6 papers
What Should Robot Policies Learn to Predict? Rethinking Supervision Targets in Vision-Language-Action Learning
Haitao Shen, Hanyu Liu, Yihan Lin +7
Vision-language-action (VLA) policies are typically trained with action prediction as the primary supervision signal, since robots ultimately execute actions. Auxiliary objectives…
SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models
Cheng Yin, Wang Xu, Junpeng Yang +8
Long-horizon manipulation is partially observable: the information needed to choose the next action may appear only in observations from minutes earlier. Existing memory mechanisms…
Efficient Frame Selection for Long Videos at Test Time with Attention-Based MLLM Selectors
Yilin Wang, Xiangxi Zheng, Dongxing Mao +6
Understanding long videos with multimodal large language models (MLLMs) requires selecting a compact set of frames from thousands of candidates, yet identifying the right frames se…
Liberating LLM Capabilities in Full-Duplex Speech Models
Luoyuan Zhang, Bokai Xu, Junbo Cui +4
Speech-based large language models are typically constrained to spoken replies, which limits their user-facing outputs to what can be verbalized and suppresses text-native capabili…
CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution
Xiangxi Zheng, Kuang He, Jiayi Hu +6
Chart-to-code generation demands strict visual precision and syntactic correctness from Vision-Language Models (VLMs). However, existing approaches are fundamentally constrained by…
V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models
Xiangxi Zheng, Linjie Li, Zhengyuan Yang +5
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in visual-text processing. However, existing static image-text benchmarks…