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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/2608.01085v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03703v1
begun on July 20, 2026; the present version is dated August 4, 2026. Claude Opus 5 and Fable 5 provided research assistance throughout, spanning code development, data construction and empirical analysis, and the drafting of the manuscript.
https://arxiv.org/list/math/new
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.AI/new
The implemented contract fixes evidence non-overwrite, branch isolation, grounded multi-subject timers, guarded state change, and declaration-ranked event ordering over time and space; an external runner still decides whether evidence becomes an authoritative move
https://arxiv.org/html/2508.02739v1
, Jian Li1
Abstract
The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs)
https://arxiv.org/html/2608.00648v1
In practical scenarios, constraints such as time windows make task feasibility highly dependent on each task’s position in the planned sequence, thereby requiring more carefully coordinated task-planning strategies [17]
https://arxiv.org/list/cs.DC/new
DC)
The artificial intelligence industry has constructed a USD 300 billion centralized data center infrastructure to serve a workload, large language model inference, that does not architecturally require centralization
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/2608.03446v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/list/cs.CV/new
Project Page: this https URL
[2] arXiv:2608
https://arxiv.org/html/2608.03529v1
(2021), and defining codebooks is time consuming and expensive Dai et al
https://arxiv.org/html/2608.02918v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03011v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03545v1
org perpetual non-exclusive license
arXiv:2608
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/2608.03077v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.02613v1
6 Ingest Overhead: When Does Structured Memory Pay Off
https://arxiv.org/html/2608.03573v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/tb/2005.14165
org/trackback/{arXiv_id}
Some blogging software supports trackback autodiscovery -- in this case, your software will automatically send a trackback as soon as your create a link to our abstract page
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/html/2608.01024v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2509.02523v1
Low latency ASR models that are small enough to deploy on-device can empower a new generation of voice-driven applications, from real-time translation devices to intelligent conversational interfaces in automobiles (Rege et al
https://arxiv.org/html/2608.03154v1
org perpetual non-exclusive license
arXiv:2608
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/list/cs.IR/new
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[1] arXiv:2608
https://arxiv.org/html/2608.03494v1
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arXiv:2608
https://arxiv.org/list/astro-ph.IM/new
IM)
Real-time processing on sub-millisecond timescales is essential for detecting fast astrophysical transients such as fast radio bursts (FRBs) and prompt electromagnetic counterparts to gravitational-wave mergers
https://arxiv.org/html/2607.28127v1
Uniquely, the FinSMART framework naturally supports market-aware retraining, at any point in time, by replacing costly manual annotation with newly observed financial articles and their realized market outcomes