The World Leaks the Future: Harness Evolution for Future Prediction Agents

Computer Science > Artificial Intelligence [Submitted on 17 Apr 2026] The World Leaks the Future: Harness Evolution for Future Prediction Agents Chuyang Wei (1 and 2), Maohang Gao (1 and 2), Zhixin Han (2), Kefei Chen (2 and 3), Yu Zhuang (2), Haoxiang Guan (1 and 2), Yanzhi Zhang (2), Yilin Cheng (2), Jiyan He (2), Huanhuan Chen (1), Jian Li (3), Yu Shi (2), Yitong Duan (2), Shuxin Zheng (2) ((1) University of Science and Technology of China, (2) Zhongguancun Academy, Beijing, China, (3) Tsinghua University) Many consequential decisions must be made before the relevant outcome is known. Such problems are commonly framed as \emph{future prediction}, where an LLM agent must form a prediction for an unresolved question using only the public information available at the prediction time. The setting is difficult because public evidence evolves while useful supervision arrives only after the question is resolved, so most existing approaches still improve mainly from final outcomes. Yet final outcomes are too coarse to guide earlier factor tracking, evidence gathering and interpretation, or uncertainty handling. When the same unresolved question is revisited over time, temporal contrasts between earlier and later predictions can expose omissions in the earlier prediction process; we call this signal \emph{internal feedback}. We introduce \emph{Milkyway}, a self-evolving agent system that keeps the base model fixed and instead updates a persistent \emph{future prediction harness} for factor tracking, evidence gathering and interpretation, and uncertainty handling. Across repeated predictions on the same unresolved question, \emph{Milkyway} extracts internal feedback and writes reusable guidance back into the harness, so later predictions on that question can improve before the outcome is known. After the question is resolved, the final outcome provides a \emph{retrospective check} before the updated harness is carried forward to subsequent questions. On FutureX and FutureWorld, Milkyway achieves the best overall score among the compared methods, improving FutureX from 44.07 to 60.90 and FutureWorld from 62.22 to 77.96. Comments: 15 pages, 3 figures, 6 tables Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.15719 [cs.AI]   (or arXiv:2604.15719v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.15719 Focus to learn more Submission history From: Chuyang Wei [view email] [v1] Fri, 17 Apr 2026 05:43:07 UTC (730 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)