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30 result(s) for “okapi bm25 site hotchips pp site github com site org de site does github com site:arxiv.org” of 37 matched · 799 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/html/2608.02668v1
com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (),
Correspondence: Cheng-Fang Su ()
“Geometry is not true, it is advantageous
https://arxiv.org/html/2608.03063v1
Our code is available at https://github
https://arxiv.org/list/cs.AI/new
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https://arxiv.org/html/2607.22511v2
The source code, formal library, and run records are available at https://github
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/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2608.02918v1
Miranda is also with LCTI, Télécom Paris, IP Paris, France
https://arxiv.org/html/2608.03372v1
org GitHub
sourcecontextcandidatememoryalign claimscontent-word overlapseven checkspolarity ⋅\cdot condition ⋅\cdot temporalhedge ⋅\cdot attributionbrittle ⋅\cdot truncatedflagsevidence + citationverdict policyhard-codedpasspass_w_flagsrewriterejectuncheckablenever a passwitness (optional)one sentence, one
https://arxiv.org/html/2608.03011v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03486v1
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https://arxiv.org/html/2607.26245v1
1 What This Enables That Prior Work Does Not
12
https://arxiv.org/html/2608.02616v1
com
Abstract
We present the first independent, systematic evaluation of OpenAI’s Privacy Filter (OPF), a 1
https://arxiv.org/html/2509.22186v2
\metadata
[* Equal contribution ✉ Corresponding author ‡\ddagger Project leader] \correspondenceConghui He, \metadata[Code:]https://github
https://arxiv.org/html/2608.02609v1
Code and pretrained model: https://github
https://arxiv.org/html/2210.00131v4
Code and open-source demos available at https://github
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/2608.02612v1
Our code and dataset are available at https://github
https://arxiv.org/html/2608.03446v1
com/KathyHaem/cla-metrics
2 Background and Related Work
Cross-lingual alignment
https://arxiv.org/html/2407.17789v2
The source code is released on GitHub111https://github
https://arxiv.org/html/2508.16279v1
org perpetual non-exclusive license
arXiv:2508
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/2508.19205v1
com/microsoft/VibeVoice
Hugging Face: microsoft/VibeVoice
Demo: aka
https://arxiv.org/html/2608.00785v1
com
Abstract
Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs
https://arxiv.org/html/2407.02518v2
com
Abstract
Large language models (LLMs) for code are typically trained to align with natural language instructions to closely follow their intentions and requirements
https://arxiv.org/html/2407.04503v4
fr Inria, Flowers team, Université de Bordeaux, France Grgur Kovač Inria, Flowers team, Université de Bordeaux, France Corentin Léger Inria, Flowers team, Université de Bordeaux, France Cédric Colas Inria, Flowers team, Université de Bordeaux, France MIT, Computational Cognitive Science Lab, Cambridge, MA, USA
https://arxiv.org/list/cs.CL/new
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/html/2608.03077v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2407.03956v3
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https://arxiv.org/html/2403.08299v1
The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot