EvoFlux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents
Small language models (1.5B-4B parameters) offer low-cost, low-latency tool use but often produce plausible plans that fail during execution due to unavailable tools, missing arguments, or broken dependencies. We introduce EvoFlux, an inference-time search method that repairs tool plans through an evolutionary loop over structured graphs of tool calls and dependencies. EvoFlux executes candidate plans against live tool servers and uses execution errors to guide targeted edits, including replacing tools, correcting parameters, inserting missing steps, and removing redundant ones. On held-out MCP-Bench tasks spanning 28 live servers and 250 tools, EvoFlux improves execution success from rou...
| Authors | Kushal Raj Bhandari, Ling Yue, Ching-Yun Ko et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 June 2026 |
| Last updated at source | 14 October 2026 |
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