# ReCast

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.

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.

RECAST casts evidence construction as a sequential decision process over retrieval and computation operations: a lightweight RouterLM iteratively selects lexical, semantic, or relational primitives, or requests customized operations that a frozen CompilerLM translates into executable code, then passes accepted evidence to a frozen AnswerLM; RouterLM is trained with SFT followed by GRPO, achieving the strongest average success rate across six heterogeneous benchmarks and zero-shot generalization on three held-out benchmarks.

RECAST casts evidence construction as a sequential decision process over retrieval and computation operations: a lightweight RouterLM iteratively selects lexical, semantic, or relational primitives, or requests customized operations that a frozen CompilerLM translates into executable code, then passes accepted evidence to a frozen AnswerLM; RouterLM is trained with SFT followed by GRPO, achieving the strongest average success rate across six heterogeneous benchmarks and zero-shot generalization on three held-out benchmarks.