Efficient Reasoning via Thought Compression for Language Segmentation

• URL: http://arxiv.org/abs/2604.02040v1
• Date: Thu, 02 Apr 2026 13:45:01 GMT
• Title: Efficient Reasoning via Thought Compression for Language Segmentation
• Abstract summary: Chain-of-thought (CoT) reasoning has significantly improved the performance of large multimodal models in language-guided segmentation.<n>We introduce WISE, a novel paradigm for efficient reasoning guided by the principle of textitthinking twice -- once for learning, once for speed.<n>We show WISE achieves state-of-the-art zero-shot performance on the ReasonSeg benchmark with 58.3 cIoU, while reducing the average reasoning length by nearly textbf5$times$ -- from 112 to just 23 tokens.
• Score: 28.138192925569317
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Chain-of-thought (CoT) reasoning has significantly improved the performance of large multimodal models in language-guided segmentation, yet its prohibitive computational cost, stemming from generating verbose rationales, limits real-world applicability. We introduce WISE (Wisdom from Internal Self-Exploration), a novel paradigm for efficient reasoning guided by the principle of \textit{thinking twice -- once for learning, once for speed}. WISE trains a model to generate a structured sequence: a concise rationale, the final answer, and then a detailed explanation. By placing the concise rationale first, our method leverages autoregressive conditioning to enforce that the concise rationale acts as a sufficient summary for generating the detailed explanation. This structure is reinforced by a self-distillation objective that jointly rewards semantic fidelity and conciseness, compelling the model to internalize its detailed reasoning into a compact form. At inference, the detailed explanation is omitted. To address the resulting conditional distribution shift, our inference strategy, WISE-S, employs a simple prompting technique that injects a brevity-focused instruction into the user's query. This final adjustment facilitates the robust activation of the learned concise policy, unlocking the full benefits of our framework. Extensive experiments show that WISE-S achieves state-of-the-art zero-shot performance on the ReasonSeg benchmark with 58.3 cIoU, while reducing the average reasoning length by nearly \textbf{5$\times$} -- from 112 to just 23 tokens. Code is available at \href{https://github.com/mrazhou/WISE}{WISE}.

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