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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
With the interaction history, it co-generates ground truth over six recall, reasoning, and trustworthiness evaluation dimensions
https://arxiv.org/list/cs.CL/new
With the interaction history, it co-generates ground truth over six recall, reasoning, and trustworthiness evaluation dimensions
https://arxiv.org/html/2407.02342v1
org) Wen Chen is with the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China(e-mail: wenchen@sjtu
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/2608.03048v1
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/2608.02668v1
Sphere pp-Angular Retraction Normalization
https://arxiv.org/html/2607.28410v1
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arXiv:2607
https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
https://arxiv.org/html/2510.03215v2
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https://arxiv.org/html/2608.03703v1
4 The Loop Gain: Rebalancing Pressure in One Number
3 A Model of Preying on a Mechanical Rebalancer
3
https://arxiv.org/html/2608.01366v2
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arXiv:2608
https://arxiv.org/list/cs.AI/new
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https://arxiv.org/html/2608.03099v1
Better perception or broader robot data may explain the gain
https://arxiv.org/html/2607.28577v1
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https://arxiv.org/html/2608.03141v1
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https://arxiv.org/html/2607.29281v2
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https://arxiv.org/html/2508.08980v3
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https://arxiv.org/html/2608.03494v1
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
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[17] arXiv:2608
https://arxiv.org/html/2407.16741v3
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https://arxiv.org/html/2608.02625v1
Causal ablations indicate that completed drafts are useful initializations: refinement from a fully masked span performs poorly, full global refinement provides a clear additional gain on GSM8K, and local refinement captures much of the gain on MBPP and MATH
https://arxiv.org/html/2509.02523v1
Compared to Whisper, Moonshine is especially well-suited for edge applications because its inference cost—in terms of FLOPs and corresponding latency—scales with the duration of the input audio
https://arxiv.org/list/cs.DC/new
Several components are the subject of pending United States patent applications by the first author and are presented here as candidate open architectural principles
[2] arXiv:2608
https://arxiv.org/html/2608.02999v1
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https://arxiv.org/list/eess/new
The design relies on two affine Volterra transformations involving both Fredholm- and Volterra-type integral terms, which results in a four-PIDE kernel equations
https://arxiv.org/html/2407.01757v1
Opportunities and Technology Feasibility
1 Introduction
1.1 What is a distributed instrument?
1.1.1 Distributed Instruments and Sensor Networks
1.1.2 Distributed Instruments and Swarms
1.2 Contribution and Organization
2 State of the Art
https://arxiv.org/html/2608.03486v1
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[2] arXiv:2608