GRACE is proposed, a Graph-based Rationalized CoT Compression framework that transforms long CoTs into reasoning graphs to enable efficient CoT compression, where reasoning steps are represented as nodes and semantic dependencies as edges.
Yizhi Wang, Li-Nan Yue, Deng-Bao Wang et al.· Proceedings of the 32nd ACM...· 0 citations
A straightforward feature mixing approach for pre-trained Transformers is presented, which combines multi-layer representations via calculated importance weights, and identifies OOD samples using Mahalanobis distance in the blended feature space.
Tong Wei, Bolin Wang, Jiang-Xin Shi et al.· Neural Information Processin...· 8 citations· ⚡2
Recent advances in multimodal large reasoning models (MLRMs) have demonstrated impressive capabilities on complex multimodal tasks, yet their reliance on long Chain-of-Thoughts (CoTs) often leads to redundant reasoning and high computational cost. Existing chain-based distillation and refinement approaches alleviate re...
Yizhi Wang, Linan Yue, Deng-Bao Wang et al.· Proceedings of the 32nd ACM...· 0 citations
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