Action-Space Shaping for LLM Agents: Measuring and Mitigating Tool-Schema Bias
Large Language Models (LLMs) have shown strong performance on tool-use agentic tasks when given a fixed tool schema. Yet a tool schema is not the action space of an agent; it is merely one interface representation of it. The same executable action can be exposed through many different, functionally equivalent tool definitions, and an agent that has truly learned a task should behave consistently across them. We show that current agents often do not, a phenomenon we term schema bias. To study this systematically, we introduce an executable transformation framework that rewrites a native tool schema using nine operators, including merging and splitting tools, altering how a single tool is e...
| Authors | Yinhong Liu, Zhili Tan, Zilin Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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