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. We propose TAC-CoT, a tool-empowered adaptive implicit CoT framework that replaces explicit reasoning traces with compact latent reasoning segments while retaining textual tokens only for tool calls, observations, and final answers. This interleaved design enables agentic tool-use without verbalizing every intermediate reasoning step. Experiments on agentic math and QA benchmarks show that TAC-CoT improves accuracy by up to 8.8 points while reducing model forward passes by up to 6.8x. Furthermore, analyses show that TAC-CoT improves tool-use quality in both single- and multi-tool settings.
Jeesu Jung Korea Advanced Institute of Science & Technology, Sangkeun Jung Chungnam National University
Sentence-level explanations can miss the bigger picture of how a black-box model behaves across data, which matters most for complex criteria like safety that cannot be defined by a single rule. We trace Logit-Trajectory, which tracks adjacent-layer logit updates as vectors and aggregates them into a reproducible dataset-level trajectory pattern, enabling depth-wise explainability through signals such as coherence and angular rotation. Across 6 languages and 5 NLP tasks, we show these trajectory summaries reveal consistent depth-wise patterns that divergence- and similarity-based baselines often wash out due to scalarization. As a case study where dataset-level intermediate decision structure matters, we evaluate safety classification, reporting both trajectory-level visual separability and classification performance.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表