We present TOC-Agent, a self-optimizing agent orchestration framework that applies Theory of Constraints (TOC) principles to multi-agent systems. Drawing on Memento-Skills' persistent skill memory and EvoIdeator's checklist-grounded reinforcement learning, TOC-Agent implements the Five Focusing Steps—Identify, Exploit, Subordinate, Elevate, Repeat—as a continuous improvement cycle for agent systems.
paperxpaper discovers every meaningful connection between two research papers by applying Goldratt's Theory of Constraints (TOC) to the connection-finding problem. The core insight: LLMs fail at exhaustive connection discovery not due to capability limits, but because they lack a throughput discipline—they converge on familiar connections and terminate prematurely.
We present TOCLINK, a ~180-line AI agent that discovers every meaningful connection between two research papers by applying Goldratt's Theory of Constraints (TOC) to the connection-finding problem. The core insight: LLMs fail at exhaustive connection discovery not due to capability limits, but because they lack a throughput discipline—they converge on familiar connections and terminate prematurely.
We present TOCLINK, an ultra-minimal AI agent that discovers every meaningful connection between two research papers by treating connection-finding as a throughput optimization problem. The agent implements Goldratt's Five Focusing Steps directly: identify the lowest-coverage connection dimension, exploit it maximally, subordinate all other reasoning to feed it, elevate if stuck, repeat.