Interactive world models must respond quickly to controls while preserving scene consistency. Existing acceleration methods can miss heterogeneous control responses and spatial transport when recovering skipped features. We observe that interaction-induced feature changes correlate with approximation error, while low-f...
Le-Yang Chen, Jun-Yi Wu, Fan-Qing Kong et al.· 0 citations
Feed-forward 3D vision models such as VGGT have achieved remarkable progress, unifying camera estimation and dense scene reconstruction in a single pass. However, their quadratic global attention makes long image sequences expensive, while existing sparse methods may favor highly attended yet value-redundant regions. T...
Jun-Yi Wu, Fan-Qing Kong, Le-Yang Chen et al.· 0 citations
JustQuant is proposed, a simple yet effective framework that moves the complexity of low-bit quantization from deployment-time operators into the training process and introduces Theseus QAD, a quantization-aware distillation method that progressively applies multi-level supervision, analogous to the gradual replacement...
Kai-Cheng Yang, Kai-Sen Yang, Chun-Yu Liu et al.· 0 citations
A causal interpretability framework is introduced that reveals that the tokens encoding semantics at the input need not be those that maintain them during generation, but also provides a causal view of internal mechanisms in diffusion transformers.
Visual token pruning reduces the inference cost of multimodal large language models, but a fixed token ratio is poorly matched to text-rich inputs. In OCR-centric tasks, decisive evidence can be a small number, label, or field whose relevance is specified by the question; indiscriminate pruning can erase that evidence...
Zizhong Ding, Junxian Li, Kai Liu et al.· 0 citations
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