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11 result(s) for “okapi bm25 site www eyespy cy site www be cy site sell an illinois edu site:arxiv.org” · 434 ms
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MMD) to implicitly enhance cross-modal distribution consistency
from the top result below
https://arxiv.org/list/cs.CL/new
NE)
Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise
https://arxiv.org/list/cs.AI/new
CL)
Recent studies have identified an ``Artificial Hivemind'' effect in Large Language Models (LLMs) causing models to converge on a narrow, homogenized consensus even for open questions
https://arxiv.org/html/2608.02613v1
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/html/2501.13956v1
Since entire conversation histories, business datasets, and other domain-specific content cannot fit effectively inside LLM context windows, new approaches need to be developed for agent memory
https://arxiv.org/html/2608.03048v1
To discourage redundant turns, we optimize the workflow through reinforcement learning with an auxiliary turn-efficiency reward, enabling the model to adaptively exit once sufficient evidence has been accumulated
https://arxiv.org/html/2407.16741v3
edu
Abstract
Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways
https://arxiv.org/list/cs.IR/new
LG)
As lifelong learning agents accumulate lifelong growing skill banks, retrieving the correct skill becomes an increasingly important bottleneck
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/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/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/list/cs.IR/recent
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Information Retrieval
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Wed, 5 Aug 2026