ACCORD: Action-Conditioned Contextual Grounding for Language Agents
User instructions are often underspecified because humans rely on implicit assumptions about the surrounding environment. For large language model (LLM) agents operating in information-rich digital and physical environments, these assumptions cannot be inferred from the instruction alone; they must be recovered from the current state of tools, data, interfaces, and observations. Effective execution therefore requires agents to identify missing context, ground it in observed evidence, and carry it forward into subsequent actions. We show that current agents often fail to do so. They act from assumed rather than observed specifics, overlook information they could have gathered, and fail to ...
| Authors | Lai Jiang, Cheng Qian, Zhenhailong Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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