1 citations · 2 across the 17 of their papers we have counts for
19 papers
WCM: A World Critic Model for Vision-Language-Action Reinforcement Learning
Senyu Fei, Xiaopeng Yu, Siyin Wang +3
Reinforcement learning (RL) post-training of Vision-Language-Action (VLA) models has shown strong promise for robotic manipulation. Among RL methods, critic-based approaches rely o…
HiMe: Hierarchical Embodied Memory for Long-Horizon Vision-Language-Action Control
Li Ji, Siyin Wang, Pengfang Qian +5
Current Vision-Language-Action (VLA) models excel at robotic manipulation but often struggle with non-Markovian tasks requiring long-term memory and reasoning due to their reliance…
Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs
Junhao Shi, Siyin Wang, Xiaopeng Yu +3
Vision-Language-Action (VLA) models are fundamentally bottlenecked by the scarcity of expert demonstrations -- triplets of observations, instructions, and actions that are costly t…
MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models
Yitian Gong, Kuangwei Chen, Zhaoye Fei +9
Discrete audio tokenizers are fundamental to empowering large language models with native audio processing and generation capabilities. Despite recent progress, existing approaches…
MOVA: Towards Scalable and Synchronized Video-Audio Generation
OpenMOSS Team, Donghua Yu, Mingshu Chen +38
Audio is indispensable for real-world video, yet generation models have largely overlooked audio components. Current approaches to producing audio-visual content often rely on casc…
WESR: Scaling and Evaluating Word-level Event-Speech Recognition
Chenchen Yang, Kexin Huang, Liwei Fan +8
Speech conveys not only linguistic information but also rich non-verbal vocal events such as laughing and crying. While semantic transcription is well-studied, the precise localiza…
MOSS Transcribe Diarize Technical Report
MOSI. AI, :, Donghua Yu +23
Speaker-Attributed, Time-Stamped Transcription (SATS) aims to transcribe what is said and to precisely determine the timing of each speaker, which is particularly valuable for meet…
FASTer: Toward Efficient Autoregressive Vision Language Action Modeling via Neural Action Tokenization
Yicheng Liu, Shiduo Zhang, Zibin Dong +12
Autoregressive vision-language-action (VLA) models have recently demonstrated strong capabilities in robotic manipulation. However, their core process of action tokenization often…
SRPO: Self-Referential Policy Optimization for Vision-Language-Action Models
Senyu Fei, Siyin Wang, Li Ji +7
Vision-Language-Action (VLA) models excel in robotic manipulation but are constrained by their heavy reliance on expert demonstrations, leading to demonstration bias and limiting p…
RoboOmni: Proactive Robot Manipulation in Omni-modal Context
Siyin Wang, Jinlan Fu, Feihong Liu +11
Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision-Language-Action (VLA) models for robotic manipulation. Although effective in many s…
LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
Senyu Fei, Siyin Wang, Junhao Shi +10
Visual-Language-Action (VLA) models report impressive success rates on robotic manipulation benchmarks, yet these results may mask fundamental weaknesses in robustness. We perform…
XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs
Yitian Gong, Luozhijie Jin, Ruifan Deng +6
Speech codecs serve as bridges between speech signals and large language models. An ideal codec for speech language models should not only preserve acoustic information but also ca…
World-aware Planning Narratives Enhance Large Vision-Language Model Planner
Junhao Shi, Zhaoye Fei, Siyin Wang +3
Large Vision-Language Models (LVLMs) show promise for embodied planning tasks but struggle with complex scenarios involving unfamiliar environments and multi-step goals. Current ap…
Unleashing Embodied Task Planning Ability in LLMs via Reinforcement Learning
Zhaoye Fei, Li Ji, Siyin Wang +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, yet they face significant challenges in embodied task planning scenarios that require c…
InstructTTSEval: Benchmarking Complex Natural-Language Instruction Following in Text-to-Speech Systems
Kexin Huang, Qian Tu, Liwei Fan +6
In modern speech synthesis, paralinguistic information--such as a speaker's vocal timbre, emotional state, and dynamic prosody--plays a critical role in conveying nuance beyond mer…
VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search
Yikun Wang, Siyin Wang, Qinyuan Cheng +5
Recent advancements in Large Vision-Language Models have showcased remarkable capabilities. However, they often falter when confronted with complex reasoning tasks that humans typi…
World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning
Siyin Wang, Zhaoye Fei, Qinyuan Cheng +4
Recent advances in large vision-language models (LVLMs) have shown promise for embodied task planning, yet they struggle with fundamental challenges like dependency constraints and…
How to Mitigate Overfitting in Weak-to-strong Generalization?
Junhao Shi, Qinyuan Cheng, Zhaoye Fei +3
Aligning powerful AI models on tasks that surpass human evaluation capabilities is the central problem of \textbf{superalignment}. To address this problem, weak-to-strong generaliz…
CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs
Jinlan Fu, Shenzhen Huangfu, Hao Fei +4
Multimodal Large Language Models (MLLMs) still struggle with hallucinations despite their impressive capabilities. Recent studies have attempted to mitigate this by applying Direct…