anti-repetition rules, and natural conversation pacing derived from LLMafia dual-module conversation system.
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基于 SOC 职业分类
Good vs. bad behavioral examples showing what excellent play looks like vs. common mistakes. Derived from xuyuzhuang-Werewolf contrastive prompting and Avalon-LLM self-play reflection.
Faction-conditional strategies for deception and detection, derived from xuyuzhuang-Werewolf and LLMWereWolf role strategies. Evil agents get deflection and alibi techniques; good agents get detection heuristics.
3-tier memory tracking (STM/Episodic/Semantic) derived from ai-traitors-v2 and ai-town generative agent reflection. Helps agents maintain beliefs about each player and synthesize round memories into strategic conclusions.
Enriches GameMaster narration with awareness of social dynamics, derived from Sotopia 7-dimensional social intelligence evaluation. Tracks influence shifts, trust erosion, and narrative tension cues.
Internal 5-step reasoning pipeline derived from xuyuzhuang-Werewolf. Agent performs Situation-Suspicion-Reflection-Strategy-Response analysis implicitly before generating any output.
Good vs. bad behavioral examples showing what excellent play looks like vs. common mistakes. Derived from xuyuzhuang-Werewolf contrastive prompting and Avalon-LLM self-play reflection.
| id | discussion_dynamics |
| name | Discussion Dynamics & Pacing |
| description | Talk-frequency awareness, anti-repetition rules, and natural conversation pacing derived from LLMafia dual-module conversation system. |
| tags | ["pacing","conversation","dynamics"] |
| targets | ["character_agent","spontaneous_reaction"] |
| priority | 40 |
| dependencies | [] |
| conflicts | [] |
| behavioral_rules | ["Vary response length based on how much you've already spoken this round","Never repeat an accusation or observation without adding new supporting evidence","Address other players by name in conversation","React to the most recent messages, not old topics"] |
