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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/html/2608.03048v1
PI-Mem gathers evidence from all chunks in parallel and uses it to update a shared memory over a bounded number of turns
https://arxiv.org/html/2407.16741v3
2024), user-facing AI systems (such as ChatGPT) have become increasingly capable of performing complex tasks such as accurately responding to user queries, solving math problems, and generating code
https://arxiv.org/list/cs.AI/new
Biological systems, in contrast, exhibit adaptive plasticity, maintaining function through dynamic re-organisation around damage
https://arxiv.org/html/2608.00458v1
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arXiv:2608
https://arxiv.org/html/2608.03099v1
In other systems, it directly conditions the action policy (Zitkovich et al
https://arxiv.org/html/2608.02918v1
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arXiv:2608
https://arxiv.org/html/2608.01085v1
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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/html/2608.03655v1
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https://arxiv.org/html/2407.00575v3
In large-scale systems, obtaining performance guarantees is challenging even with cooperative players since they do not know the reward functions of their peers
https://arxiv.org/html/2607.28847v1
The European Union’s Artificial Intelligence Act classifies creditworthiness assessment as a high-risk application, requiring that automated systems undergo rigorous fairness evaluation before deployment and throughout their operational lifecycle (European Parliament, 2024)
https://arxiv.org/html/2501.13956v1
2 Knowledge Graph Construction
2.1 Episodes
2.2 Semantic Entities and Facts
2.2.1 Entities
2.2.2 Facts
2.2.3 Temporal Extraction and Edge Invalidation
2.3 Communities
3 Memory Retrieval
3.1 Search
3.2 Reranker
4 Experiments
4.1 Choice
https://arxiv.org/list/cs.IR/new
It selects coverage-aware anchors, clusters document patches, and uses a lightweight shared network to synthesize one representative per cluster
https://arxiv.org/html/2608.03011v1
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https://arxiv.org/html/2607.28577v1
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https://arxiv.org/html/2609.02852v1
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https://arxiv.org/html/2607.27188v1
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https://arxiv.org/html/2608.00648v1
MA] 01 Aug 2026
MDGAM-Based Cooperative Task Scheduling for Communication-Constrained Distributed Multi-Agent Systems
Licheng Wang Mingtao Huang Yuan Shen Thanks: The authors are with the Department of Electronic Engineering, and Beijing National Research Center for Information Science and Technology
https://arxiv.org/html/2407.10811v1
3Ziyue Li and Wolfgang Ketter are with the Information Systems Department at the University of Cologne, Germany
https://arxiv.org/html/2609.03159v1
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https://arxiv.org/html/2608.02668v1
Sphere pp-Angular Retraction Normalization
https://arxiv.org/html/2608.02909v1
For example, an LLM’s estimate of an article’s slant or the emotional content of text may only be a proxy for the true value, as it uses a limited sample and subject to idiosyncratic (e
https://arxiv.org/html/2608.01366v2
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https://arxiv.org/html/2607.28410v1
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https://arxiv.org/html/2608.02971v1
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https://arxiv.org/html/2608.03545v1
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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.
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M