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arXiv AI By Yuval David, Fabiana Fournier, Lior Limonad, Hadar Mulian

Semantic Feature Analysis: Improving Agents Without Searching Over Rollouts

Read the original on arXiv AI →

Semantic Feature Analysis (SFA) is a method that refines agent specifications without performing any rollout-based search. It analyzes existing execution traces, clusters workflow node outputs, extracts semantic feature classes via an extended subject‑verb‑object schema, ranks these features with a decision tree, and injects the most impactful features back into the system prompt. Evaluations on four benchmarks show that SFA consistently outperforms five prompt‑optimisation algorithms and a single‑reflection baseline, especially when rollout costs are high.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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