Visualizing Graph-to-Answer Mechanism Recovery in Materials-Science Hypothesis Generation

Abstract:AI co-scientists can generate fluent materials-science hypotheses, but fluency does not show that an answer preserves a scientifically meaningful mechanism. We present a graph-to-answer mechanism-tracing case study for Graph-PRefLexOR-8B, a Qwen3-8B model adapted to expose distinct stages for brainstorming, graph construction, pattern extraction, and synthesis. We organize semantic backtracking, graph corruption, activation-based recovery measurements, and layer-by-token-region grids into a visual diagnostic workflow for inspecting this pathway. Across 100 open-ended materials-science questions, final answers remain closest to the model's own structured stages, especially synthesis. Under graph corruption, a full sweep over 37 residual-stream checkpoints, the embedding output and 36 transformer blocks, shows little mechanism recovery in the earlier transition region at layers 7--10, recovery instead concentrates in late synthesis and answer-start regions around layers 30 and 36. The workflow is intended to help scientists and model developers identify where a generated hypothesis loses or regains mechanism support before it is passed to downstream experimental planning.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.04170 [cs.CL]

(or arXiv:2608.04170v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.04170 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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