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14 result(s) for “okapi bm25 site terms com pp site hotchips pp site doi org site:arxiv.org” · 603 ms
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The query asks the model to recall the topic of an earlier conversation about Tesla; the gold answer is anchored in one specific session about Pooja’s dig site
from the top result below
https://arxiv.org/html/2608.02613v1
2 pp, exceeding MemSearch reader scaling (+10
https://arxiv.org/pdf/2608.03437
obj > endobj 5 0 obj /Dest 2814 0 R>> endobj 6 0 obj > endobj 7 0 obj > endobj 8 0 obj > endobj 9 0 obj > endobj 10 0 obj > endobj 11 0 obj > endobj 12 0 obj > endobj 13 0 obj > endobj 14 0 obj > endobj 15 0 obj > endobj 16 0 obj > endobj
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
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
https://arxiv.org/list/cs.CL/new
2 pp, exceeding MemSearch reader scaling (+10
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/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/list/cs.AI/new
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Artificial Intelligence
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https://arxiv.org/html/2407.04503v4
Tools to Evaluate LLMs in Multi-turn Settings
1 Introduction
2 Related work
Biases in LLMs outputs
Transmission chains featuring artificial agents
Iterated Learning
Evaluation of LLMs in social contexts
3 Methods
3.1 Experimental
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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Information Retrieval
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https://arxiv.org/html/2407.01757v1
Opportunities and Technology Feasibility
1 Introduction
1.1 What is a distributed instrument?
1.1.1 Distributed Instruments and Sensor Networks
1.1.2 Distributed Instruments and Swarms
1.2 Contribution and Organization
2 State of the Art