Skip to content
OpenTrain AIFor AI Companies

HFEPX · Eval paper review

Learning How to Use Tools, Not Just When: Pattern-Aware Tool-Integrated Reasoning

Ningning Xu, Yuxuan Jiang, Shubhashis Roy Dipta, Hengyuan Zhang

Published

Sep 27, 2025

Citations

0

Trust level

High

Usefulness score

65/100 (Medium)

Extraction confidence

80% (High)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Jun 29, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

Automatic Metrics Coding Math Pairwise Preference

Should you rely on this paper?

This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.

Use this as a practical starting point for protocol research, then validate against the original paper.

Best use

Secondary protocol comparison source

Use if you need

A benchmark-and-metrics comparison anchor.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

No major weakness surfaced.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
65/100
Moderate-confidence candidate

Useful as a secondary reference; validate protocol details against neighboring papers.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems. Prior work has mainly studied when to invoke tools, while overlooking how tools are applied. We identify two common patterns: a calculator pattern that uses code for direct computation, and an algorithmic pattern that encodes problems as programs. Misaligned choices often cause failures even when reasoning is sound. We propose a two-stage framework that first builds code competence from both patterns and then aligns pattern selection with teacher preferences. Across challenging math datasets, our pattern-aware method substantially improves both code usage and accuracy, for instance raising Code@1 on MATH500 from 64.0% to 70.5% and on AIME24 from 26.7% to 50.0%. These gains highlight the effectiveness of a pattern-aware approach for tool-integrated reasoning.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

strong

Pairwise Preference

Directly usable for protocol triage.

"Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems."

Evaluation Modes

strong

Automatic Metrics

Includes extracted eval setup.

"Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems."

Benchmarks / Datasets

strong

MATH 500

Useful for quick benchmark comparison.

"Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems."

Reported Metrics

strong

Accuracy

Useful for evaluation criteria comparison.

"Across challenging math datasets, our pattern-aware method substantially improves both code usage and accuracy, for instance raising Code@1 on MATH500 from 64.0% to 70.5% and on AIME24 from 26.7% to 50.0%."

Benchmarks and datasets

MATH-500

Reported metrics

accuracy
Human feedback details
Uses human feedback
Yes
Feedback types
Pairwise Preference
Rater population
Not reported
Expertise required
Math, Coding
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
None
Quality controls
Not reported
Evidence quality
High
Use this page as
Secondary protocol comparison source

Research brief

Metadata summary

Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems.
  • Prior work has mainly studied when to invoke tools, while overlooking how tools are applied.
  • We identify two common patterns: a calculator pattern that uses code for direct computation, and an algorithmic pattern that encodes problems as programs.

Researcher actions

  • Compare this paper against others mentioning MATH.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

MATH Accuracy

Contribution summary

  • We propose a two-stage framework that first builds code competence from both patterns and then aligns pattern selection with teacher preferences.
  • Across challenging math datasets, our pattern-aware method substantially improves both code usage and accuracy, for instance raising Code@1 on MATH500 from 64.0% to 70.5% and on AIME24 from 26.7% to 50.0%.

Why it matters for eval

  • We propose a two-stage framework that first builds code competence from both patterns and then aligns pattern selection with teacher preferences.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Pairwise Preference

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    Detected: MATH-500

  • Metric reporting is present

    Detected: accuracy