Long-form instructional videos require automatic chaptering for efficient organization and navigation. While long-context language models can predict chapters from textualized video representations, they become costly for content-dense videos with long transcripts, smooth topic transitions, and detailed chapter descriptions. A scalable segment-then-caption paradigm reduces this cost but suffers from boundary error propagation and fragmented cross-chapter context. We propose CausalChapter, a causal-inspired interventional dependency enhancement framework for long-video chaptering. Our method estimates the observable influence of semantic units and context segments through lightweight masking and removal interventions. For boundary localization, it detects dependency changes between adjacent temporal windows via masking-based feature reconstruction; for chapter description generation, it selects cross-chapter contexts according to their influence on generated descriptions. Experiments on long-video chaptering benchmarks show consistent improvements in boundary localization, description quality, and cross-chapter coherence.

Despite exceptional performance on static benchmarks, Large Language Models (LLMs) exhibit significant brittleness when evaluated on perturbed variants of the original questions. Current extrinsic interventions, prompt engineering or supervised fine-tuning, overlook an important intrinsic factor underlying this degradation: the distributional shifts in layer activations between original and perturbed questions. To address this, this work formulates the reasoning robustness challenge as an Unsupervised Domain Adaptation (UDA) problem and proposes the Elastic Domain Adaptation (EDA) method. Unlike rigid strategies that enforce a uniform alignment constraint, EDA dynamically aligns layer activations based on the specific layers and perturbation types. Specifically, EDA incorporates an Adaptive Layer Selection (ALS) to identify pivotal layers and employs an Elastic Shift Module (ESM) to selectively pull or push layer representations depending on the perturbation types. Extensive experiments across six LLMs and 15 perturbation types demonstrate that EDA achieves state-of-the-art performance on most perturbed benchmarks while preserving reasoning capability on original benchmarks.