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arxiv: 2604.24175 · v1 · submitted 2026-04-27 · 💻 cs.CL · cs.AI

AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models

Yimin Deng , Yejing Wang , Zhenxi Lin , Zichuan Fu , Guoshuai Zhao , Derong Xu , Yefeng Zheng , Xiangyu Zhao
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Xian Wu Li Zhu Xueming Qian
This is my paper

Pith reviewed 2026-05-08 03:41 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords temporal reasoninglarge language modelsadaptive reasoningLLM plannerreformulate rewrite reviewno external tools
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The pith

AdapTime lets LLMs dynamically select among reformulate, rewrite, and review steps for temporal questions via an internal planner.

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

The paper argues that fixed pipelines for temporal reasoning waste effort on simple questions and fall short on complex ones, while external-tool methods lack generalizability. AdapTime instead relies on an LLM planner to choose and sequence three actions—reformulate the query, rewrite it for clarity, or review prior steps—based on the specific input. This adaptive process runs inside existing models and improves their handling of time-related information without outside help. A sympathetic reader would care because it offers a way to make LLMs more reliable on real-world queries involving dates, sequences, or timelines.

Core claim

AdapTime is an adaptive temporal reasoning method that dynamically executes reasoning steps based on the input context. It involves three temporal reasoning actions: reformulate, rewrite and review, with an LLM planner guiding the reasoning process. AdapTime integrates seamlessly with state-of-the-art LLMs and significantly enhances their temporal reasoning capabilities without relying on external support.

What carries the argument

An LLM planner that selects and sequences the three actions (reformulate, rewrite, review) to match the needs of each temporal question.

Load-bearing premise

The LLM planner can reliably and correctly decide which actions or sequence is needed for any given temporal question, and that this dynamic choice works better than fixed pipelines for both simple and complex cases.

What would settle it

A side-by-side evaluation on temporal reasoning benchmarks where replacing the planner with a fixed sequence of all three actions produces equal or higher accuracy than the full AdapTime method.

Figures

Figures reproduced from arXiv: 2604.24175 by Derong Xu, Guoshuai Zhao, Li Zhu, Xiangyu Zhao, Xian Wu, Xueming Qian, Yefeng Zheng, Yejing Wang, Yimin Deng, Zhenxi Lin, Zichuan Fu.

Figure 1
Figure 1. Figure 1: An example of temporal reasoning in question view at source ↗
Figure 2
Figure 2. Figure 2: The overall architecture of our model. held), then estimates their temporal spans, and fi￾nally infers the answer based on the specified time expression. This decomposition is induced by the model’s own understanding of temporal semantics, requiring no additional rule definitions or tools and thus allowing it to adapt to different settings. The Reformulate module reduces ambiguity by isolating temporally r… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of the proportion of each opera view at source ↗
read the original abstract

Large language models have demonstrated strong reasoning capabilities in general knowledge question answering. However, their ability to handle temporal information remains limited. To address this limitation, existing approaches often involve external tools or manual verification and are tailored to specific scenarios, leading to poor generalizability. Moreover, these methods apply a fixed pipeline to all questions, overlooking the fact that different types of temporal questions require distinct reasoning strategies, which leads to unnecessary processing for simple cases and inadequate reasoning for complex ones. To this end, we propose AdapTime, an adaptive temporal reasoning method that dynamically executes reasoning steps based on the input context. Specifically, it involves three temporal reasoning actions: reformulate, rewrite and review, with an LLM planner guiding the reasoning process. AdapTime integrates seamlessly with state-of-the-art LLMs and significantly enhances their temporal reasoning capabilities without relying on external support. Extensive experiments demonstrate the effectiveness of our approach.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes AdapTime, a method for adaptive temporal reasoning in LLMs. It uses an LLM planner to dynamically choose and sequence among three actions—reformulate, rewrite, and review—depending on the input temporal question's complexity. The approach is presented as integrating directly with existing LLMs, avoiding external tools or fixed pipelines, and the abstract states that extensive experiments confirm it significantly improves temporal reasoning capabilities.

Significance. If the central claims hold—specifically that the planner makes reliable, superior decisions and that this yields measurable gains over fixed strategies—AdapTime would offer a lightweight, generalizable way to enhance LLMs on temporal tasks without added infrastructure. This could be useful for applications like timeline extraction or event-based QA, by avoiding overkill on simple cases while providing deeper reasoning where needed.

major comments (3)
  1. [Abstract] The abstract claims 'extensive experiments demonstrate the effectiveness' yet reports no baselines, metrics (e.g., accuracy, F1), error bars, or ablations. This is load-bearing for the central claim, as the value of adaptivity cannot be assessed without evidence that the planner's dynamic choices outperform always applying all three actions or fixed pipelines.
  2. [Method (planner description)] No details are provided on the LLM planner's prompt, decision accuracy, or how it selects among reformulate/rewrite/review (or sequences). Without empirical validation of planner reliability (e.g., human or automatic evaluation of action choices on a held-out set), the adaptivity advantage reduces to standard multi-step prompting.
  3. [Experiments] The experiments section (assuming standard structure) must include an ablation isolating the planner's contribution versus fixed application of all actions; absent this, gains cannot be attributed to adaptivity rather than simply using more reasoning steps.
minor comments (2)
  1. [Method] Clarify whether the three actions are mutually exclusive or can be sequenced, and provide the exact planner prompt template used.
  2. [Experiments] Add a table comparing AdapTime against at least two strong baselines (e.g., standard CoT, self-consistency) on the temporal QA datasets used.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We appreciate the referee's thorough review and constructive suggestions. We will revise the manuscript to provide more details on the experimental results, the planner's implementation, and additional ablations as requested.

read point-by-point responses

  1. Referee: [Abstract] The abstract claims 'extensive experiments demonstrate the effectiveness' yet reports no baselines, metrics (e.g., accuracy, F1), error bars, or ablations. This is load-bearing for the central claim, as the value of adaptivity cannot be assessed without evidence that the planner's dynamic choices outperform always applying all three actions or fixed pipelines.

    Authors: We acknowledge that the abstract is currently high-level. We will revise the abstract to include specific metrics such as accuracy and F1, mention the baselines (including fixed pipelines), and note the improvements with error bars. This will better substantiate the effectiveness claims. revision: yes

  2. Referee: [Method (planner description)] No details are provided on the LLM planner's prompt, decision accuracy, or how it selects among reformulate/rewrite/review (or sequences). Without empirical validation of planner reliability (e.g., human or automatic evaluation of action choices on a held-out set), the adaptivity advantage reduces to standard multi-step prompting.

    Authors: We will add the full prompt used by the LLM planner to the method section or appendix. We will also include an evaluation of the planner's decision accuracy on a held-out set, using automatic evaluation where feasible, to demonstrate that the adaptive choices are reliable and not equivalent to fixed multi-step prompting. revision: yes

  3. Referee: [Experiments] The experiments section (assuming standard structure) must include an ablation isolating the planner's contribution versus fixed application of all actions; absent this, gains cannot be attributed to adaptivity rather than simply using more reasoning steps.

    Authors: We agree that isolating the planner's contribution is important. We will add an ablation study in the experiments section that compares AdapTime to a non-adaptive version that applies all actions in a fixed sequence, as well as other fixed strategies. This will show that the dynamic selection by the planner provides benefits beyond additional reasoning steps. revision: yes

Circularity Check

0 steps flagged

No circularity in claimed derivation

full rationale

The paper describes AdapTime as an engineering method that combines existing LLM prompting capabilities via a planner selecting among reformulate/rewrite/review actions. No equations, fitted parameters, or mathematical derivations are present in the provided text. The approach is presented as a dynamic combination of standard LLM behaviors rather than a derivation that reduces to its own inputs by construction, self-citation chains, or renamed empirical patterns. The central claim rests on experimental validation of the adaptive pipeline, which is independent of any self-referential loop.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper introduces no new mathematical constants, fitted parameters, or postulated physical entities. It relies on the domain assumption that current LLMs already possess sufficient planning and reasoning ability to be guided by the described actions.

axioms (1)
  • domain assumption Large language models possess sufficient internal reasoning and planning ability to act as both executor and planner for temporal questions.
    The entire AdapTime pipeline depends on the LLM correctly interpreting and executing the planner's decisions without external verification.

pith-pipeline@v0.9.0 · 5480 in / 1287 out tokens · 58941 ms · 2026-05-08T03:41:34.655534+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

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