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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
A million-token cloud context window does not help here, as it requires off-device upload, which privacy-sensitive personal memory management is meant to avoid
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/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/2510.03215v2
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https://arxiv.org/html/2608.03486v1
While linear SVAR models are useful in many applications, they are not able to capture nonlinear features of economic dynamics
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/2607.26245v1
No strategy iteration survived fees and spread; out-of-sample, the strongest published model does not beat, and slightly underperforms the probability already implied by Polymarket’s own order book (Section 10
https://arxiv.org/html/2607.22511v2
The source code, formal library, and run records are available at https://github
https://arxiv.org/html/2608.02918v1
Miranda is also with LCTI, Télécom Paris, IP Paris, France
https://arxiv.org/html/2608.03011v1
In many real-world application scenarios, however, the acoustic environment present in the speech signal is not merely an undesired distortion, but rather constitutes a desired auditory attribute of the synthesized speech
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/list/cs.CL/new
Prior work enables one-way, scholar-oriented translation from Akkadian to English, but offers no path in the reverse direction: non-specialist users cannot compose new content in cuneiform, and therefore remain passive consumers of ancient culture rather than active participants
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/2608.02612v1
Our code and dataset are available at https://github
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/2407.03956v3
It must also encode additional constraints: the Football and Red houses occupy House 1 or House 3, respectively, and they must not be the same house
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/2608.00785v1
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/2407.17789v2
The source code is released on GitHub111https://github
https://arxiv.org/html/2608.03446v1
com/KathyHaem/cla-metrics
2 Background and Related Work
Cross-lingual alignment
https://arxiv.org/html/2509.22186v2
\metadata
[* Equal contribution ✉ Corresponding author ‡\ddagger Project leader] \correspondenceConghui He, \metadata[Code:]https://github
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
https://arxiv.org/html/2508.16279v1
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Why HTML? Report
https://arxiv.org/html/2608.03077v1
This process-output alignment breaks the credit-assignment bottleneck in reasoning-augmented MT, allowing the model to optimize not only final translation quality which translation decisions support domain-faithful translation
https://arxiv.org/html/2508.19205v1
com/microsoft/VibeVoice
Hugging Face: microsoft/VibeVoice
Demo: aka
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