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This study synthesizes adjacent research and introduces Enactive AI as a conceptual framework for enterprise and industry reasoning, site-level decision support, and execution feedback
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https://arxiv.org/list/cs.AI/new
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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
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/html/2608.03048v1
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https://arxiv.org/html/2407.16741v3
2 OpenHands Architecture
2.1 Agent Definition and Implementation
2.2 Agent Runtime: How Execution of Actions Results in Observations
2.3 Agent Skills: The Extensible Agent-Computer Interface
2.4 Agent Delegation: Cooperative Multi-agent
https://arxiv.org/pdf/2006.02318
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/pdf/2608.03883
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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/abs/2510.01395
01395] Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/pdf/2407.00345
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https://arxiv.org/pdf/2608.02943
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https://arxiv.org/src/2205.11916v1
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https://arxiv.org/pdf/2608.01564
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https://arxiv.org/pdf/2608.02244
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https://arxiv.org/html/2508.08980v3
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https://arxiv.org/src/2006.02318
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https://arxiv.org/pdf/2508.02685
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https://arxiv.org/pdf/2608.02625
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https://arxiv.org/pdf/2604.05327v4
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