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REVIEW 3 major objections 5 minor 1 cited by

ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that current LLMs lack robust temporal understanding of Allen interval relations, as shown by accuracies near or below 60 percent and a drop when real event names are replaced by abstract labels, which the authors…

desk verdict A genuinely new benchmark, but the year-granularity labeling makes most of the per-relation results uninterpretable. read the letter →

arxiv 2501.03040 v2 pith:OGRCAYOB submitted 2025-01-06 cs.LG cs.CL

Duygu Sezen Islakoglu , Jan-Christoph Kalo This is my paper · ORCID
classification cs.LGcs.CL
keywords temporalreasoningAllenintervalalgebralargelanguagemodelsbenchmarkWikidatamemorizationchain-of-thoughtpromptingarithmetic
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper presents ChronoSense, a benchmark that tests large language models on all thirteen Allen interval relations between pairs of events, plus three temporal arithmetic tasks. It evaluates seven widely used LLMs in zero-shot, few-shot, chain-of-thought, and abstract-label settings. The central finding is that models perform poorly and inconsistently on Allen relations, with several scoring below 0.60 accuracy, and that replacing real event names with abstract labels lowers accuracy for most models, which the authors take as evidence of memorization rather than reasoning. A sympathetic reader would care because temporal understanding underpins applications like historical analysis, legal AI, and medical timelines, and the paper provides a diagnostic tool for measuring it.

What carries the argument

The central object is Allen's interval algebra, the set of 13 mutually exclusive relations that can hold between two time intervals (Equals, Before, After, Overlaps, Overlapped-by, Contains, During, Starts, Started-by, Finishes, Finished-by, Meets, Met-by). The benchmark verbalizes each relation as a natural-language Hypothesis, presents pairs of event intervals from Wikidata, and asks the model to judge the statement True or False. The argument's engine is the controlled comparison between named events and abstract events ('Event A'/'Event B'): the accuracy gap between those two conditions is what carries the memorization claim.

What would settle it

Take only the event pairs where the two intervals are unambiguous at year granularity, for example Before pairs with a gap of several years, and compare model accuracy on those against accuracy on boundary-sharing pairs such as identical start years. If models perform near chance on the unambiguous pairs too, the paper's claim of poor temporal reasoning is confirmed; if they score high there but low on ambiguous pairs, the benchmark's labels, not the models' reasoning, are the problem.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that current large language models do not robustly reason about the time intervals of events. Using the 13 mutually exclusive Allen relations, ChronoSense asks models to answer True or False about whether a described relation holds between two events whose start and end years are given. The results show that one model, GPT-4o, reaches 0.87 zero-shot accuracy, while most other models remain near or below 0.60, and that the models treat symmetrical relations (e.g., Before vs After, Meets vs Met-by) asymmetrically. When the real event names are replaced with abstract labels, accuracy drops for most models, and the paper interprets this gap as a sign that the models lean on memorized event knowledge rather than computing the relation from the stated intervals. The authors conclude that temporal understanding in LLMs needs improvement and offer ChronoSense as a benchmark for future research.

Load-bearing premise

The dataset assumes that the start and end years given for each event are sufficient to determine the correct Allen relation, even though relations like Equals, Starts, and Finishes depend on exact dates, not just years.

Editorial extensions

If this is right

  • The benchmark gives researchers a way to test all 13 Allen relations instead of the subsets covered by earlier temporal reasoning datasets.
  • Chain-of-thought prompting lifts temporal arithmetic accuracy substantially for nearly all tested models, suggesting step-by-step reasoning is effective for calculation-style temporal questions.
  • The named-versus-abstract accuracy gap implies that evaluations using only real-world events may overstate genuine temporal reasoning ability.
  • Models' asymmetric performance on symmetric relation pairs such as Before/After and Meets/Met-by shows that relation labels carry lexical biases that should be controlled in future benchmarks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the memorization finding holds, then temporal QA benchmarks built on encyclopedic events should always include abstract or synthetic controls to separate recall from reasoning.
  • A natural next experiment is to generate synthetic event pairs with random names and day-level timestamps; if the abstract-accuracy gap persists, it would confirm the effect is not an artifact of Wikidata naming.
  • The year-granularity ambiguity for boundary-sharing relations means some reported accuracies may be conservative lower bounds; re-annotating with finer dates or excluding ambiguous pairs could change model rankings.
  • The novel Intermediate Timepoint task, which asks whether an event was happening at a given year, could be extended to continuous intervals and used as a probe for interval-membership reasoning separate from arithmetic.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. ChronoSense introduces a benchmark for evaluating LLMs on Allen's 13 interval relations and three temporal arithmetic tasks, using real event intervals from Wikidata plus abstract versions, across 0-shot, few-shot, and chain-of-thought prompting. The authors evaluate seven LLMs and report generally low accuracy on Allen relations, uneven performance across symmetric relations, and a drop in accuracy when real event names are replaced by abstract labels, which they interpret as evidence that models rely on memorization. The dataset and code are released.

Significance. If the measurement were clean, the benchmark would fill a genuine gap: most prior temporal-reasoning benchmarks cover only a subset of Allen relations, whereas ChronoSense covers all 13 and releases data and code, which is a useful resource for the community. The paper also provides a broad comparison across seven open and closed models and multiple prompting regimes. However, the load-bearing measurement is compromised by the year-granularity problem: for many items the provided context does not determine the gold Allen relation, so the accuracy numbers and the memorization comparison are not cleanly interpretable. The paper's own qualitative example (Appendix A.5, Example #1) shows a model correctly noting this ambiguity, which underscores that the issue is real rather than hypothetical.

major comments (3)
  1. [Section 3 and A.1.1] The core technical issue is that year-level start/end values do not determine the true Allen relation for any pair in which a boundary year coincides. For example, two events both stated as occurring 'between year 1900 and year 1900' may be Equals, Before, After, Meets, Overlaps, or other relations depending on the actual days; similar underdetermination affects Starts, Finishes, Started-By, Finished-By, Contains, During, Overlaps, Overlapped-By, Meets, and Met-By whenever a start or end year is shared. The construction in Section 3 ('We set the time granularity to years') and the positive-label generation in A.1.1 therefore assign a unique gold relation where none is entailed by the context. The negative-sample exclusions in A.1.1 address only the pool of false hypotheses; they do not repair positive labels. Because the reported accuracies for these relations (Tables 2, 4-8) include items with indeterminate labels, the headline low accuracies for relations such as Equals, Starts, and Finishes are not a valid measure of Allen-relation reasoning. The fix is to filter items to those where the year-level interval uniquely entails the relation, or to use finer-grained dates.
  2. [General Findings, item 6 and Table 8] The memorization conclusion rests on comparing real-event accuracy (Table 4) with abstract-event accuracy (Table 8). This comparison is confounded: the abstract setting removes event names, but it also changes the distribution of the items (e.g., by removing any possibility of using real-world priors about event durations) and is only reported in the 0-shot setting, whereas the real-event results are reported across all settings. The observed drop could reflect the absence of useful topical priors or a format mismatch rather than memorization specifically. The authors should either provide a matched comparison that controls for these factors (e.g., using plausible but incorrect event names, or measuring memorization by probing the model's factual recall separately) or temper the claim in the abstract and conclusion that models 'may rely on memorization.'
  3. [A.5, Example #1] The paper's own qualitative example illustrates the label-validity problem: GPT-4o-mini answers 'True' to a Finishes question while stating that the exact dates are not specified, and the paper labels this a failure. In fact, given only the years, the model's expressed uncertainty is justified, and the gold label itself is not entailed. This example should either be removed or reframed; as presented, it supports the referee's concern rather than the paper's interpretation.
minor comments (5)
  1. [Table 10] The 'Contains' prompt variant 1 reads 'Does Event A fully enclose Event A...' but should refer to Event B; this typo appears in two places in the appendix table and could affect reproducibility if the templates are used verbatim.
  2. [Throughout] The relation name is written inconsistently as 'Met-by' in Table 3 and 'Met-By' in Tables 2, 4-8 and in the text; please standardize the capitalization.
  3. [Table 9 and A.2] Several typographical issues: 'Next-Occurence' should be 'Next-Occurrence', and the table header repeats 'End-Timepoint (0-shot)' style inconsistently with the text; also, in Table 11 the spacing around '0 .95' and similar entries appears as a LaTeX artifact that should be cleaned.
  4. [A.3] The sentence 'we do not observe a relation that has consistently high values' is unclear; the intended meaning appears to be that no relation has consistently low variance across prompt variants, or that no prompt variant consistently outperforms others. Please rephrase.
  5. [Section 4, General Findings item 1] The claim that 'random prediction would lead to 0.50 accuracy' is only true if the model always answers True or False; the paper's own asterisk convention shows that several models produce many unclear answers, so the random baseline should be stated conditionally or per-model.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the benchmark compares LLM outputs to Wikidata-derived labels, and the memorization claim is a post-hoc interpretation rather than a fitted quantity.

full rationale

No significant circularity found. ChronoSense is an evaluation benchmark: ground-truth labels are produced by the dataset construction step, not by the models being evaluated. Section A.1 states that the authors 'determine the valid Allen relation for this event pair by comparing the time intervals of these events,' and the models are then asked True/False questions against those externally generated labels. There is no fitted parameter that is later renamed as a prediction, and the abstract-versus-real comparison simply reports the same models' accuracies on two prompt variants; the memorization finding is an interpretation of the measured accuracy gap, not a quantity derived from that gap by construction. No load-bearing self-citation appears in the paper: the cited prior work consists of external benchmarks and methods, and the authors do not invoke their own prior results to justify the central claim. The main threat to interpretability is the year-granularity ambiguity discussed in Section A.1.1: because contexts specify only years, several Allen relations with shared boundary years are not entailed by the given context, and the paper excludes such problematic relations only when selecting negative candidates, not for positive labels. That is a benchmark-validity concern about whether some gold labels are determinate, but it is not a case of the paper's claimed results being equivalent to their inputs by construction or by a self-citation chain. The accuracy scores and the memorization comparison remain empirical measurements, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no fitted parameters or new physical entities. Its central evidential weight rests on the assumption that year granularity is sufficient to determine Allen relations, which is partially false, plus standard assumptions about Wikidata accuracy and evaluation scoring.

assumptions (5)
  • standard math Allen's interval algebra has 13 mutually exclusive relations that cover all interval pairs.
    Standard result from Allen (1989), cited in the Preliminaries section.
  • ad hoc to paper Year-level start and end values from Wikidata determine the true Allen relation between two events.
    Section 3 and A.1.1. This assumption is load-bearing and questionable when endpoints share a year.
  • domain assumption Wikidata event intervals are accurate enough to serve as ground truth.
    Section 3 and the Ethics Statement. The paper itself notes Wikidata may contain incorrect information.
  • domain assumption Treating outputs that are not exactly 'True' or 'False' as incorrect is a fair scoring rule.
    Section 4 and starred tables. The paper flags models with >=250 unclear answers but includes them in averages.
  • domain assumption The difference between named and abstract versions can be attributed to memorization.
    Section 4, General Findings item 6. This is an interpretation, not a controlled measurement of pretraining exposure.

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Cite this review

Pith. "Pith review of ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events." pith.science (2026). https://pith.science/paper/OGRCAYOB

@misc{pith2026250103040,
  author       = {Pith},
  title        = {Pith review of: ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OGRCAYOB}},
  note         = {Machine review of arXiv:2501.03040}
}
read the original abstract

Large Language Models (LLMs) have achieved remarkable success in various NLP tasks, yet they still face significant challenges in reasoning and arithmetic. Temporal reasoning, a critical component of natural language understanding, has raised increasing research attention. However, comprehensive testing of Allen's interval relations (e.g., before, after, during) -- a fundamental framework for temporal relationships -- remains underexplored. To fill this gap, we present ChronoSense, a new benchmark for evaluating LLMs' temporal understanding. It includes 16 tasks, focusing on identifying the Allen relation between two temporal events and temporal arithmetic, using both abstract events and real-world data from Wikidata. We assess the performance of seven recent LLMs using this benchmark and the results indicate that models handle Allen relations, even symmetrical ones, quite differently. Moreover, the findings suggest that the models may rely on memorization to answer time-related questions. Overall, the models' low performance highlights the need for improved temporal understanding in LLMs and ChronoSense offers a robust framework for future research in this area. Our dataset and the source code are available at https://github.com/duyguislakoglu/chronosense.

Figures

Figures reproduced from arXiv: 2501.03040 by the authors.

Figure 1
Figure 1. 13 Allen relations between two intervals, covering all combinations. Allen’s intervals provide a formal structure for representing temporal relationships between events, defining thirteen possible relations between time intervals. Despite its importance, existing bench￾marks cover only subsets of these relations. We demonstrate these 13 relations in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. An example for comparing two temporal events with LLMs. • We present a comprehensive evaluation of LLMs’ performance on temporal reasoning tasks using our ChronoSense benchmark. Our evaluation spans Allen relations and temporal arithmetic tasks across 0-shot, few-shot, and chain-of-thought (CoT) prompting scenarios. • We demonstrate the effectiveness of few-shot and CoT prompting in improving LLM per￾formance, espec… view at source ↗

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Forward citations

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Reference graph

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