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This work develops Tool-Flow Analysis to track how later operations reuse information from earlier tool results, providing a signal called observed reuse, andlations show that observed reuse improves task performance and retention of results reused later.
Results demonstrate that semantics-aware source-level analysis and transformation are effective complements to PyTorch's dynamic JIT compilation pipeline, substantially improving both usability and performance.
These results show that compiling procedural structure improves the reliability and efficiency of skill execution while retaining model judgment where it is needed, and shows that compiling procedural structure improves the reliability and efficiency of skill execution while retaining model judgment where it is needed.
Skill Compilation is introduced, realized in SIGIL, which compiles a prose skill into an executable harness, and is model-independent: the harness holds at 86% across two model generations while prose swings from 56% to 68%.
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