instead of judging a new agent system only by binary correctness, this method embeds LLM-summarized execution traces and predicts the system's full benchmark score from its position among reference systems. On SWE-bench Verified, adding the trace geometry to an item-response model cut mean absolute error from 0.13 to 0.08 given just one random executed task; on Terminal-Bench 2.0 it went from 0.14 to 0.12.