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cs.IR2026
Whole-Pool Setwise Reranking with Long-Context Language Models
Hang Li, Chuting Yu, Teerapong Leelanupab +2
Previous LLM-based passage re-rankers are often expensive and slow because the input context constraints require the LLM to make many dependent model calls. We study how recent lon…
cs.IR2026
Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphs
Haoyu Han, Kai Guo, Harry Shomer +5
Reasoning over structured graphs remains a fundamental challenge for Large Language Models (LLMs), particularly when scaling to large graphs. Existing approaches typically follow t…