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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
6 Ingest Overhead: When Does Structured Memory Pay Off
https://arxiv.org/html/2608.03099v1
4 System Success Alone Does Not Isolate Language
5
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/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/src/2005.13905
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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/src/2211.10435
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https://arxiv.org/list/cs.AI/new
Research artifact available under the Apache-2
https://arxiv.org/html/2608.02999v1
3 Dictionary coverage does not identify a reading
7 Discussion
What the construction establishes
https://arxiv.org/src/2608.04008
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https://arxiv.org/src/2006.01354
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https://arxiv.org/pdf/2309.08431
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https://arxiv.org/pdf/2508.02691
Statistical modeling of SOFR term structureTeemu PennanenWaleed Taoumhttp://arxiv.org/licenses/nonexclusive-distrib/1.0/q-fin.ST endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj > endobj 7 0 obj > endobj 8 0 obj > endobj 9 0 obj >
https://arxiv.org/src/2006.00342
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https://arxiv.org/pdf/2006.03178
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/src/2006.01402
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https://arxiv.org/src/2006.03594
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https://arxiv.org/src/2506.01844
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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/pdf/2608.01651v1
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https://arxiv.org/html/2609.02852v1
Description:
arXiv is now an independent nonprofit! Learn more ×
The Implications of Linguistic Illegibility for LLM Security
James Mickens Affiliation: Harvard University Email: mickens@g.harvard.edu
Abstract
LLMs are trained to
https://arxiv.org/src/2002.02145
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https://arxiv.org/pdf/2412.20138
TradingAgents: Multi-Agents LLM Financial Trading FrameworkYijia XiaoEdward SunDi LuoWei Wanghttp://creativecommons.org/licenses/by/4.0/q-fin.TRcs.AIcs.CEcs.LG endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj > endobj 7 0 obj >
https://arxiv.org/list/cs.IR/new
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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/list/cs.DC/new
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The artificial intelligence industry has constructed a USD 300 billion centralized data center infrastructure to serve a workload, large language model inference, that does not architecturally require centralization
https://arxiv.org/tb/redirect/1847908/1a6c804c6
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https://arxiv.org/src/2006.00819
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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