works on

From the 1 of 15 linked papers with an AI index.

activity
20242026
collaborators

15 papers

cs.AI2026

SurgLAT: Surgical Latent Attention Tracking for Depth-Aware Robotic Laparoscope Control

Rulin Zhou, Qiujie Song, Yujie Ma +10

Autonomous laparoscopic camera control requires continuous understanding of the surgeon's operative intent in dynamic surgical scenes, where the target operative region is not a st…

cs.CL2026

Does More Retrieved Evidence Help Visual Retrieval-Augmented Generation with Diffusion Language Models?

Jiankun Wang, Yisen Gao, Ziwei Zhang +3

Visual retrieval-augmented generation (RAG) commonly expands the retrieved evidence set to improve answer-page coverage, implicitly assuming that all available evidence should be p…

cs.RO2026

EmbodiedVAE: Disentangled Video VAE for Efficient and Controllable Embodied Manipulation

Jiayi Luo, Hanxin Zhu, Chen Gao +5

Latent diffusion models (LDMs) have recently significantly advanced embodied learning in constructing powerful embodied manipulation world models. However, despite the remarkable p…

cs.LG2026

Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning

Haobo Zhang, Jiankun Wang, Suraj Rajendran +5

The paper introduces Dysco, a plug‑in technique for federated fine‑tuning of large models that dynamically allocates client‑specific LoRA subspaces to reduce interference caused by…

cs.CL2026

Structural Rationale Distillation via Reasoning Space Compression

Jialin Yang, Jiankun Wang, Jiajun Wu +3

When distilling reasoning from large language models (LLMs) into smaller ones, teacher rationales for similar problems often vary wildly in structure and strategy. Like a chef who…

cs.CV2026

Attention Sparsity is Input-Stable: Training-Free Sparse Attention for Video Generation via Offline Sparsity Profiling and Online QK Co-Clustering

Jiayi Luo, Jiayu Chen, Jiankun Wang +6

Diffusion Transformers (DiTs) achieve strong video generation quality but suffer from high inference cost due to dense 3D attention, motivating sparse attention techniques for impr…