arXiv cs.AI· Yongyi Huang, Huaping Zhang, Wenyao Cui, Qiuchi Li, Lei Li, Baohua Zhang·· 3 天前AI 评分29
ChronoRegime：面向未来时间推理的可预测性建模
Is the Future Predictable? Modeling Future Predictability for Reliable Temporal Reasoning
AI 导读
针对 LLM 在未来时间推理中常忽略未来状态差异、对本质易变的事实仍强行给出确定答案的问题，研究者提出 ChronoRegime 任务与 Confidence-Aware Temporal Reasoning（CTR）框架，从时间动态中估计未来可预测性并据此调节作答承诺。
正文 · 原文
Abstract:Large language models (LLMs) remain unreliable when reasoning about the future. Existing models often overlook the difference among future states, committing to specific answers even when the queried facts are inherently volatile. To capture this challenge, we introduce ChronoRegime, a future-oriented temporal reasoning task that requires models to determine whether historical temporal evidence justifies commitment to a future answer. Furthermore, we propose Confidence-Aware Temporal Reasoning (CTR), a framework that estimates future predictability from temporal dynamics and regulates answer commitment accordingly. Rather than performing unconditional forecasting, CTR commits only when the reconstructed evidence supports the target future state. Across multiple LLMs, CTR improves strict overall accuracy by 6.7 percentage points and reduces hallucination rates by 18.4 percentage points on average over the plain QA baseline. These results demonstrate that explicit predictability assessment is an effective mechanism for safer and more reliable temporal reasoning under future uncertainty.
| Comments: | Accepted to EMNLP 2026 (Main Conference) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.14073 [cs.CL] |
| (or arXiv:2610.14073v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14073 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Baohua Zhang [view email]
[v1]
Thu, 8 Oct 2026 03:46:15 UTC (2,812 KB)
来源：arXiv cs.AI · arxiv.org