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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
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/list/cs.CL/new
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/list/cs.AI/new
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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/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
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https://arxiv.org/html/2510.03215v2
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https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
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/2608.03011v1
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arXiv:2608
https://arxiv.org/html/2608.01085v1
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arXiv:2608
https://arxiv.org/list/cond-mat.mes-hall/new
Together with metallic transport and a crystal-site density much lower than the doped carrier density, this supports metallic electron crystals in which a subset of carriers crystallizes
https://arxiv.org/html/2608.02620v1
com/index/retiring-gpt-4o-and-older-models
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/2608.02975v1
com
Abstract
Large language models (LLMs) have demonstrated impressive performance in MQM-based translation quality (TQ) evaluation, and recent advances in large reasoning models (LRMs) promise even greater improvements
https://arxiv.org/html/2410.05779v3
com Both Chao Huang and Yanhua Yu are corresponding authors
https://arxiv.org/html/2608.02612v1
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arXiv:2608
https://arxiv.org/html/2608.03035v1
Fitting a linear probe on these factual true and false statements makes it difficult to separate “the model encodes the proposition pp as true” from “the model has memorized the proposition pp”, which is often sensitive to presence or absence of content in pretraining data
https://arxiv.org/list/quant-ph/new
In this work, we initiate the study of a fundamental theorem for matrix product locally purifiable density operators (LPDOs) and focus on sequentially generated LPDOs (sLPDOs), a broad subclass admitting an interpretation in terms of successive applications of quantum channels on an initial state
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
https://arxiv.org/list/cs.IR/recent
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[17] arXiv:2608
https://arxiv.org/list/cs.IR/new
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https://arxiv.org/abs/2608.04003
We report both later-task gains and whether those gains follow the intended save, retrieve, and update pathway
https://arxiv.org/html/2403.08299v1
The Eval Environment executes the suggested operations, providing the AI Agent with the resulting outcome
https://arxiv.org/html/2608.02673v1
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