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Did you mean okapi bm25 site hotchips pp site github com site text of de site www inc washington site:arxiv.org?

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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

Report GitHub Issue

https://arxiv.org/html/2608.02613v1
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 8787/77/4848 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations

Report GitHub Issue

https://arxiv.org/html/2608.03048v1
To address these issues, we propose PI-Mem (Parallel-Iterative Memory), a mechanism that processes all chunks in parallel and iteratively refines a shared memory over a bounded number of turns

Report GitHub Issue

https://arxiv.org/html/2608.03063v1
Offline comparison of deployment candidates (RQ3, Table 13)

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
However, their performance heavily depends on the clarity and completeness of data semantics

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
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 87/7/48 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations

1 Introduction

https://arxiv.org/html/2407.16741v3
2 Agent Runtime: How Execution of Actions Results in Observations 2

Sphere Retraction Normalizations

https://arxiv.org/html/2608.02668v1
Both prove to be members of a one-parameter family of angular retractions, pp-SpheretNorm, whose rotation angle saturates rather than growing without bound

MinerU2.5: A Decoupled Vision-Language Model for Efficient High

https://arxiv.org/html/2509.22186v2
ocr, PP-StructureV3), establishing new performance records in text recognition, formula recognition, table recognition, and reading order prediction

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https://arxiv.org/pdf/2608.03883
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https://arxiv.org/src/2006.15704
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https://arxiv.org/pdf/2607.26188v1
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https://arxiv.org/src/2006.02893
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https://arxiv.org/pdf/2006.02759
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https://arxiv.org/src/2405.20232
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https://arxiv.org/src/2005.13905
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/src/2006.01354
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https://arxiv.org/src/2006.01331
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https://arxiv.org/pdf/2608.03233
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https://arxiv.org/src/2006.01318
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https://arxiv.org/src/2104.08691
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https://arxiv.org/pdf/2006.01402
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https://arxiv.org/pdf/2607.27070v1
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https://arxiv.org/pdf/2407.01722
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https://arxiv.org/src/2006.02055
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https://arxiv.org/src/2006.02464
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[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

https://arxiv.org/abs/2608.03883
The task requires systems to rank candidate Reddit sentences according to their relevance to each of the 18 symptoms in the Adult ADHD Self-Report Scale (ASRS-v1

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

https://arxiv.org/abs/2104.08663
We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark

[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