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Ran Yi

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#artificial intelligence Preprint Sep 2026

DIET: Deletion-response Expert Trimming for Video Diffusion Transformers

Video diffusion transformers (DiTs) increasingly adopt mixture-of-experts (MoE) architectures to reduce active computation, but their full expert storage remains costly. Existing one-shot pruning criteria mainly rely on static activation or routing statistics and cannot capture layer-level re-routing after expert delet...

Jia-Chang Zhang, Teng Hu, Bo-Hao Feng et al. · 0 citations
Preprint Aug 2026

TRACE-Bench: Decomposing and Diagnosing Multi-Reference Image Generation

Recognizing that diverse multi-reference tasks share a common set of atomic operations, this work formalizes four operators: Anchor, Disentangle, Disentangle, Apply, and Compose, and constructs TRACE-Bench, comprising approximately 1,600 evaluation cases across slot counts 1--8.

Haoran Wang, Chaofan Ma, Ran Yi et al. · 0 citations
Preprint Aug 2026

Efficient Audio-Visual Generation via Synchrony-Aware Cross-Modal Sparse Attention

This work presents a synchronization-aware acceleration framework for efficient audio-visual generation by explicitly accounting for cross-modal dependence during acceleration, and improves inference efficiency while keeping video quality, audio quality, and audio-video synchronization.

Sheng-Chuan Gao, Teng Hu, Bo-Hao Feng et al. · 1 citation
Jul 2026

PhysAgent: Reflective Agentic Physics Control for Physically Plausible Video Generation

This paper proposes PhysAgent, a reflective agentic framework that closes the loop among physical program generation, physics simulation, stage-specific verification, and targeted program repair, and design a set of physics-control APIs to support more stable and complex motion behaviors.

Qirui Li, Jinkun Hao, Yibo Li et al. · 1 citation
Preprint Jul 2026

Cycle-World: Mitigating Error Accumulation in Long-term Video World Models via Reverse-Prediction Cycle Consistency

This work proposes Cycle-World, a novel framework designed for stable and temporally consistent long-video generation that tackles error drift by enforcing strict temporal reversibility across both the training and inference phases, and demonstrates that forward generative drift can be strictly bottlenecked by a cycle-...

Zihan Su, Teng Hu, Jiangning Zhang et al. · 1 citation

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