让复杂推理更高效透明，自动压缩冗余步骤并说明原因
Bridging Efficiency and Transparency: Explainable CoT Compression in Multimodal Large Reasoning Models
- 用强化学习把多模态推理链当作决策过程来压缩
- 压缩后推理长度减少，准确率保持不变，还生成解释
- 适合需要可解释性推理的AI系统开发者
长链条思维（Long CoTs）在多模态推理模型中被广泛用于处理复杂任务，通过捕捉详细的视觉信息。然而，这些长链通常过于冗长，包含重复推理步骤，影响推理效率。压缩长推理链是自然解决方案，但现有方法面临两大挑战：(1) 可能破坏视觉-文本对齐的关键线索，损害推理完整性；(2) 压缩过程缺乏可解释性，难以判断哪些信息关键。为此，我们提出XMCC——一种可解释的多模态思维链压缩器，将压缩建模为通过强化学习优化的序列决策过程。XMCC能有效缩短推理轨迹，同时保留关键步骤与答案正确性，并生成自然语言解释其压缩决策。在多个代表性多模态推理基准上的实验表明，XMCC不仅降低推理长度，还提供可解释性说明，验证了其有效性。
原文摘要 · Abstract (English)
Long chains of thought (Long CoTs) are widely employed in multimodal reasoning models to tackle complex tasks by capturing detailed visual information. However, these Long CoTs are often excessively lengthy and contain redundant reasoning steps, which can hinder inference efficiency. Compressing these long CoTs is a natural solution, yet existing approaches face two major challenges: (1) they may compromise the integrity of visual-textual reasoning by removing essential alignment cues, and (2) the compression process lacks explainability, making it difficult to discern which information is critical. To address these problems, we propose XMCC, an eXplainable Multimodal CoT Compressor that formulates compression as a sequential decision-making process optimized via reinforcement learning. XMCC can effectively shorten reasoning trajectories while preserving key reasoning steps and answer correctness, and simultaneously generates natural-language explanations for its compression decisions. Extensive experiments on representative multimodal reasoning benchmarks demonstrate that XMCC not only reduces reasoning length but also provides explainable explanations, validating its effectiveness.
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