IoT-Brain uses a neuro-symbolic Spatial Trajectory Graph to ground LLMs for verifiable semantic-spatial sensor scheduling, achieving 37.6% higher task success with lower resource use on a campus-scale benchmark.
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Plainbook makes data-science notebooks natural-language-first by preserving cell descriptions, generating code via AI, enforcing linear execution via a checkpointing kernel, and adding value-centered cell and global tests.
PLENA introduces a co-designed system with three optimization pathways for long-context agentic LLM inference, claiming up to 2.23x throughput over A100 and 4.04x energy efficiency.
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IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling
IoT-Brain uses a neuro-symbolic Spatial Trajectory Graph to ground LLMs for verifiable semantic-spatial sensor scheduling, achieving 37.6% higher task success with lower resource use on a campus-scale benchmark.
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Plainbook: Data Science, in Plain Language
Plainbook makes data-science notebooks natural-language-first by preserving cell descriptions, generating code via AI, enforcing linear execution via a checkpointing kernel, and adding value-centered cell and global tests.
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Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
PLENA introduces a co-designed system with three optimization pathways for long-context agentic LLM inference, claiming up to 2.23x throughput over A100 and 4.04x energy efficiency.