Lomekwi: Resource-Bounded Tool Discovery in LLM Agents
Existing tool-use benchmarks report a single success rate for complex, multistep tasks. Inspired by ideas from cognitive science, we distinguish tool use from tool discovery and decompose the latter into curiosity (the model's ability to discover the parts needed to build the tool), recognition (the model's ability to discover the process of creating the tool), and efficiency (the model's use of the tool after creation). We show that this framework can be applied to existing discovery tasks, such as Voyager. In addition, we provide evidence that recognition inversely scales with model size, and we introduce and analyze a class of combinatorial games that demonstrates this. We further obse...
| Authors | Roshan Klein-Seetharaman, Daniel Wang, Andrew Xu |
|---|---|
| Primary category | cs.AI |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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