Semantic Feature Analysis: Improving Agents Without Searching Over Rollouts
Quick summary
arXiv:2604.10513v2 Announce Type: replace Abstract: Ambiguity is an inherent property of natural-language agent specifications. When a system prompt leaves behaviour underdetermined, identical inputs follow divergent execution paths and produce inconsistent outcomes. The standard remedy is prompt optimisation: propose candidate prompts, run the agent to score them, and keep the best. This loop pays for the agent twice: once to generate candidates and again to rank them. On a tool-using agent whose rollouts cost dollars and minutes, the ranking cost dominates and budget-constrained optimisers r
Key takeaways
- arXiv:2604.10513v2 Announce Type: replace Abstract: Ambiguity is an inherent property of natural-language agent specifications.
- When a system prompt leaves behaviour underdetermined, identical inputs follow divergent execution paths and produce inconsistent outcomes.
- The standard remedy is prompt optimisation: propose candidate prompts, run the agent to score them, and keep the best.
Why it matters
This development shows AI moving deeper into everyday software. Productivity potential should be weighed against price, data permissions, exportability and the preservation of human control.
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