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15 result(s) for “okapi bm25 site terms com pp site www evan of course org site:arxiv.org” · 1180 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
(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
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
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
https://arxiv.org/html/2407.16741v3
2 Agent Runtime: How Execution of Actions Results in Observations
2
https://arxiv.org/list/cs.AI/new
However, their performance heavily depends on the clarity and completeness of data semantics
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
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
https://arxiv.org/abs/2104.08663
Benchmark for Zero-shot Evaluation of Information Retrieval Models, by Nandan Thakur and 4 other authors
View PDF HTML (experimental)
Abstract:Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of
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/2509.02523v1
PDF HTML (experimental)
Abstract:We present the Flavors of Moonshine, a suite of tiny automatic speech recognition (ASR) models specialized for a range of underrepresented languages
https://arxiv.org/list/cs.CE/current
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[1] arXiv:2608
https://arxiv.org/abs/2410.15608v2
Live Transcription and Voice Commands, by Nat Jeffries and 5 other authors
View PDF HTML (experimental)
Abstract:This paper introduces Moonshine, a family of speech recognition models optimized for live transcription and voice command processing
https://arxiv.org/abs/2410.15608
15608] Moonshine: Speech Recognition for Live Transcription and Voice Commands
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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
We provide the core implementation of KGD at this https URL