Technical Summary: Structured yet Bounded Temporal Understanding in Large Language Models

Problem Statement

While Large Language Models (LLMs) demonstrate strong performance on temporally grounded tasks (e.g., timeline construction, temporal QA), it remains unclear how their temporal reasoning depends on the linguistic and cognitive framing of time. Existing approaches often treat time as absolute timestamps or discrete relations, failing to account for how LLMs, trained on natural language, inherit linguistically encoded temporal abstractions. Specifically, the field lacks a systematic understanding of how LLMs adapt to different Temporal Frames of Reference (t-FoRs)—distinguishing between deictic framing (anchored to an experiencer's "now," yielding past/present/future) and sequential framing (anchored to a specific event, yielding before/after). Furthermore, the influence of structural factors such as temporal distance, interval topology (Allen's Interval Algebra), and event duration on LLM temporal representations is not well characterized.

Methodology

The authors propose a frame-sensitive evaluation framework utilizing a similarity judgement task to probe LLM temporal cognition.

1. Dataset Construction

• Source: Real-world events extracted from Wikidata, resulting in a dataset of 49,956 events spanning from 0001 to 2100.
• Anonymization: To prevent reliance on memorized historical knowledge, event names were replaced with rare animal names while preserving start and end timestamps.
• Sampling: A balanced subset of 321 events was selected to ensure coverage of all 13 Allen's Interval Algebra relations.
• Scale: The final dataset contains ~2.9 million entries for the deictic t-FoR and ~51,000 entries for the sequential t-FoR.

2. Experimental Design

The study evaluates two open-source LLMs: Qwen3-4B and Qwen3-30B.

• Deictic t-FoR: Reference points are generated as a sequence of "now" timestamps ($\tau$) relative to the experiencer. Data pairs are $(\tau, e_i)$, where $e_i$ is a target event.
• Sequential t-FoR: Reference points are specific events ($e_j$). Data pairs are $(e_i, e_j)$, establishing relative ordering.
• Task: Models are prompted to output a similarity score $s \in [-1.00, 1.00]$ indicating how similar the reference time/event is to the target event.
• Metrics:
  • Mean Similarity vs. Temporal Distance: Analyzed as a function of signed distance (days).
  • Interval-Specific Variance: Grouped by Allen's relations to measure stability.
  • Duration Interaction: Analyzed via heatmaps of (distance, duration) bins.

Key Results

1. Adaptation to Temporal Frames of Reference

LLMs systematically adapt their behavior to the chosen t-FoR, but the resulting patterns differ significantly:

• Deictic t-FoR: Similarity scores form a graded, asymmetric structure centered on the present.
  • Future: Scores drop sharply and converge to -1.00 with low variance, indicating a compressed, uniform representation.
  • Past: Scores decline more gradually with substantially higher variance, suggesting a richer but less stable representation of past events.
• Sequential t-FoR: Similarity scores exhibit strong polarization. Once events are temporally separated (non-zero distance), scores rapidly collapse to strongly negative values. Variance is concentrated only near zero distance (where multiple interval relations like overlaps or meets coexist).

2. Influence of Interval Structure (Allen's Algebra)

• Stable Relations: Strictly ordered relations (before, after) and boundary-aligned relations (meets, met-by, equals) yield consistent, polarized scores.
• Unstable Relations: Overlap and containment relations (e.g., overlaps, during, contains) exhibit significantly higher variance. This indicates that LLMs struggle with structurally ambiguous temporal topologies, particularly in the sequential frame.

3. Interaction of Duration and Temporal Distance

• Deictic t-FoR (Past Only): A distinct duration effect is observed for past events. Long-duration past events receive higher similarity ratings than short-duration events at comparable distances. In some cases, distant long-duration events are rated as more similar than nearby short-duration events.
• No Duration Effect: This modulation does not occur for future events in the deictic frame, nor does it appear under the sequential t-FoR, where similarity remains polarized regardless of duration.

Key Contributions

1. Frame-Sensitive Evaluation Framework: Introduced a benchmark contrasting deictic and sequential t-FoRs using similarity judgements on real-world events, moving beyond simple factual correctness.
2. Large-Scale Temporal Dataset: Created a Wikidata-derived dataset preserving interval structure and duration, enabling systematic analysis of temporal distance, Allen's algebra, and duration at scale.
3. Empirical Characterization of Temporal Behavior: Provided evidence that LLM temporal understanding is frame-dependent and shaped by structural factors, specifically identifying:
   • Past-future asymmetry under deictic framing.
   • Rapid similarity collapse under sequential framing.
   • Elevated instability in overlap-based relations.
   • Duration effects restricted to past events under deictic anchoring.

Significance and Claims

The paper argues that LLMs possess a structured yet bounded form of temporal understanding.

• Structured: LLMs do not treat time randomly; they adapt coherently to different reference structures (deictic vs. sequential) and are sensitive to interval geometry and duration.
• Bounded: This understanding is not a global, explicit temporal metric. It is constrained by the linguistic and cognitive regularities encoded in natural language.
  • Representations are richer in the past than in the future.
  • Representations are more stable for cleanly ordered relations than for overlapping intervals.
  • The past-future asymmetry and the selective duration effect suggest that LLMs do not possess a symmetric, objective sense of time but rather a perspectival one anchored to the "experiencer" or specific events.

The authors conclude that temporal understanding in LLMs is likely shaped by linguistically acquired regularities rather than explicit temporal inference mechanisms, manifesting as a graded, context-anchored similarity space that adapts to framing but does not collapse into a single global metric.

Limitations

The study is currently limited to English text. The choice of t-FoR taxonomies is based on Evans (2013) to complement prior work, though the paper acknowledges there is no universal consensus on which taxonomy best describes event relations.