SEEKA: Self-Evolving Skill Optimization for Entity Disambiguation over Knowledge Base

Entity disambiguation links an ambiguous mention to the entity it denotes in a knowledge base (KB). Existing learned rerankers require labeled data and retraining as domains or entity inventories change, while zero-shot large language model (LLM) rerankers avoid training but cannot improve from recurring errors when their instructions remain fixed. We introduce SEEKA, a nonparametric adaptation framework that co-evolves a KB-grounded synthetic curriculum and the external skill of a frozen LLM reranker (also called Solver). A Proposer generates challenging cases from target entities and their nearest confusers, while an audit verifies factual grounding and unique identification. The Solver’s errors drive validation-gated updates to its instructions, memory, and decision workflow, while a learnability objective adapts the curriculum to the evolving Solver. Across the official held-out test sets of four benchmarks and four Solver families, SEEKA achieves higher top-1 accuracy than the corresponding zero-shot baseline in all 16 model–dataset comparisons, without modifying model parameters or candidate pools.