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This paper presents a candidate-constrained RAG system for the LongEval-RAG task at CLEF 2026, combining deterministic provenance tracking with passage retrieval, query expansion, pseudo-relevance feedback, reciprocal rank fusion, evidence reranking, and citation-aware aggregation. An ablation study of ten pipeline variants shows that a rule-based chunking pipeline with sentence-level neural selection achieves the best performance.
PaSaMaster is a self-evolving agentic literature retrieval system that iteratively refines search intent and produces evidence-grounded paper rankings, outperforming GPT-5.2 by 30% at 1% cost with zero hallucinations.