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For each evaluation sample, we construct an Okapi BM25 index over its decoded text chunks; the index is therefore sample-specific rather than a shared external knowledge base
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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/2608.02613v1
6 Ingest Overhead: When Does Structured Memory Pay Off
https://arxiv.org/list/cs.AI/new
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We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/html/2501.13956v1
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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.IR/new
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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
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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.
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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
https://arxiv.org/tb/2005.14165
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https://arxiv.org/html/2608.03494v1
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arXiv:2608
https://arxiv.org/html/2407.16741v3
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https://arxiv.org/html/2608.03035v1
Moreover, the truth of factual statements usually does not need to be updated throughout the course of a conversation, whereas an LLM needs to constantly update its representation of a contextual statement: in multi-agent collaborative settings, “truth” is often whatever has been established in the conversation
https://arxiv.org/html/2607.28127v1
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https://arxiv.org/html/2608.00648v1
Distributed Multi-Agent Systems
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https://arxiv.org/html/2608.01024v1
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https://arxiv.org/html/2608.03086v1
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https://arxiv.org/html/2608.02616v1
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We present the first independent, systematic evaluation of OpenAI’s Privacy Filter (OPF), a 1
https://arxiv.org/html/2608.03411v1
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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
https://arxiv.org/html/2608.03154v1
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https://arxiv.org/html/2510.03215v2
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https://arxiv.org/html/2407.02518v2
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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/2608.03446v1
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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
https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking
https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs
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