EvoAlloc: A Self-Evolving Resource Allocation Agent for Efficient Program Evolution
- Published
- Source
- arXiv
- Paper number
- 1205
- Field
- Research Agents
- arXiv ID
- 2610.12086
Key points
- In program evolution, evaluation is computationally expensive, so allocating limited resources to promising candidates was a central decision of the search.
- EvoAlloc periodically consolidated prior search and allocation outcomes into reusable experience, which informed subsequent strategy revisions.
- A counterfactual exploration mechanism occasionally evaluated candidates denied resources by the allocator, revealing hidden outcomes and enriching its experience.
- Across coding and agent-harness optimization benchmarks, EvoAlloc required 59–82% fewer full evaluations and 61–89% fewer LLM tokens, and achieved 8.7–12.0% higher final performance under the same budget.
- With the same Full-evaluation budget on the same seed, EvoAlloc examined 124 proposals on ADAS-AIME versus 75 for ShinkaEvolve, widening the search.
- Across exploration rates, final performance was similar but tokens to the target varied substantially, and ρ = 0.3 was the most efficient.
Paper links
External research summaries. These are not HDATF publications or measured product results.