ReCast turns frozen-LLM hidden states into attribution-oriented step representations, achieving the best Hit@1 on four failure-attribution benchmarks
After quantifying that text embeddings and hidden states separate root-cause steps only weakly, the work proposes ReCast: an attribution-aware probe selects layers, a sparse Johnson–Lindenstrauss projection builds complementary pattern and deviation features, and a bidirectional encoder trained with contrastive and ranking objectives converts frozen-LLM hidden states into attribution-oriented step representations; it also releases the ReCast-2K training set and attains the best Hit@1 on Who&When, TraceElephant, AFTraj-2K, and Who&When Pro.