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arxiv: 2605.09544 · v1 · submitted 2026-05-10 · 💻 cs.AI

Recognition: 1 theorem link

· Lean Theorem

TIDE-Bench: Task-Aware and Diagnostic Evaluation of Tool-Integrated Reasoning

Yize Li , Junzhi Li , Jason Song , Chuxiong Sun , Rui Wang , Changwen Zheng
Authors on Pith no claims yet

Pith reviewed 2026-05-12 02:40 UTC · model grok-4.3

classification 💻 cs.AI
keywords tool-integrated reasoningbenchmarktool groundingevaluation protocollarge language modelsdiagnostic evaluationtask diversityinference efficiency
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The pith

TIDE-Bench reveals tool grounding as the main persistent bottleneck in tool-integrated reasoning methods.

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

The paper introduces TIDE-Bench to fix gaps in how tool-integrated reasoning is tested. Current evaluations use narrow tasks, incomplete metrics, and full datasets that waste compute on easy cases. TIDE-Bench adds two new task types for experimental design and dynamic interaction, scores answers alongside process, efficiency, and cost, and drops low-discrimination items from prior sets. Experiments across models then show that failures in correctly selecting and invoking tools remain the core limit even when other aspects improve. This setup gives a clearer map of where TIR methods still need work.

Core claim

TIDE-Bench supplies diverse task settings that merge mathematical reasoning and knowledge QA with new tool-grounded experimental design and dynamic interactive tasks. It applies a comprehensive task-aware protocol that jointly tracks final answer quality, process reliability, tool-use efficiency, and inference cost. High-quality sets are built by filtering low-discrimination instances from existing datasets, which cuts evaluation cost while concentrating on harder samples. Tests on multiple foundation models and TIR methods identify persistent bottlenecks in tool grounding.

What carries the argument

TIDE-Bench, the benchmark that pairs expanded TIR task types with a multi-dimensional task-aware scoring protocol and discrimination-filtered datasets to isolate tool-use weaknesses.

If this is right

  • TIR research must prioritize improvements in tool selection and invocation to raise overall performance.
  • Evaluations should routinely include interactive and multi-tool coordination scenarios to capture real usage demands.
  • Filtered evaluation sets can lower compute costs while preserving or sharpening the ability to distinguish methods.
  • Diagnostic results from such benchmarks can direct targeted fixes rather than broad scaling efforts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same filtering technique could sharpen other AI reasoning benchmarks by removing cases that do not separate systems.
  • Persistent grounding failures may reflect missing pretraining signals on tool interfaces more than deficits in general reasoning.
  • Applying the benchmark to additional tool environments such as code interpreters or external APIs would test whether the observed limits are general.

Load-bearing premise

Filtering low-discrimination instances from existing datasets produces more challenging and diagnostically useful sets without introducing selection bias or dropping information essential for judging tool-use ability.

What would settle it

Re-running the full original unfiltered datasets and finding that tool-grounding bottlenecks no longer appear as the dominant failure mode or that method rankings shift substantially.

Figures

Figures reproduced from arXiv: 2605.09544 by Changwen Zheng, Chuxiong Sun, Jason Song, Junzhi Li, Rui Wang, Yize Li.

Figure 1
Figure 1. Figure 1: The overall framework of TIDE-Bench. The upper part summarizes the benchmark [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Pipeline of Pipeline of ExpoDesign. Given an underspecified research prompt, the [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Pipeline of Interaction. In a simulated e-commerce service scenario, the agent interacts with [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Tool gain over the no-tool baseline on mathematical and knowledge-intensive tasks. Each [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance gains and computational costs of tool use across mathematical and knowledge [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
read the original abstract

Tool-integrated reasoning has emerged as a promising paradigm for enhancing large language models with external computation, retrieval, and execution capabilities. However, the field still lacks a high-quality and unified evaluation benchmark, and existing TIR evaluations remain limited in dataset quality, task diversity, diagnostic comprehensiveness, and evaluation efficiency. In this work, we introduce TIDE-Bench, a holistic and efficient benchmark for evaluating TIR methods, featuring three key advantages. First, it provides diverse task settings, combining widely used mathematical reasoning and knowledge-intensive QA tasks with two newly designed tasks, namely the tool-grounded experimental design task and the dynamic interactive task, to probe models' abilities in complex tool invocation and multi-tool coordination. Second, TIDE-Bench adopts a comprehensive yet task-aware evaluation protocol, jointly measuring final answer quality, process reliability, tool-use efficiency, and inference cost across heterogeneous task settings. Third, TIDE-Bench constructs high-quality and discriminative evaluation sets by filtering low-discrimination instances from existing datasets, substantially reducing evaluation cost while focusing on more challenging samples. Extensive experiments on multiple foundation models and TIR methods reveal persistent bottlenecks in tool grounding, offering insights for future TIR research.

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

1 major / 2 minor

Summary. The paper introduces TIDE-Bench, a benchmark for tool-integrated reasoning (TIR) that augments standard mathematical reasoning and knowledge-intensive QA tasks with two newly designed tasks (tool-grounded experimental design and dynamic interactive) to test complex tool invocation and multi-tool coordination. It proposes a task-aware evaluation protocol that jointly assesses final answer quality, process reliability, tool-use efficiency, and inference cost, and constructs high-quality discriminative test sets by filtering low-discrimination instances from existing datasets to reduce evaluation cost. Experiments across multiple foundation models and TIR methods identify persistent bottlenecks in tool grounding.

Significance. If the benchmark construction and filtering are validated, TIDE-Bench would supply the TIR field with a more diverse, diagnostically comprehensive, and computationally efficient evaluation framework than prior ad-hoc setups. The new task categories directly target under-explored capabilities, and the multi-metric protocol could yield actionable insights into specific failure modes such as tool grounding. The efficiency gain from focused test sets is a practical strength if selection bias is demonstrably avoided.

major comments (1)
  1. [§4.2] §4.2 (evaluation set construction): the central claim that filtering low-discrimination instances yields 'high-quality and discriminative evaluation sets' while preserving diagnostic value is load-bearing for the reported 'persistent bottlenecks in tool grounding,' yet the manuscript supplies no explicit definition of the discrimination criterion, no quantitative ablation or distributional comparison of retained versus discarded instances (e.g., multi-tool coordination complexity or edge-case grounding), and no verification that coverage of the two new tasks remains balanced post-filtering. This directly engages the stress-test concern and requires concrete evidence before the diagnostic conclusions can be accepted.
minor comments (2)
  1. [Abstract] Abstract: the summary of experimental findings is entirely qualitative; adding one or two headline numbers (e.g., number of models evaluated, average performance gap on tool-grounding metrics) would improve immediate readability without lengthening the paragraph.
  2. [Evaluation protocol] Evaluation protocol section: the precise operationalization of 'process reliability' and 'tool-use efficiency' (e.g., exact formulas or rubrics) should be stated explicitly, ideally with a small illustrative example, to allow replication.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their thorough review and valuable feedback on our manuscript. We address the major comment point by point below and outline the revisions we will make to strengthen the paper.

read point-by-point responses

  1. Referee: [§4.2] §4.2 (evaluation set construction): the central claim that filtering low-discrimination instances yields 'high-quality and discriminative evaluation sets' while preserving diagnostic value is load-bearing for the reported 'persistent bottlenecks in tool grounding,' yet the manuscript supplies no explicit definition of the discrimination criterion, no quantitative ablation or distributional comparison of retained versus discarded instances (e.g., multi-tool coordination complexity or edge-case grounding), and no verification that coverage of the two new tasks remains balanced post-filtering. This directly engages the stress-test concern and requires concrete evidence before the diagnostic conclusions can be accepted.

    Authors: We appreciate the referee's emphasis on rigorously validating the filtering procedure, as it is indeed central to our claims. Upon review, we agree that the manuscript would benefit from more explicit details on the discrimination criterion and supporting analyses. In the revised manuscript, we will: (1) provide a formal definition of the discrimination criterion, including the formula or method used to identify low-discrimination instances; (2) include quantitative ablations and distributional comparisons between retained and discarded instances, covering aspects such as multi-tool coordination complexity, edge-case grounding requirements, and other relevant metrics; (3) verify and report that the coverage of the two newly designed tasks remains balanced after filtering. These additions will substantiate the preservation of diagnostic value and support our findings on persistent bottlenecks in tool grounding. We believe this revision will fully address the concern. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark construction with independent task and metric design

full rationale

The paper constructs TIDE-Bench by defining new tasks (tool-grounded experimental design and dynamic interactive), adopting a multi-aspect evaluation protocol, and applying a filtering procedure to existing datasets. No equations, predictions, or fitted parameters appear in the abstract or described contributions. The filtering of low-discrimination instances is presented as a methodological choice for efficiency and focus, not as a derivation that reduces to its own inputs or relies on self-citation for uniqueness. The work contains no self-definitional loops, fitted-input predictions, or ansatz smuggling; its claims rest on the explicit construction of new evaluation artifacts rather than any chain that equates outputs to prior fitted values by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The benchmark rests on the domain assumption that multi-faceted scoring and removal of low-discrimination items yield superior diagnostic power for tool-integrated reasoning; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Existing TIR datasets contain low-discrimination instances that can be safely filtered to create more efficient and focused evaluation sets
    Invoked to justify the construction of high-quality discriminative evaluation sets

pith-pipeline@v0.9.0 · 5516 in / 1347 out tokens · 55811 ms · 2026-05-12T02:40:45.927673+00:00 · methodology

discussion (0)

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