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Multi-hop reasoning accuracy improves dramatically when LLMs dynamically assess and refine their reasoning paths instead of relying on static knowledge.
ToFu achieves superior token efficiency and cost-effectiveness in agentic coding, all while empowering researchers with a transparent, modifiable framework.
Reasoning with LLMs just got a whole lot faster: MemoSight cuts KV cache footprint by 66% and speeds up inference by 1.56x without sacrificing CoT performance.
