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MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation

Xiaoyuan Li, Keqin Bao, Yubo Ma, Moxin Li, Wenjie Wang, Rui Men, Yichang Zhang, Fuli Feng, Dayiheng Liu · May 21, 2025 · Citations: 0

Simulation Env
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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

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

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks. However, current evaluations predominantly focus on single-turn reasoning scenarios, leaving interactive tasks largely unexplored. We attribute it to the absence of comprehensive datasets and scalable automatic evaluation protocols. To fill these gaps, we present MTR-Bench for LLMs' Multi-Turn Reasoning evaluation. Comprising 4 classes, 40 tasks, and 3600 instances, MTR-Bench covers diverse reasoning capabilities, fine-grained difficulty granularity, and necessitates multi-turn interactions with the environments. Moreover, MTR-Bench features fully-automated framework spanning both dataset constructions and model evaluations, which enables scalable assessment without human interventions. Extensive experiments reveal that even the cutting-edge reasoning models fall short of multi-turn, interactive reasoning tasks. And the further analysis upon these results brings valuable insights for future research in interactive AI systems.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

2/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 40%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

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

missing

None explicit

No explicit feedback protocol extracted.

"Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks."

Evaluation Modes

partial

Simulation Env

Includes extracted eval setup.

"Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks."

Benchmarks / Datasets

partial

Mtr Bench

Useful for quick benchmark comparison.

"To fill these gaps, we present MTR-Bench for LLMs' Multi-Turn Reasoning evaluation."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Expertise required: General

Evaluation Details

  • Evaluation modes: Simulation Env
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

Mtr-Bench

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks.

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

Key Takeaways

  • Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks.
  • However, current evaluations predominantly focus on single-turn reasoning scenarios, leaving interactive tasks largely unexplored.
  • We attribute it to the absence of comprehensive datasets and scalable automatic evaluation protocols.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • 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

Research Summary

Contribution Summary

  • However, current evaluations predominantly focus on single-turn reasoning scenarios, leaving interactive tasks largely unexplored.
  • We attribute it to the absence of comprehensive datasets and scalable automatic evaluation protocols.
  • To fill these gaps, we present MTR-Bench for LLMs' Multi-Turn Reasoning evaluation.

Why It Matters For Eval

  • However, current evaluations predominantly focus on single-turn reasoning scenarios, leaving interactive tasks largely unexplored.
  • To fill these gaps, we present MTR-Bench for LLMs' Multi-Turn Reasoning evaluation.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Simulation Env

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Pass: Benchmark or dataset anchors are present

    Detected: Mtr-Bench

  • Gap: Metric reporting is present

    No metric terms extracted.

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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