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30 result(s) for “okapi bm25 site terms com pp site levine ca site does could good com site draft site:arxiv.org” of 38 matched · 862 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
6 Ingest Overhead: When Does Structured Memory Pay Off
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/2608.03099v1
4 System Success Alone Does Not Isolate Language
5
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/2609.03103v1
The accumulated exposure pattern is set by the history of GRBs, ToOs, orbital visibility, and scheduling constraints, so Swift does not tile a prescribed sky region on a controlled lattice or maintain a planned soft X-ray depth and revisit pattern
https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
https://arxiv.org/html/2407.01757v1
Pasadena (CA) 91109, USA Saptarshi Bandyopadhyay Jet Propulsion Laboratory - California Institute of Technology
4800 Oak Grove Dr
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/list/cs.AI/new
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Artificial Intelligence
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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/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2608.02625v1
Draft–then–refine and progressive diffusion
https://arxiv.org/html/2410.05779v3
com Both Chao Huang and Yanhua Yu are corresponding authors
https://arxiv.org/html/2407.19144v1/
To overcome this limitation, we present the Collaborative Adaptation (CA) framework, highlighting its unique capability to operate in both continuous and discrete domains
https://arxiv.org/html/2608.03545v1
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https://arxiv.org/html/2506.01844v1
7 Ablation Study
Cross-attention (CA) vs
https://arxiv.org/html/2608.03138v1
During training, LLMs generate draft–revision trajectories and drafts that are substantially improved by their revisions receive an additional penalty
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/list/cs.IR/new
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Information Retrieval
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https://arxiv.org/list/cs.IR/recent
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Information Retrieval
Authors and titles for recent submissions
Wed, 5 Aug 2026
https://arxiv.org/html/1802.00048v2
Tandon School of Engineering, New York University, New York, USA John Levine Affiliation: Computer and Information Science Department, University of Strathclyde, Glasgow, UK, E-mail Damien
https://arxiv.org/html/2407.00632v1
End-to-end model-based methods exhibit good transferability but are relatively ineffective in navigation efficiency and task success rates [5]
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/list/cs.SY/new
The standard tools, classical and modern, each fail quantifiably: probing is punished before it pays, commitment is fatal at sub-disturbance excitation once adaptation is necessary, optimism degenerates to tie-breaking or pays asymptotically at least twice the optimum, and regret certificates are blind to the worst
https://arxiv.org/list/econ.TH/new
We study the overlapping-generations learning model of Fudenberg and Levine (1993), in which finitely lived Bayesian agents are repeatedly and randomly matched with agents in other player roles, observe only their own matches, and learn from experience
https://arxiv.org/html/2607.28127v1
FinSMART transforms realized market outcomes into a direct supervision signal via a three-stage pipeline: (1) Data Alignment: Financial news articles are filtered and synchronized with publication-day market data to construct clean training pairs; (2) Dual-Filter Reward Evaluation: The behavior policy LLM generates
https://arxiv.org/list/cs.LG/2022-12
Lee, Claire Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni
Comments: Appeared in L4DC 2023
https://arxiv.org/list/cs.CL/2022-10
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Computation and Language
Authors and titles for October 2022
Total of 1397