1 citations · 1 across the 14 of their papers we have counts for
15 papers
What Makes an Efficient VLA? Navigating Action-Head Design, Scaling, and Latency
Luoyang Sun, Guoyang Xia, Fengfa Li +9
Vision-Language-Action (VLA) models combine a pretrained vision encoder, a language backbone, and an action head, but their relative contribution has not been established under con…
VANE: Reliable Test-Time Training for Vision-Language-Action Models via Future Visual Representation Prediction
Hongjin Ji, Guoyang Xia, Luoyang Sun +2
Test-time training (TTT) offers a lightweight way to adapt vision--language--action (VLA) policies from unlabeled deployment streams, but it remains difficult to use reliably in cl…
VLAFlow: A Unified Training Framework for Vision-Language-Action Models via Co-training and Future Latent Alignment
Guoyang Xia, Fengfa Li, Hongjin Ji +4
Vision-language-action models (VLAs) have recently advanced robotic manipulation, yet the effects of different robot-data pre-training paradigms remain difficult to compare because…
Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling
Fengfa Li, Hongjin Ji, Yifeng Ding +2
The Mixture of Experts MoE architecture is highly promising for resource constrained on device deployments yet training these models from scratch incurs prohibitive costs Current m…
Lngram: N-gram Conditional Memory in Latent Space
Yunao Zheng, Guoyang Xia, Xiaojie Wang +1
Sequence modeling requires both compositional reasoning and local static knowledge retrieval, yet standard Transformers handle both through dense computation. Engram partially deco…
Hardware Co-Design Scaling Laws via Roofline Modelling for On-Device LLMs
Luoyang Sun, Jiwen Jiang, Yifeng Ding +9
Vision-Language-Action Models (VLAs) have emerged as a key paradigm of Physical AI and are increasingly deployed in autonomous vehicles, robots, and smart spaces. In these resource…
ROSA-Tuning: Enhancing Long-Context Modeling via Suffix Matching
Yunao Zheng, Xiaojie Wang, Lei Ren +1
Long-context capability and computational efficiency are among the central challenges facing today's large language models. Existing efficient attention methods reduce computationa…
Diagnose, Correct, and Learn from Manipulation Failures via Visual Symbols
Xianchao Zeng, Xinyu Zhou, Youcheng Li +5
Vision-Language-Action (VLA) models have recently achieved remarkable progress in robotic manipulation, yet they remain limited in failure diagnosis and learning from failures. Add…
FastMMoE: Accelerating Multimodal Large Language Models through Dynamic Expert Activation and Routing-Aware Token Pruning
Guoyang Xia, Yifeng Ding, Fengfa Li +4
Multimodal large language models (MLLMs) have achieved impressive performance, but high-resolution visual inputs result in long sequences of visual tokens and substantial inference…
ChunkLLM: A Lightweight Pluggable Framework for Accelerating LLMs Inference
Haojie Ouyang, Jianwei Lv, Lei Ren +3
Transformer-based large models excel in natural language processing and computer vision, but face severe computational inefficiencies due to the self-attention's quadratic complexi…
SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs
Yanxiao Zhao, Yaqian Li, Zihao Bo +6
Large language models (LLMs) exhibit strong general reasoning, yet the community lacks controllable, scalable, and verifiable tools to analyze and improve these abilities. We prese…
SMAR: Soft Modality-Aware Routing Strategy for MoE-based Multimodal Large Language Models Preserving Language Capabilities
Guoyang Xia, Yifeng Ding, Fengfa Li +4
Mixture of Experts (MoE) architectures have become a key approach for scaling large language models, with growing interest in extending them to multimodal tasks. Existing methods t…
Evaluating MLLMs with Multimodal Multi-image Reasoning Benchmark
Ziming Cheng, Binrui Xu, Lisheng Gong +14
With enhanced capabilities and widespread applications, Multimodal Large Language Models (MLLMs) are increasingly required to process and reason over multiple images simultaneously…
FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image Generation
Zebin Yao, Lei Ren, Huixing Jiang +4
Subject-driven image generation aims to synthesize novel scenes that faithfully preserve subject identity from reference images while adhering to textual guidance. However, existin…
Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents
Haochen Sun, Shuwen Zhang, Lujie Niu +6
Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks. This paper proposes a new LLM-based Multi-Agent Sy…