TAC-CoT: Tool-Empowered Implicit Chain-of-Thought via Interleaving Explicit Tokens

TAC-CoT is a framework that replaces explicit chain-of-thought reasoning with compact latent reasoning segments, keeping only textual tokens for tool calls, observations, and final answers. This interleaved design allows large language model agents to use tools without generating full verbal reasoning steps. Experiments on math and QA benchmarks show up to 8.8‑point accuracy gains and a 6.8× reduction in model forward passes, while also improving tool‑use quality in single- and multi‑tool settings.

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• llms
• agents
• benchmarks