mlsanityData to 14 Oct 2026

Is the Future Predictable? Modeling Future Predictability for Reliable Temporal Reasoning

arXiv 2610.14073 ↗First posted 8 Oct 2026cs.CL cs.AIcited by 0

On the map: LLM reasoning → Time series reasoning also fits: Test-time compute and efficient thinking

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Time series reasoning, one of 17 directions in LLM reasoning. Builds on 15 of its 29 references that are in the map. First posted on arXiv 8 Oct 2026.

Builds on

15 of 29 references.

References · 15

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2025Qwen3 Technical Reportcited by 8,820
  3. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  4. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 318
  5. 2021Time-Aware Language Models as Temporal Knowledge Basescited by 296
  6. 2022RealTime QA: What's the Answer Right Now?cited by 244
  7. 2024Large Language Models Can Learn Temporal Reasoningcited by 111
  8. 2024Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoningcited by 88
  9. 2019Joint Reasoning for Temporal and Causal Relationscited by 62
  10. 2025FutureX: An Advanced Live Benchmark for LLM Agents in Future Predictioncited by 59
  11. 2023Do Large Language Models Know about Facts?cited by 51
  12. 2024Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMscited by 24
3 more
  1. 2024Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphscited by 17
  2. 2023Once Upon a textitTime in textitGraph: Relative-Time Pretraining for Complex Temporal Reasoningcited by 11
  3. 2026When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?cited by 7

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  1. 2023Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models
  2. 2025TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios
  3. 2026TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Tasks
  4. 2025Time-R1: Towards Comprehensive Temporal Reasoning in LLMs
  5. 2026WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning
  6. 2026Distilling Temporal Search and Reasoning: Evolving LLMs for Future Prediction via Harness-Assisted Efficient Data Synthesis
  7. 2024Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time
  8. 2025Temporalizing Confidence: Evaluation of Chain-of-Thought Reasoning with Signal Temporal Logic