PreSage: Pre-release anticipation of public-figure Statement impact by multi-agents

Pre-release anticipation of adverse impacts from public-figure statements supports proactive risk governance, yet prior work focuses on post-circulation intervention with limited consequence types and lacks multi-dimensional impact datasets. Via LLM-assisted annotation and human adjudication on decade-long real-world cases, we construct a 6×6 impact space and PreSage-Bench for public-figure statement risk assessment. PreSage-Bench contains 1,012 real cases covering 943 public figures (2015–2026) across six social scenarios and enables genuine foresight risk evaluation instead of retrospective post-hoc reasoning. Furthermore, we propose PreSage, a self-improving multi-agent system for pre-assessing statement-induced risks. It integrates nine role agents, an Evidence Judge, and an Impact Judge for joint prediction. The system distills recurring prediction errors from finished cases into generalized rules to refine agents’ skills and prompts. Critically, all predictions rely only on information available before statement release. Evaluated on 250 post-2025 test cases with valid outputs from all baselines, PreSage achieves a micro precision of 0.677 and micro recall of 0.586. Compared with the recall (0.318) of directly using LLM prompting, our PreSage can support genuine foresight risk evaluation of public-figure statements rather than retrospective reasoningThe dataset and code will be released after the paper is accepted..