Fixing an LLM's weak spot is not enough: judging its knowledge state first lifts two benchmarks by 18.41 points on average
Knowledge improvement for large language models is moving from predefined incorporation pipelines to evaluation-guided updating, yet locating a weak spot does not say which intervention fits. Recursive Knowledge Improvement (RKI) organizes knowledge into graph-structured units and, each round, localizes weaknesses, estimates the model's operational knowledge state from diagnostic evidence and intervention history, selects an intervention, and re-evaluates the updated model. On HotpotEval and SafeInternalEval, RKI outperforms existing knowledge incorporation methods by 18.41 points on average, and analyses indicate knowledge states add guidance beyond weakness localization and can change across updates while the observed weakness persists.