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13 result(s) for “okapi bm25 site hotchips pp site github com site os dev site pops python org site:arxiv.org” · 714 ms

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1 Introduction

https://arxiv.org/html/2407.16741v3

com/All-Hands-AI/OpenHands Slack http://bit

Report GitHub Issue

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

Artificial Intelligence

https://arxiv.org/list/cs.AI/new

Lean 4 checks kernel analogues for positions, evidence, clocks, monitors, atomicity, and branch source retention; 88 tests, 3,534 bounded checks, and 32 Lean/Python runtime-kernel cases bound the implementation claim to the checked cases

Report GitHub Issue

https://arxiv.org/html/2608.03048v1

com/JetAstra/PI-Mem Introduction The ability to reason over long contexts is increasingly important for large language models (LLMs), supporting applications such as long-document question answering (Gemini Team 2025; Yang et al

1 Introduction

https://arxiv.org/html/2501.13956v1

com Abstract We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark

Computation and Language

https://arxiv.org/list/cs.CL/new

We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

Don’t Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn

https://arxiv.org/html/2608.03388v1

The Game Master hides a target Python function ff and interacts with the LLM through a black-box protocol

[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

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

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

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

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

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

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https://arxiv.org/src/2608.03898

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Information Retrieval

https://arxiv.org/list/cs.IR/recent

Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Information Retrieval Authors and titles for recent submissions Wed, 5 Aug 2026

Information Retrieval

https://arxiv.org/list/cs.IR/new

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