Scaling Small Agents Through Strategy Auctions
- Published
- Source
- arXiv
- Paper number
- 116
- Field
- Multi-Agent / Routing
- arXiv ID
- 2602.02751
Key points
- The performance of smaller AI agents relative to larger models on complex long-horizon tasks has not been well understood.
- Effectively routing tasks to the best AI agent from a heterogeneous pool of models with different sizes, costs, and capabilities was a challenge for optimizing both accuracy and cost.
- Current routing methods for multi-model orchestration are often too expensive for agentic workflows, or they generalize poorly and degrade as task difficulty increases.
- Strategy Auctions for Workload Efficiency (sale) is introduced as a market-inspired framework for dynamic task allocation across heterogeneous AI agents.
- Agents generate short strategic solution plans for each task as bids, which are evaluated by a learned cost-value mechanism that accounts for model price and plan quality.
- The framework includes a memory-based self-improvement loop in which cheaper agents selectively improve their strategies using past auction outcomes, raising performance over time.
Paper links
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