Object-Centric Environment Modeling for Agentic Tasks
Large language model (LLM) agents can improve through accumulated experience, but free-form textual memories become difficult to maintain, validate, and reuse as interactions grow. Recent symbolic approaches learn executable skills or programmatic world models, yet often store local procedures or assume simplified dynamics. We propose Object-Centric Environment Modeling (OCM), which organizes experience into an executable object-centric environment model. OCM maintains two connected code bases: object knowledge, which defines environment entities and mechanisms as Python classes, and procedure knowledge, which records reusable interaction patterns that must import and use the object model...
| Authors | Yiyang Li, Tianyi Ma, Zehong Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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