OpenJarvis decomposes personal AI into Intelligence, Engine, Agents, Tools & Memory, and Learning primitives and applies LLM-guided spec search to produce on-device configurations that reach within 3.2 pp of cloud baselines on average across eight tasks.
Fast inference from transformers via speculative decoding
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2026 2verdicts
UNVERDICTED 2representative citing papers
Agentic AI systems should be designed as marginal token allocators that balance benefit against cost, latency, and risk across their layers rather than as unit-priced text generators.
citing papers explorer
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OpenJarvis: Personal AI, On Personal Devices
OpenJarvis decomposes personal AI into Intelligence, Engine, Agents, Tools & Memory, and Learning primitives and applies LLM-guided spec search to produce on-device configurations that reach within 3.2 pp of cloud baselines on average across eight tasks.
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Agentic AI Systems Should Be Designed as Marginal Token Allocators
Agentic AI systems should be designed as marginal token allocators that balance benefit against cost, latency, and risk across their layers rather than as unit-priced text generators.