ForeWeave: Learning What to Investigate for Event Forecasting

Forecasting future events requires agents to acquire evidence before decisive outcomes are known. Yet an apparently plausible forecast can still depend on consequential conditions that remain unresolved. We introduce ForeWeave, a framework for process-guided adaptive investigation that connects reusable structures from historical investigations with evidence gaps revealed during a current investigation. ForeWeave induces historical process structures to guide three view-specific investigations, then performs targeted continuation from a selected trajectory prefix when an unresolved issue could change the forecast. A Forecast Synthesis Agent audits the initial and continued records, performs targeted verification when blocking evidence gaps remain, and returns the final answer and probability. We evaluate ForeWeave on FutureX-Past with Qwen, DeepSeek, and GPT backbones, and on ForeWeave-Live with DeepSeek and GPT. Across both benchmarks, ForeWeave achieves the highest Overall Score, outperforming the strongest baseline by 1.96–6.45 points. It achieves Overall Scores of 49.33, 56.66, and 59.51 on FutureX-Past with Qwen, DeepSeek, and GPT, respectively, and 70.59 and 67.65 on ForeWeave-Live with DeepSeek and GPT.