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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/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/2608.02613v1
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
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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/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/abs/2510.01395
01395] Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
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https://arxiv.org/pdf/2006.02318
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard
https://arxiv.org/pdf/2608.01651v1
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/pdf/2608.03883
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard
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/abs/2607.29380
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https://arxiv.org/abs/1411.2842
2842] Logical Limitations to Machine Ethics with Consequences to Lethal Autonomous Weapons
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https://arxiv.org/pdf/2609.03103
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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/pdf/2608.01564
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https://arxiv.org/pdf/2608.02609
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https://arxiv.org/pdf/2412.12213
Finance-Informed Neural Network: Learning the Geometry of Option PricingAmine M. AboussalahXuanze LiCheng ChiRaj Patelhttp://creativecommons.org/licenses/by/4.0/cs.LGq-fin.CPstat.ML endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj
https://arxiv.org/html/2508.08980v3
org perpetual non-exclusive license
arXiv:2508
https://arxiv.org/pdf/2608.01366
) /Title (Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/html/2508.16279v1
org perpetual non-exclusive license
arXiv:2508
https://arxiv.org/pdf/2405.17770
-Neutral Generative NetworksZhonghao XianXing YanCheuk Hang LeungQi Wuhttp://creativecommons.org/licenses/by-nc-nd/4.0/q-fin.MFq-fin.PR endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj > endobj 7 0 obj > endobj 8 0 obj > endobj 9
https://arxiv.org/src/2403.08299
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https://arxiv.org/pdf/2608.02943
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https://arxiv.org/pdf/2608.02244
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2508.08980v3
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