Negative interference from cross-lingual conflicting syntactic patterns is a primary obstacle in Multilingual Neural Machine Translation (MNMT). We trace this problem to the entanglement of transferable, universal semantics with non-transferable, language-specific syntactic structures. Existing methods, relying on disjoint training-only specialization or inference-only filtering, fail to fully resolve this fundamental entanglement. To address this, we propose NRD (Neuron Representation Disentanglement), a two-stage hybrid framework that couples training-time specialization with inference-time filtering. First, a Specialization Fine-tuning stage identifies functional neurons via a semantic-invariant activation-variance metric and reinforces intrinsic modularity through sparse updates. Second, a Dynamic Representation Filtering stage purifies semantic representations at inference by adaptively suppressing syntax-sensitive neurons, guided by each language’s pre-computed gradient consistency. On the OPUS-100 benchmark, NRD outperforms strong baselines, achieving an average gain of +1.9 BLEU on supervised directions. On the WMT-10 zero-shot benchmark, it obtains a substantial +7.1 BLEU, demonstrating robust cross-lingual generalization. These results provide strong evidence that our hybrid approach effectively purifies semantic representations by mitigating syntactic interference, paving the way for more robust cross-lingual generalization.

Computational narrative analysis aims to capture rhythm, tension, and emotional dynamics in literary texts. Existing large language models can generate long stories but overly focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives. This suggests a structural misalignment between model- and human-generated narratives.We therefore position narrative analysis as a diagnostic proxy for generation and propose VISTA Space, a high-dimensional framework for narrative orchestration that unifies human and model perspectives while jointly characterizing narrative function and structure in a common space.We further introduce LitVISTA, a structurally annotated benchmark grounded in literary texts, which operationalizes VISTA Space for systematic evaluation of models’ narrative orchestration capabilities. Under an oracle setting with gold event anchors, we evaluate frontier LLMs including GPT, Claude, Grok, and Gemini. Results reveal systematic deficiencies, as current models struggle to jointly capture narrative function and structure and fail to form an integrated global view of literary narrative orchestration. End-to-end analysis further shows that failures are dominated by anchor identification and localization errors. Even advanced thinking modes yield mixed and often limited gains for literary narrative understanding.