Enterprise agents need more than RAG. Our survey maps how LLMs can build the structured grounding layer—vocabulary, constraints, actions, governance, and maintenance—that makes agents actually deployable.
Led by Zhongwei Xie, our survey maps the four-layer Agent Harness stack—execution, context, interaction, and guardrails—and why it may matter as much as the model itself.
How LeCun's vision for world models and knowledge graph reasoning converge on the same architecture - and what that means for both fields.
A short overview of my Home Depot talk on building knowledge graphs, from rich semantics to autonomous construction and task-aware optimization.
AutoSchemaKG, AutoGraph R1, and multimodal abductive reasoning—three angles on building and using knowledge for language models.
Talk on how we move from symbolic knowledge graphs to neural graph databases, and what this means for data, algorithms, and real-world applications.
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