TAC-CoT improves agentic LLM accuracy and efficiency
Original title: TAC-CoT: Tool-Empowered Implicit Chain-of-Thought via Interleaving Explicit Tokens
TAC-CoT interleaves compact latent reasoning segments with explicit tool calls, observations, and answers to enable tool-use without verbalizing every step.
LLM-based agentic systems increasingly combine CoT reasoning with tool-use for tasks needing accurate reasoning, external knowledge, and multi-step deliberation. TAC-CoT replaces explicit textual rationales with compact latent reasoning segments while retaining textual tokens only for tool calls, observations, and final answers. This interleaved design enables agentic tool-use. Experiments on agentic math and QA benchmarks show accuracy improvements of up to 8.8 points and reductions in model forward passes of up to 6.8x. Analyses confirm improved tool-use quality in both single- and multi-tool settings.
- Accuracy gains of up to 8.8 points on benchmarks
- Model forward passes reduced by up to 6.8x
- Supports single- and multi-tool agentic settings
- Retains explicit tokens only for tool calls and observations
From the original article
1 min read at huggingface.co
LLM-based agentic systems are increasingly combined with CoT reasoning to solve tasks that require accurate reasoning, external knowledge, and multi-step deliberation. Yet, explicit CoT incurs substantial generation overhead, while implicit CoT, which replaces textual rationales with compact continuous tokens, does not naturally support the explicit communication interface required for agentic tool-use.
By Juseon Do, Sangmin Bae, Jeesu Jung · huggingface.co
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