aiqbench analysis
This research challenges the prevailing assumption that temporal reasoning in large language models splits cleanly into arithmetic calculation and semantic understanding. The study demonstrates that current benchmarks largely reward model memorization of time-related facts rather than testing genuine transferable reasoning capabilities or logical drivers. Consequently, progress metrics based on this dichotomy may be misleading regarding actual model intelligence in handling time-based tasks.
Keeping Time vs. Understanding Time: Neither Alone Explains Temporal Reasoning in LLMs
arXiv:2610.29507v1 Announce Type: cross Abstract: Temporal reasoning is widely assumed to decompose into two modes: temporal arithmetic (date and time calculations), and temporal semantics (understanding temporal and causal relations). This dichotomy shapes how benchmarks are designed, how models are trained, and how progress is measured, yet its empirical validity has never been directly tested. We provide two lines of evidence that challenge it. First, we test whether tasks labelled as arithmetic or semantic by the benchmarks support transferable gains within their purported mode. Finetuning on these subsets yields predominantly dataset-specific gains ($+6.8$ to $+23.1$ percentage points), while transfer to a different dataset in the same mode ranges from $-3.2$ to $+1.1$ percentage points. Across the two benchmark families used in the transfer analysis, this pattern provides little evidence of coherent, transferable temporal abilities. Second, using cognitive-demand-based model evaluation, we examine which capabilities actually drive performance on these benchmarks. We find that quantitative and logical reasoning, the assumed proxies for temporal arithmetic and temporal semantics, are not the primary drivers; knowledge is. Together, these findings show that current temporal reasoning benchmarks do not cleanly realise the arithmetic-semantics distinction and instead largely reward models for remembering how time typically works.
Title:Keeping Time vs. Understanding Time: Neither Alone Explains Temporal Reasoning in LLMs
Abstract:Temporal reasoning is widely assumed to decompose into two modes: temporal arithmetic (date and time calculations), and temporal semantics (understanding temporal and causal relations). This dichotomy shapes how benchmarks are designed, how models are trained, and how progress is measured, yet its empirical validity has never been directly tested. We provide two lines of evidence that challenge it. First, we test whether tasks labelled as arithmetic or semantic by the benchmarks support transferable gains within their purported mode. Finetuning on these subsets yields predominantly dataset-specific gains ($+6.8$ to $+23.1$ percentage points), while transfer to a different dataset in the same mode ranges from $-3.2$ to $+1.1$ percentage points. Across the two benchmark families used in the transfer analysis, this pattern provides little evidence of coherent, transferable temporal abilities. Second, using cognitive-demand-based model evaluation, we examine which capabilities actually drive performance on these benchmarks. We find that quantitative and logical reasoning, the assumed proxies for temporal arithmetic and temporal semantics, are not the primary drivers; knowledge is. Together, these findings show that current temporal reasoning benchmarks do not cleanly realise the arithmetic-semantics distinction and instead largely reward models for remembering how time typically works.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2610.29507 [cs.CL]
(or arXiv:2610.29507v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.29507 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 303 words
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