Arbor applies tree search as a cognition layer across Orchestrator and Critic agents to run multi-day autonomous optimization campaigns, reporting up to 193% throughput-latency gains over vendor baselines on LLM inference.
Evolution Through Large Models
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LLM-driven program mutation converges to restricted structural attractors, with 87% of chains showing over 93% structural revisits and most variation limited to terminal substitutions, unlike classical GP.
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Arbor: Tree Search as a Cognition Layer for Autonomous Agents
Arbor applies tree search as a cognition layer across Orchestrator and Critic agents to run multi-day autonomous optimization campaigns, reporting up to 193% throughput-latency gains over vendor baselines on LLM inference.
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Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution
LLM-driven program mutation converges to restricted structural attractors, with 87% of chains showing over 93% structural revisits and most variation limited to terminal substitutions, unlike classical GP.
- Test-Time Compute for Frozen Embedding Models through Agentic Program Search