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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

Report GitHub Issue

https://arxiv.org/html/2608.02613v1

We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

Artificial Intelligence

https://arxiv.org/list/cs.AI/new

We demonstrate that it can self-assemble functional circuits from scratch and rapidly re-route logic around permanent, previously unseen hardware faults

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https://arxiv.org/html/2608.03048v1

for large language models, as recent recurrent-memory approaches face two inherent challenges: sequential chunk-wise updates can overwrite early critical evidence with later irrelevant content, and serial inter-chunk dependencies limit parallelism and cause latency to increase with context length

1 Introduction

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

1 Introduction

https://arxiv.org/html/2407.16741v3

com/All-Hands-AI/OpenHands Slack http://bit

1 Introduction

https://arxiv.org/html/2508.02739v1

2 Preliminary 3 Methodology K-line Tokenization Hierarchical Autoregressive Modeling Model Pre-training Dataset Model Training Inference 4 Experiments Experimental Setup Main Results Prediction Tasks Generative Tasks Investment

1 Introduction

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

Information Retrieval

https://arxiv.org/list/cs.IR/new

Our results show that skill representation itself matters, and that simply preserving the structure already present in skill files can substantially improve retrieval

Hardware Architecture

https://arxiv.org/list/cs.AR/new

more | all New submissions (showing 4 of 4 entries) [1] arXiv:2608

Instrumentation and Methods for Astrophysics

https://arxiv.org/list/astro-ph.IM/new

In particular, the maximal light deflection of a given Solar System body can be easily estimated for a given observer

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https://arxiv.org/html/2608.03411v1

com Abstract Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the model’s true epistemic state

Computation and Language

https://arxiv.org/list/cs.CL/new

LG) Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world's oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed

Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/new

Project Page: this https URL [2] arXiv:2608

Mathematics

https://arxiv.org/list/math/new

up to 2000 entries per page: fewer | more | all New submissions (showing 219 of 219 entries) [1] arXiv:2608

Electrical Engineering and Systems Science

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

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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

Beyond Initialization Loss: A Systematic Study of Token Embedding

https://arxiv.org/html/2608.03494v1

org perpetual non-exclusive license arXiv:2608

Automatic Patient-Specific Microwave Ablation Planning Accelerated by a

https://arxiv.org/html/2608.03086v1

org perpetual non-exclusive license arXiv:2608

Report GitHub Issue

https://arxiv.org/html/2608.03154v1

org perpetual non-exclusive license arXiv:2608

Predicting Multilingual Classification and Translation Performance of

https://arxiv.org/html/2608.03446v1

org perpetual non-exclusive license arXiv:2608

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

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

Article Trackbacks

https://arxiv.org/tb/2005.14165

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[2404.05321] Unravelling the Power of Single-Pass Look-Ahead in Modern

https://arxiv.org/abs/2404.05321

05321] Unravelling the Power of Single-Pass Look-Ahead in Modern Codecs for Optimized Transcoding Deployment Skip to main content Learn about arXiv becoming an independent nonprofit