Toward Automated Robustness Evaluation of Mathematical Reasoning

by Yutao Hou arxiv/2506.05038

Free2AITools Nexus Index

38.5

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0

P: Popularity 0

R: Recency 79

Q: Quality 60

Tech Context

Vital Performance —

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 contaminatio...

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Paper Information Summary
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Registry ID	2506.05038
License	arXiv
Provider	arxiv

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Academic & Research Attribution

BibTeX

@misc{arxiv_2506_05038,
  author = {Yutao Hou},
  title = {Toward Automated Robustness Evaluation of Mathematical Reasoning Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2506.05038}},
  note = {Accessed via Free2AITools.}
}

APA Style

Yutao Hou. (2026). Toward Automated Robustness Evaluation of Mathematical Reasoning [Paper]. Free2AITools. https://arxiv.org/abs/2506.05038

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⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0

Popularity (P) 0

Recency (R) 79

Quality (Q) 60

💬 Index Insight

FNI V2.0 for Toward Automated Robustness Evaluation of Mathematical Reasoning: Authority (A:0), Popularity (P:0), Recency (R:79), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"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 contaminatio..."

❝ Cite Node

@article{Hou2026Toward,
  title={Toward Automated Robustness Evaluation of Mathematical Reasoning},
  author={Yutao Hou},
  journal={arXiv preprint arXiv:2506.05038},
  year={2026}
}