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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
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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
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html|title=University of Massachusetts Official Athletic Site – Traditions|work=umassathletics
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It’s a high-scale, top-of-funnel product used across billions of site visits
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https://arxiv.org/html/2607.22511v2
The source code, formal library, and run records are available at https://github
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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://huggingface.co/datasets/microsoft/benchpress-score-matrix
com/eval-sys/mcpmark"},"canonical_setting_json":{"kind":"string","value":"{\"higher_is_better\": true, \"judge\": \"programmatic verification scripts\", \"metric_type\": \"pct\", \"multimodal_input\": false, \"notes\": \"Official sources are arXiv:2509
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com/rosewang2008/sight","githubRepoAddedBy":"auto","ai_summary":"A dataset and automated methods using large language models classify online student feedback efficiently, enhancing teacher improvement from lectures
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