1 citations · 1 across the 6 of their papers we have counts for
7 papers
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…
SLIDEFORGE: An LLM Agent for Controllable Editing of Slides as Structured Artifacts
Haozhen Zheng, Fulin Wang, Tianhu Xiong +6
Current AI agents compellingly describe slides. However, AI-assisted slide editing requires more than understanding: the output must retain layout, style, component structure, and…
SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning
Mingyuan Wu, Jingcheng Yang, Shengyi Qian +11
We introduce Self-Verified Reasoner (SVR-R1), a multi-turn RL framework that turns a model's own verification into a learning signal for multimodal reasoning. For each query, the m…
Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning
Banghao Chi, Yining Xie, Mingyuan Wu +9
Spreadsheet systems (e.g., Microsoft Excel, Google Sheets) play a central role in modern data-centric workflows. As AI agents grow increasingly capable of automating complex tasks,…
DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data
Yuhang Zhou, Jing Zhu, Shengyi Qian +7
Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF). Among RLHF methods, Group Relative Policy Op…
LinkGPT: Teaching Large Language Models To Predict Missing Links
Zhongmou He, Jing Zhu, Shengyi Qian +2
Large Language Models (LLMs) have shown promising results on various language and vision tasks. Recently, there has been growing interest in applying LLMs to graph-based tasks, par…
Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning
Jing Zhu, Yuhang Zhou, Shengyi Qian +4
Graph machine learning has made significant strides in recent years, yet the integration of visual information with graph structure and its potential for improving performance in d…