Home Agent extends Home Assistant's native conversation platform to enable natural language control and monitoring of your smart home. It works with any OpenAI-compatible LLM provider, giving you ...
OntoMem is built on the concept of Ontology Memory—structured, coherent knowledge representation for AI systems. Give your AI agent a "coherent" memory, not just "fragmented" retrieval. Traditional ...
Abstract: On-device Large Language Model (LLM) inference enables private, personalized AI but faces memory constraints. Despite memory optimization efforts, scaling laws continue to increase model ...
At the start of 2025, I predicted the commoditization of large language models. As token prices collapsed and enterprises moved from experimentation to production, that prediction quickly became ...
Abstract: Large language model (LLM) pruning with fixed N:M structured sparsity significantly limits the expressivity of the sparse model, yielding sub-optimal performance. On the contrary, support ...
When an enterprise LLM retrieves a product name, technical specification, or standard contract clause, it's using expensive GPU computation designed for complex reasoning — just to access static ...
According to Stanford AI Lab (@StanfordAILab), the newly released TTT-E2E framework enables large language models (LLMs) to continue training during deployment by using real-world context as training ...
According to Stanford AI Lab, the team released End-to-End Test-Time Training (TTT-E2E), enabling LLMs to continue training during deployment by using live context as training data to update model ...
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