2 citations · 2 across the 11 of their papers we have counts for
14 papers
Frechet Forcing: Training Long-Horizon Streaming World Models from Short Clips
Yian Zhao, Yu Yan, Hongcan Guo +3
Streaming video world models generate the future chunk by chunk, conditioned on their own past and a stream of actions. Trained on short clips, they drift once a rollout exceeds th…
MiniWorld: Democratizing the Training of Video World Models from Scratch
Yian Zhao, Ruochong Zheng, Hongcan Guo +3
Video world models predict future observations conditioned on historical observations and control signals, enabling long-horizon generation through autoregressive state transitions…
DeskCraft: Benchmarking Desktop Agents on Professional Workflows and Human-in-the-Loop Collaboration
Wenkai Wang, Tao Xiong, Jingchen Ni +6
Real-world professional desktop workflows in specialized creative and engineering software unfold over long horizons and often require human-in-the-loop coordination, where agents…
SimSD: Simple Speculative Decoding in Diffusion Language Models
Junxia Cui, Haotian Ye, Runchu Tian +9
Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs, offering faster inference through parallel or blockwise decodi…
Continuous Latent Diffusion Language Model
Hongcan Guo, Qinyu Zhao, Yian Zhao +8
Large language models have achieved remarkable success under the autoregressive paradigm, yet high-quality text generation need not be tied to a fixed left-to-right order. Existing…
Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding
Wenkai Wang, Xiyun Li, Hongcan Guo +5
Graphical User Interface (GUI) grounding requires mapping natural language instructions to precise pixel coordinates. However, due to visually homogeneous elements and dense layout…
Disentangling Deception and Hallucination Failures in LLMs
Haolang Lu, Hongrui Peng, WeiYe Fu +5
Failures in large language models (LLMs) are often analyzed from a behavioral perspective, where incorrect outputs in factual question answering are commonly associated with missin…
Anchored Policy Optimization: Mitigating Exploration Collapse Via Support-Constrained Rectification
Tianyi Wang, Long Li, Hongcan Guo +5
Reinforcement Learning with Verifiable Rewards (RLVR) is increasingly viewed as a tree pruning mechanism. However, we identify a systemic pathology termed Recursive Space Contracti…
VideoMiner: Iteratively Grounding Key Frames of Hour-Long Videos via Tree-based Group Relative Policy Optimization
Xinye Cao, Hongcan Guo, Jiawen Qian +6
Understanding hour-long videos with multi-modal large language models (MM-LLMs) enriches the landscape of human-centered AI applications. However, for end-to-end video understandin…
A Rolling Stone Gathers No Moss: Adaptive Policy Optimization for Stable Self-Evaluation in Large Multimodal Models
Wenkai Wang, Hongcan Guo, Zheqi Lv +1
Self-evaluation, a model's ability to assess the correctness of its own output, is crucial for Large Multimodal Models (LMMs) to achieve self-improvement in multi-turn conversation…
Advancing Compositional LLM Reasoning with Structured Task Relations in Interactive Multimodal Communications
Xinye Cao, Hongcan Guo, Guoshun Nan +9
Interactive multimodal applications (IMAs), such as route planning in the Internet of Vehicles, enrich users' personalized experiences by integrating various forms of data over wir…
Two Is Better Than One: Rotations Scale LoRAs
Hongcan Guo, Guoshun Nan, Yuan Yang +9
Scaling Low-Rank Adaptation (LoRA)-based Mixture-of-Experts (MoE) facilitates large language models (LLMs) to efficiently adapt to diverse tasks. However, traditional gating mechan…
Advancing Expert Specialization for Better MoE
Hongcan Guo, Haolang Lu, Guoshun Nan +8
Mixture-of-Experts (MoE) models enable efficient scaling of large language models (LLMs) by activating only a subset of experts per input. However, we observe that the commonly use…
Measuring algorithmic interpretability: A human-learning-based framework and the corresponding cognitive complexity score
John P. Lalor, Hong Guo
Algorithmic interpretability is necessary to build trust, ensure fairness, and track accountability. However, there is no existing formal measurement method for algorithmic interpr…