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20242026
most citedFSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models

12 citations · 15 across the 18 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

SpatialGrammar: A Domain-Specific Language for LLM-Based 3D Indoor Scene Generation

Song Tang, Kaiyong Zhao, Yuliang Li +5

Automatically generating interactive 3D indoor scenes from natural language is crucial for virtual reality, gaming, and embodied AI. However, existing LLM-based approaches often su…

cs.AI2026

Are Dilemmas and Conflicts in LLM Alignment Solvable? A View from Priority Graph

Zhenheng Tang, Xiang Liu, Qian Wang +3

As Large Language Models (LLMs) become more powerful and autonomous, they increasingly face conflicts and dilemmas in many scenarios. We first summarize and taxonomize these divers…

cs.AI2025

Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research

Xiang Liu, Penglei Sun, Shuyan Chen +7

The rapid advancement of perovskite solar cells (PSCs) has led to an exponential growth in research publications, creating an urgent need for efficient knowledge management and rea…

cs.AI2024

Should We Really Edit Language Models? On the Evaluation of Edited Language Models

Qi Li, Xiang Liu, Zhenheng Tang +4

Model editing has become an increasingly popular alternative for efficiently updating knowledge within language models. Current methods mainly focus on reliability, generalization,…

cs.AI2024

ExpertFlow: Efficient Mixture-of-Experts Inference via Predictive Expert Caching and Token Scheduling

Xin He, Shunkang Zhang, Kaijie Tang +8

Sparse Mixture-of-Experts (MoE) models can outperform dense large language models at similar computation by activating only a small set of experts per token. However, stacking many…