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30 result(s) for “okapi bm25 site terms com pp site does could good com site pass uses gov site:arxiv.org” of 33 matched · 674 ms
Scope: Nishi’s own index (our crawl + Common Crawl) — not the whole live web. Some sites block all automated crawlers (anti-bot walls), so their pages may be absent here even though we never filter lawful content.
Freshness: no result on this page carries a fetch time yet (indexed before the fetch epoch was stored beside the doc)
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
6 Ingest Overhead: When Does Structured Memory Pay Off
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/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/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
https://arxiv.org/html/2608.03703v1
An LETF is a repackaging: it holds the underlying and borrows, and spans no payoff an investor could not construct directly
https://arxiv.org/list/cs.AI/new
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Artificial Intelligence
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https://arxiv.org/html/2607.22511v2
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https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
https://arxiv.org/html/2608.02625v1
Despite showing early promise, especially in terms of efficiency, DLMs face a fundamental challenge when applied to language generation: DLMs are by design inherently bidirectional
https://arxiv.org/html/2608.03138v1
We propose StructPO, a struct-aware policy learning framework that internalizes the entire multi-stage writing workflow into a single-pass policy controlled by explicit stage tokens
https://arxiv.org/html/2608.02817v1
3 Markup–Curvature Mapping and Generic Pass-through
Cost pass-through
https://arxiv.org/html/2608.02971v1
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https://arxiv.org/html/2608.02918v1
Miranda is also with LCTI, Télécom Paris, IP Paris, France
https://arxiv.org/list/cs.CV/new
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Computer Vision and Pattern Recognition
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https://arxiv.org/html/2412.12213v2
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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/2608.03372v1
3 A word list cannot be finished
6 Where the model earns its cost
The witness perceives; it does not judge
https://arxiv.org/html/2608.02941v1
72% Pass) while systematically dismantling containment (96
https://arxiv.org/html/2608.02673v1
Continuous Autoregressive Model
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Software Engineering
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https://arxiv.org/html/2402.03300v3
com/deepseek-ai/DeepSeek-Math
Abstract
Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature
https://arxiv.org/html/2608.03624v1
In domains such as healthcare, this is a hard requirement; sensitive data cannot leave the premises, so models must run on-site, often on limited hardware Garg et al
https://arxiv.org/list/cs.IR/new
It selects coverage-aware anchors, clusters document patches, and uses a lightweight shared network to synthesize one representative per cluster
https://arxiv.org/html/2608.03411v1
com
Abstract
Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the model’s true epistemic state
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/2404.05321
Our experimental results demonstrate single pass encoding inside modern encoder implementations can give us very good quality at a reasonable compute cost
https://arxiv.org/html/2403.08299v1
8% of Pass@1 for code generation and test generation respectively, demonstrating its effectiveness in automating software engineering tasks while maintaining a secure and user-controlled development environment
https://arxiv.org/html/2608.01024v1
While the evolutionary methods can in principle be run under such non-uniform demand, their search is not informed by it: random mutation does not know which shelves are requested frequently, so placing the high-frequency shelves well is left to chance
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