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FML-bench: Benchmarking Machine Learning Agents for Scientific Research

Qiran Zou, Hou Hei Lam, Wenhao Zhao, Yiming Tang, Tingting Chen, Samson Yu, Tianyi Zhang, Chang Liu, Xiangyang Ji, Dianbo Liu · Oct 12, 2025 · Citations: 0

Automatic Metrics
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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

Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments. However, existing benchmarks predominantly adopt an engineering-oriented perspective: they emphasize application-oriented tasks and evaluate primarily on final performance and computational cost, overlooking agents' research processes and limiting assessment of their capabilities in scientific research settings. To more comprehensively evaluate agents in scientific research settings, we introduce FML-bench, a benchmark comprising 8 diverse and fundamental ML research tasks, and further propose complementary metrics, notably Exploration Diversity, which quantifies the variance of proposals across iterations and reveals how exploration patterns influence research outcomes. We evaluate state-of-the-art research agents on FML-bench, showing that agents employing broad exploration strategies exhibit higher exploration diversity and achieve superior performance, and that exploration diversity positively correlates with performance improvements across multiple tasks. We hope these findings and our benchmark inform future agent design and support the community in further investigating agent behavior. Our benchmark is available at https://github.com/qrzou/FML-bench.

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

5/100 • 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 45%

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.

"Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments."

Benchmarks / Datasets

partial

Fml Bench

Useful for quick benchmark comparison.

"To more comprehensively evaluate agents in scientific research settings, we introduce FML-bench, a benchmark comprising 8 diverse and fundamental ML research tasks, and further propose complementary metrics, notably Exploration Diversity, which quantifies the variance of proposals across iterations and reveals how exploration patterns influence research outcomes."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments."

Human Feedback Details

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

Evaluation Details

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

Protocol And Measurement Signals

Benchmarks / Datasets

Fml-Bench

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments.

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

Key Takeaways

  • Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments.
  • However, existing benchmarks predominantly adopt an engineering-oriented perspective: they emphasize application-oriented tasks and evaluate primarily on final performance and computational cost, overlooking agents' research processes and limiting assessment of their capabilities in scientific research settings.
  • To more comprehensively evaluate agents in scientific research settings, we introduce FML-bench, a benchmark comprising 8 diverse and fundamental ML research tasks, and further propose complementary metrics, notably Exploration Diversity, which quantifies the variance of proposals across iterations and reveals how exploration patterns influence research outcomes.

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

  • Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments.
  • To more comprehensively evaluate agents in scientific research settings, we introduce FML-bench, a benchmark comprising 8 diverse and fundamental ML research tasks, and further propose complementary metrics, notably Exploration Diversity,…
  • We evaluate state-of-the-art research agents on FML-bench, showing that agents employing broad exploration strategies exhibit higher exploration diversity and achieve superior performance, and that exploration diversity positively…

Why It Matters For Eval

  • To more comprehensively evaluate agents in scientific research settings, we introduce FML-bench, a benchmark comprising 8 diverse and fundamental ML research tasks, and further propose complementary metrics, notably Exploration Diversity,…
  • We evaluate state-of-the-art research agents on FML-bench, showing that agents employing broad exploration strategies exhibit higher exploration diversity and achieve superior performance, and that exploration diversity positively…

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Pass: Benchmark or dataset anchors are present

    Detected: Fml-Bench

  • Gap: Metric reporting is present

    No metric terms extracted.

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

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