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HFEPX · Eval paper review

Toward Automated Robustness Evaluation of Mathematical Reasoning

Yutao Hou, Zeguan Xiao, Fei Yu, Yihan Jiang +5 more

Published

Jun 5, 2025

Citations

0

Trust level

Low

Usefulness score

0/100 (Low)

Extraction confidence

25% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Apr 24, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

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.

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

Best use

Background context only

Use if you need

Background context only.

What to verify

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

Main weakness

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

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
0/100
Adjacent candidate

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

If you are doing eval pipeline work, start here

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

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning-intensive tasks. However, these models exhibit unexpected brittleness, often failing on simple variations of the same underlying task. Existing robustness evaluations predominantly rely on hand-crafted templates or a limited set of perturbation rules. Consequently, such approaches lack the adaptability to probe latent vulnerabilities unique to specific models and remain susceptible to data contamination. To address this, we propose the Math Stress Tester (MaSTer), an automated framework inspired by software stress testing. MaSTer generates adversarial variants via a multi-round rewrite-verify loop, ensuring semantic consistency while successfully inducing model failure. Our framework generates benchmark variants dynamically for each LLM, thus minimizing the risk of data contamination. Experiments on GSM8K and MATH-500 demonstrate the effectiveness of MaSTer on mathematical tasks. Additionally, we validate the framework's extensibility to non-mathematical tasks, highlighting its broad applicability. Furthermore, we demonstrate that the synthesized variants generated by MaSTer can be utilized as a fine-tuning dataset to significantly enhance the model's robustness.

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 demonstrated remarkable capabilities in various reasoning-intensive tasks."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning-intensive tasks."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning-intensive tasks."

Benchmarks / Datasets

partial

MATH 500, GSM8K

Useful for quick benchmark comparison.

"Experiments on GSM8K and MATH-500 demonstrate the effectiveness of MaSTer on mathematical tasks."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning-intensive tasks."

Benchmarks and datasets

MATH-500GSM8K

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
Math
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning-intensive tasks.

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

Key takeaways

  • Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning-intensive tasks.
  • However, these models exhibit unexpected brittleness, often failing on simple variations of the same underlying task.
  • Existing robustness evaluations predominantly rely on hand-crafted templates or a limited set of perturbation rules.

Researcher actions

  • Compare this paper against others mentioning GSM8K.
  • 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

GSM8K

Contribution summary

  • Existing robustness evaluations predominantly rely on hand-crafted templates or a limited set of perturbation rules.
  • To address this, we propose the Math Stress Tester (MaSTer), an automated framework inspired by software stress testing.
  • Furthermore, we demonstrate that the synthesized variants generated by MaSTer can be utilized as a fine-tuning dataset to significantly enhance the model's robustness.

Why it matters for eval

  • Existing robustness evaluations predominantly rely on hand-crafted templates or a limited set of perturbation rules.
  • Our framework generates benchmark variants dynamically for each LLM, thus minimizing the risk of data contamination.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    Detected: MATH-500, GSM8K

  • Metric reporting is present

    No metric terms extracted.