mlsanityData to 14 Oct 2026

Chronocept: Instilling a Sense of Time in Machines

arXiv 2505.07637 ↗First posted 12 May 2025cs.CL cs.AI cs.LGcited by 3

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Abstract (arXiv)

Human cognition is deeply intertwined with a sense of time, known as Chronoception. This sense allows us to judge how long facts remain valid and when knowledge becomes outdated. Despite progress in vision, language, and motor control, AI still struggles to reason about temporal validity. We introduce Chronocept, the first benchmark to model temporal validity as a continuous probability distribution over time. Using skew-normal curves fitted along semantically decomposed temporal axes, Chronocept captures nuanced patterns of emergence, decay, and peak relevance. It includes two datasets: Benchmark I (atomic facts) and Benchmark II (multi-sentence passages). Annotations show strong inter-annotator agreement (84% and 89%). Our baselines predict curve parameters - location, scale, and skewness - enabling interpretable, generalizable learning and outperforming classification-based approaches. Chronocept fills a foundational gap in AI's temporal reasoning, supporting applications in knowledge grounding, fact-checking, retrieval-augmented generation (RAG), and proactive agents. Code and data are publicly available.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 49 references that are in the map. First posted on arXiv 12 May 2025.

Builds on

14 of 49 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
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  7. 2019To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Taskscited by 167
  8. 2021Time Waits for No One! Analysis and Challenges of Temporal Misalignmentcited by 91
  9. 2021Evaluating the Robustness of Neural Language Models to Input Perturbationscited by 87
  10. 2021Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuningcited by 82
  11. 2018A Multi-Axis Annotation Scheme for Event Temporal Relationscited by 75
  12. 2020TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questionscited by 65
2 more
  1. 2023An Overview Of Temporal Commonsense Reasoning and Acquisitioncited by 10
  2. 2020Building Proactive Voice Assistants: When and How (not) to Interactcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Can LLMs Perceive Time? An Empirical Investigationcited by 3
  2. 2026Memory Consolidation Flattens the Temporal Shape of User Factscited by 1
  3. 2026Chronofy: A Temporal-Logical Decay Architecture for Information Validity in Time-Aware Retrieval-Augmented Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Chronocooked: A Benchmark for Interval Timing in Reinforcement Learning Agents
  2. 2023TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models
  3. 2026Chronological Blindness: Benchmarking Temporal Reasoning in Vision-Language Models with CHRONOSIGHT
  4. 2026Chronos: Learning Temporal Dynamics of Reasoning Chains for Test-Time Scaling
  5. 2026Is the Future Predictable? Modeling Future Predictability for Reliable Temporal Reasoning
  6. 2025TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios
  7. 2025Discrete Minds in a Continuous World: Do Language Models Know Time Passes?
  8. 2025Temporal Reasoning in AI systems