CPU-ARC enables efficient on-CPU LLM reasoning reuse
Original title: CPU-ARC: Agentic Reasoning Caching for on-CPU Reasoning Reuse
CPU-ARC builds agentic reasoning caches from LLM traces matching frontier models on math tasks at 16-291x lower cost.
LLMs solve every problem from scratch forcing recomputation. CPU-ARC constructs robust agentic reasoning caches from reasoning traces for efficient CPU execution. ARCs match frontier LLMs on difficult mathematical and scientific tasks are explainable continually trainable robust to perturbations and 16-291x cheaper.
- Matches frontier LLMs on math and science tasks
- 16-291x cheaper invocation
- Fully explainable and trainable
- Robust to input perturbations
- Built from LLM reasoning traces
From the original article
1 min read at arxiv.orgLarge 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.
By Peter Belcak · arxiv.org
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