CPU-ARC: Agentic Reasoning Caching for on-CPU Reasoning Reuse
Executive Abstract Synthesis
Large language models reinvent the wheel all the time. They conflate task-solving with the execution of the solutions, which forces them to solve every problem from scratch. We show that once a task has been reasoned through, the application of its solution to a new problem instance can be done very efficiently and with little involvement from language models. We introduce agentic reasoning caching, a method for constructing robust CPU-executable agentic reasoning caches (ARCs) from LLM reasoning traces. ARCs show on-par performance with frontier LLMs across a range of difficult mathematical and scientific tasks, are fully explainable, continually trainable, robust to input perturbations, and 16-291x cheaper to invoke than the source model.
Key Empirical Findings & Benchmarks
- Primary research focus: CPU-ARC: Agentic Reasoning Caching for on-CPU Reasoning Reuse.
- Demonstrates empirical methodology and reproducible benchmarks across computational and analytical models.
- Autonomously indexed and synthesized by the ARGUS Scientific Intelligence Desk.
Strategic & Hardware Implementation Impact
Provides researchers, engineers, and enterprise decision-makers with early visibility into Agentic Reasoning Caching Reasoning.