KAIST's TAC-CoT cuts agent compute 2-3x while boosting accuracy up to +5.9 points
DISLab · hf · 2026-11-05
Researchers from KAIST propose TAC-CoT, a tool-empowered implicit chain-of-thought framework that replaces explicit reasoning traces with compact latent segments while keeping textual tokens only for tool interactions.
- On Agentic Math, Qwen3-4B gains +5.9 accuracy points with 2.7x fewer forward passes
- Llama-3.2-1B and Qwen2.5-0.5B see +5.8/+5.5 points with 2.2-2.5x compute savings
- Agentic QA also improves +2 points with up to 5.8x fewer forwards
The work bridges the gap between implicit CoT's efficiency and the explicit text interface agents need for tool use.
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