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https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking
https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs
https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation
https://arxiv.org/list/cs.IR/recent
IR)
Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
https://arxiv.org/list/cs.IR/new
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https://arxiv.org/pdf/2608.04341
) /Title (Continuous Improvement and Parallel Autonomous Exploration: An LLM-Agent Framework for Searching Large Solution Spaces) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/list/cs.AI/new
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https://arxiv.org/pdf/2006.02464
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https://arxiv.org/pdf/2608.02361
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https://arxiv.org/pdf/2608.02244
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https://arxiv.org/html/2407.05132v1
1 Introduction
1.1 Background and Motivation
1.2 Objectives
2 Literature Review
2.1 Related Works on Karma
2.2 Value proposition of Karma
3 Materials and Methods
3.1 Game Theoretic Formalism
3.1.1 Notation
3.1.2 Karma as a game
3.1.3