activity
20172026
most citedLearning Role-based Graph Embeddings

69 citations · 172 across the 44 of their papers we have counts for

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Showing 2026Show all

9 papers · 1 filter

cs.LG2026

FlexRouter: Learning Complementary Model Sets for Flexible LLM Routing

Wang Wei, Harry Yang, Tiankai Yang +6

Existing Large Language Model (LLM) routing methods score LLMs independently to select top- models. However, this ignores model correlations and enforces a rigid computational b…

cs.CV2026

Joint and Cross-Modal Video-Audio Generation and Editing: A Unified Formulation and Design Taxonomy

Abhinav Sharma, Sai Karthik Navuluru, Wang Wei +21

Video and audio are perceived together, yet most generative models treat them in isolation. We examine methods that model the two modalities jointly, generate one from the other, o…

cs.CL2026

Visual Search Augmented Chain-of-Thought Reasoning for Attribute Value Extraction from Product Videos

Tong Wu, Ming Cheng, Jiazhen Hu +2

Existing approaches to visual attribute value extraction (AVE) primarily rely on static product images, failing to capture temporal cues, multi-angle views and fine-grained visual…

cs.CL2026

Hierarchical Wasserstein Merging for Multi-Domain Multi-Task Learning: From Specialists to a Generalist

Ming Cheng, Jiaying Gong, Hoda Eldardiry

Multi-domain multi-task learning (MD-MTL) aims to build a single generalist model that performs well across heterogeneous domains and tasks. However, joint training often suffers f…

cs.AI2026

Beyond Top- Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents

Wang Wei, Tiankai Yang, Samyadeep Basu +7

Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many skills are functionally redu…

cs.LG2026

Online Learning with LLM Experts from Limited Feedback

Wang Wei, Soumyabrata Pal, Koyel Mukherjee +4

We study adaptive routing of prompts to large language model (LLM) experts to maximize response quality in an online setting with limited feedback. We formulate it as a bandit prob…