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A Four-Stage Decomposition of Word-Problem Solving and Mechanistic Fragility in LLM Math ReasoningA Four-Stage Decomposition of Word-Problem Solving and Mechanistic Fragility in LLM Math Reasoning

📅 2026-09-17 ⏱️ 约 5 分钟阅读⏱️ 5 min read ✍️ AI导航编辑部✍️ AI Nav Editorial 🔗 arxiv.org
A Four-Stage Decomposition of Word-Problem Solving and Mechanistic Fragility in LLM Math Reasoning
📝 内容摘要📝 Summary

Computer Science > Artificial Intelligence [Submitted on 15 Sep 2026] Title:A Four-Stage Decomposition of Word-ProComputer Science > Artificial Intelligence [Submitted on 15 Sep 2026] Title:A Four-Stage Decomposition of Word-Pro

📌 核心要点

  • Computer Science > Artificial IntelligenceComputer Science > Artificial Intelligence
  • [Submitted on 15 Sep 2026][Submitted on 15 Sep 2026]
  • Bibliographic and Citation ToolsBibliographic and Citation Tools

Computer Science > Artificial Intelligence

[Submitted on 15 Sep 2026]

Title:A Four-Stage Decomposition of Word-Problem Solving and Mechanistic Fragility in LLM Math Reasoning

View PDF HTML (experimental)Abstract:Large language models solve grade-school math word problems with high accuracy, yet a single irrelevant clause inserted into the problem can collapse it. We reconcile these observations with a mechanistic account. We show that the model's internal computation decomposes into a four-stage sequential pipeline, Schema Abstraction, Operation Planning, Operand Binding, and Computation, each stage producing a distinct intermediate representation in an identifiable band of layers. Using the same scaffold to diagnose distractor-induced failure, we localize the corruption to a single stage, Operation Planning, implemented by a set of attention heads whose causal role we validate bidirectionally. In short, we provide a mechanistic interpretation of math word problem reasoning in LLMs, and their failure when distracted.

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Computer Science > Artificial Intelligence

[Submitted on 15 Sep 2026]

Title:A Four-Stage Decomposition of Word-Problem Solving and Mechanistic Fragility in LLM Math Reasoning

View PDF HTML (experimental)Abstract:Large language models solve grade-school math word problems with high accuracy, yet a single irrelevant clause inserted into the problem can collapse it. We reconcile these observations with a mechanistic account. We show that the model's internal computation decomposes into a four-stage sequential pipeline, Schema Abstraction, Operation Planning, Operand Binding, and Computation, each stage producing a distinct intermediate representation in an identifiable band of layers. Using the same scaffold to diagnose distractor-induced failure, we localize the corruption to a single stage, Operation Planning, implemented by a set of attention heads whose causal role we validate bidirectionally. In short, we provide a mechanistic interpretation of math word problem reasoning in LLMs, and their failure when distracted.

References & Citations

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Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)

Connected Papers (What is Connected Papers?)

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scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub (What is DagsHub?)

Gotit.pub (What is GotitPub?)

Hugging Face (What is Huggingface?)

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Demos

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arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

来源arxiv.org· 本文为编辑整理,仅供参考