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GRACE uses a typed semantic graph to represent persistent instructions for LLM agents, enabling scoped verification of updates to improve reliability under distribution shift. Experiments on a telecom agent harness show significant improvements in strict reliability over baselines.
Introduces RSEA, a method for recursive self-evolution of LLM agents using a three-layer natural-language state and a held-out selection gate to prevent regression. Evaluated across four benchmarks, it shows that context evolution is benchmark-dependent and that a strict selection gate is crucial for reliability.