While Large Language Models (LLMs) have emerged with remarkable capabilities in complex tasks through Chain-of-Thought (CoT) reasoning, practical resource constraints have sparked interest in transferring these abilities to smaller models. However, achieving both domain performance and cross-domain generalization remains challenging. Existing approaches typically restrict students to following a single golden rationale and treat different reasoning paths independently. Due to distinct inductive biases and intrinsic preferences, alongside the student’s evolving capacity and reasoning preferences during training, a teacher’s "optimal" rationale could act as out-of-distribution noise. This misalignment leads to a degeneration of the student’s latent reasoning distribution, causing suboptimal performance. To bridge this gap, we propose MIND, a capability-adaptive framework that transitions distillation from passive mimicry to active cognitive construction. We synthesize diverse teacher perspectives through a novel "Teaching Assistant" network. By employing a novel Feedback-Driven Inertia Calibration mechanism, this network utilizes inertia-filtered training loss to align supervision with the student’s current adaptability, effectively enhancing performance while mitigating catastrophic forgetting. Extensive experiments demonstrate that MIND achieves state-of-the-art performance on both in-distribution and out-of-distribution benchmarks, and our sophisticated latent space analysis further confirms the mechanism of reasoning ability internalization.

Speculative decoding has emerged as a promising technique to accelerate large language model inference by employing a smaller draft model to predict multiple tokens, which are then verified in parallel by the larger target model. However, existing approaches face a fundamental limitation: candidates at the same tree layer share identical feature representations, constraining diversity and diminishing overall effectiveness. We identify this as an intra-layer coupling problem that limits prediction accuracy. To address this challenge, we propose Jakiro, which introduces decoupled Mixture of Experts (MoE) into the draft model, enabling different experts to generate diverse candidate tokens from distinct feature spaces. We further propose Contrastive-Enhanced Parallel Decoding (CEPD) that combines autoregressive and parallel decoding with a contrastive mechanism to reduce inference steps while maintaining accuracy. Extensive experiments across diverse models and tasks demonstrate that Jakiro achieves significant speedups over strong baselines, with particularly notable improvements in non-greedy decoding scenarios where token diversity is crucial.