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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/2608.03048v1
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https://arxiv.org/list/cs.AI/new
Research artifact available under the Apache-2
https://arxiv.org/pdf/2608.02851v1
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https://arxiv.org/pdf/2508.07408
-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant TradingYueyi WangQiyao Weihttp://creativecommons.org/licenses/by/4.0/q-fin.STcs.CLcs.LG endstream endobj 4 0 obj > endobj 5 0 obj
https://arxiv.org/pdf/2508.16279
AgentScope 1.0: A Developer-Centric Framework for Building Agentic ApplicationsDawei GaoZitao LiYuexiang XieWeirui KuangLiuyi YaoBingchen QianZhijian MaYue CuiHaohao LuoShen LiLu YiYi YuShiqi HeZhiling LuoWenmeng ZhouZhicheng ZhangXuguang
https://arxiv.org/pdf/2608.00426
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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/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/src/2407.17789
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https://arxiv.org/src/2006.03271
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/src/2005.14165
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https://arxiv.org/src/2608.03882
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https://arxiv.org/src/2608.04003
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https://arxiv.org/src/2006.02055
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https://arxiv.org/src/2006.03318
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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/src/2402.03300
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https://arxiv.org/pdf/2608.02609
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https://arxiv.org/pdf/2510.03215
7 % 1 0 obj > endobj 2 0 obj > endobj 3 0 obj > stream Cache-to-Cache: Direct Semantic Communication Between Large Language ModelsTianyu FuZihan MinHanling ZhangJichao YanGuohao DaiWanli OuyangYu Wanghttp://arxiv
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/pdf/2608.01861
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https://arxiv.org/list/cs.IR/new
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https://arxiv.org/pdf/2508.02686
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https://arxiv.org/pdf/2608.03138
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https://arxiv.org/pdf/2006.02055
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https://arxiv.org/pdf/2002.02145
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