arXiv:2603.09221v2 Announce Type: replace Abstract: Associative memory has long underpinned the design of sequential models. Beyond recall, humans reason by projecting future states and selecting goal-directed actions, a capability that modern language models increasingly require but do not natively encode. While prior work uses reinforcement learning or test-time training, planning remains external to the model architecture. We formulate reasoning as optimal control and introduce the Test-Time Control (TTC) layer, which performs finite-horizon LQR planning over latent states at inference time
The accelerating pace of large language model capabilities is pushing researchers to integrate more sophisticated reasoning structures, moving beyond simple recall to emulate human-like foresight and planning.
This breakthrough offers a potential pathway to significantly enhance LLM reasoning, allowing them to perform complex, goal-directed tasks autonomously, which is critical for future AI applications.
Current LLMs, which primarily rely on associative memory, will be augmented with an architectural component enabling real-time, optimal control planning over latent states.
- · AI model developers
- · Robotics
- · Automation software
- · Logistics and supply chain management
- · Companies relying on simple LLM applications
- · Traditional algorithmic planning methods
- · Manual white-collar tasks
LLMs gain a more robust and native capacity for complex, goal-oriented reasoning and planning at inference time.
This improved reasoning will enable AI agents to tackle more intricate, multi-step problems and interact more effectively with dynamic environments.
Advanced AI agents, equipped with superior planning and control, could fundamentally transform industries requiring sequential decision-making, accelerating automation across diverse sectors.
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Read at arXiv cs.LG