State-Centric Decision Process
Sungheon Jeong, Ryozo Masukawa, Sanggeon Yun, Mahdi Imani, Mohsen Imani

TL;DR
The paper introduces the State-Centric Decision Process (SDP), a framework enabling agents to construct explicit state representations from raw text environments through predicate-based actions, improving planning and analysis.
Contribution
SDP is a novel runtime framework that constructs state spaces and transitions from raw language environments by predicate-based actions, enabling better planning and analysis.
Findings
SDP achieves top training-free results across five benchmarks.
SDP's trajectories support detailed failure analysis and modular modifications.
Performance advantage increases with planning horizon length.
Abstract
Language environments such as web browsers, code terminals, and interactive simulations emit raw text rather than states, and provide none of the runtime structure that MDP analysis requires. No explicit state space, no observation-to-state mapping, no certified transitions, and no termination criterion. We introduce the State-Centric Decision Process (SDP), a runtime framework that constructs these missing inputs by having the agent build them, predicate by predicate, as it acts. At each step the agent commits to a natural-language predicate describing how the world should look, takes an action to make it true, and checks the observation against it. Predicates that pass become certified states, and the resulting trajectory carries the four objects language environments do not provide, namely a task-induced state space, an observation-to-state mapping, certified transitions, and a…
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