EvoState: Multi-Operation Context-State Transitions for Long-Horizon LLM Agents

Long-horizon LLM agents must preserve action-critical information while tracking which facts remain valid and which requirements remain unresolved. As execution progresses, new evidence can change both the status of retained information and its relevance to subsequent actions. We introduce EvoState, a training-free framework for maintaining evolving context state. The framework models context state in three complementary parts: Current Trace for ongoing execution evidence, Event Memory for historical outcomes, and Task State for current facts and requirements, each with distinct retention and update rules. Its Multi-Operation Context Editor combines rule-based updates with conditional LLM-based consolidation to maintain these records as new evidence arrives. The Dependency-Aware Context Assembler uses the revised state to prioritize exact dependencies, current facts, and unresolved requirements for the next decision, allowing necessary evidence to exceed a soft token target. Using GLM-5.1, we evaluate EvoState on AppWorld Normal and Challenge, BFCL Long Context, and an interactive exact-match subset of CRMArena-Pro. EvoState lies on the observed success–token Pareto frontier in all four settings, with gains of up to 7.1 percentage points in success and reductions of up to 29.4% in tokens per task relative to Full History. Controlled analyses further show improved reference retention and next-action progress.