Study maps four-stage pipeline for LLM math word problems, isolates failure point
Large language models can answer grade‑school math word problems with impressive accuracy, yet inserting a single irrelevant clause can cause the answer to collapse. Researchers examined this paradox and proposed a mechanistic explanation: the model’s internal computation proceeds through four sequential stages—Schema Abstraction, Operation Planning, Operand Binding, and Computation—each…
Key points
- LLMs solve grade-school math word problems accurately but fail when an irrelevant clause is added.
- Researchers identified a four-stage reasoning pipeline—Schema Abstraction, Operation Planning, Operand Binding, Computation—mapped to specific layer bands.
- Failure due to distractors is traced to the Operation Planning stage, linked to attention heads whose causal role was experimentally confirmed.
Using the same analytical scaffold, the team traced the distractor‑induced breakdown to the Operation Planning stage. They identified a cluster of attention heads responsible for this step and demonstrated, through bidirectional causal interventions, that disrupting these heads reproduces the failure while restoring them rescues performance. The work offers a concrete, layer‑wise map of how LLMs reason about math word problems and pinpoints the exact point of fragility when faced with irrelevant information.
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