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RESEARCH
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Daily Morning Research
Every morning we sit down with a paper. We read it carefully, break down the math in plain language, find the cross-domain resonance with what we are building, and publish what we learn — pointing everyone back to the primary source. This is a learning resource. The researchers whose work appears here deserve to be read, cited, and built upon. We are grateful they publish openly.
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2026
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2026.03.05
RW-RESEARCH-003
arXiv:2511.18354v1
Toward an AI-Native Internet: Rethinking the Web Architecture for Semantic Retrieval
King Abdullah University of Science and Technology (KAUST)
Serving semantically chunked units instead of full HTML documents delivers statistically equivalent answer quality at 13–19% of the data — a 74–87% bandwidth reduction — pointing toward a web where meaning, not documents, is the fundamental unit of retrieval.
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cs.NI · cs.IR · cs.AI
2026.03.04
RW-RESEARCH-002
arXiv:2409.04701v3
Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Jina AI GmbH · Weaviate B.V.
Inverting the operation order — embedding the full document before applying chunk boundaries — produces contextual chunk embeddings that consistently outperform naive chunking across every model and dataset tested, at no additional computational cost.
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cs.CL · cs.IR
2026.03.03
RW-RESEARCH-001
arXiv:2506.17580
WISE: Workflow for Intelligent Scientific Knowledge Extraction on Linked Open Data
University of Idaho — Department of Computer Science
A recursive, score-guided traversal of linked data sources achieved 0.84 recall on a biomedical extraction benchmark — versus 0.47 for GPT-4o and 0.15 for Google Search — by measuring unique contribution per source rather than ranking by authority or popularity.
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cs.IR · cs.AI · cs.DL
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Papers Read:2
Chain Start:2026.03.03
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