AllocBench: Measuring Online Tool Allocation Capability in LLM Agents
Creating a reusable tool is an investment: an agent pays a fixed cost now in exchange for the potential of future reuse. Therefore, a user should prefer an agent that creates a small number of highly reusable tools, rather than many one-offs. We introduce a paired benchmark that tests whether LLM agents exhibit conscious allocation behavior under a fixed budget in two contexts: an abstract text-based formulation and a code-construction task. We find that every frontier model we test---Claude Haiku, Claude Opus, GPT-5.4-mini, and GPT-5.6 Sol---acts near-optimally in the abstract framing but fails to transfer this ability to script-writing. Through further experiments, we identify the parti...
| Authors | Daniel Wang, Andrew Xu |
|---|---|
| Primary category | cs.LG |
| Published | 25 July 2026 |
| Last updated at source | 31 July 2026 |
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