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cs.IR2026
Scaling Laws for Cross-Encoder Reranking
Rahul Seetharaman, Aman Bansal, Hamed Zamani +1
Scaling laws are well studied for language models and first-stage retrieval, but not for reranking. We present the first systematic study of scaling laws for cross-encoder reranker…
cs.IR2026
: Semantic Residual Effective Contrastive Information for Evaluating Hard Negatives
Aarush Sinha, Rahul Seetharaman, Aman Bansal
Hard-negative source selection for dense retrieval is usually decided only after fine-tuning and downstream evaluation. We propose ECIsem, a validity-weighted diagnostic that ranks…
cs.IR2025
InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking
Rahul Seetharaman, Kaustubh D. Dhole, Aman Bansal
Large Language Models (LLMs) have demonstrated significant strides across various information retrieval tasks, particularly as rerankers, owing to their strong generalization and k…