Arbor's tree-search cognition layer slashes LLM inference latency by 193%
Arbor introduces a shared tree-search memory that coordinates multiple specialist agents, letting them treat failures as diagnostic signals and expand the search as successes shift bottlenecks. Tested on full-stack LLM inference, the system delivered up to a 193% Pareto improvement in throughput‑latency versus vendor‑optimized baselines, while staying hardware-agnostic.