1 citations · 1 across the 7 of their papers we have counts for
9 papers
INTCORT: Training-Free Spatial Reasoning Enhancement for Vision-Language Models via Input Transformations and Confidence Routing
Haoran Sun, Jingqi Xu, Yanhui Li +3
Vision-Language Models (VLMs) have demonstrated remarkable capabilities in multimodal tasks, yet they still exhibit poor ability in spatial reasoning. Existing training-dependent a…
Thousand-GPU Large-Scale Training and Optimization Recipe for AI-Native Cloud Embodied Intelligence Infrastructure
Yongjian Guo, Yunxuan Ma, Haoran Sun +22
Embodied intelligence is a key step towards Artificial General Intelligence (AGI), yet its development faces multiple challenges including data, frameworks, infrastructure, and eva…
SD-PSFNet: Sequential and Dynamic Point Spread Function Network for Image Deraining
Jiayu Wang, Haoyu Bian, Haoran Sun +1
Image deraining is crucial for vision applications but is challenged by the complex multi-scale physics of rain and its coupling with scenes. To address this challenge, a novel app…
Preference-Aware Memory Update for Long-Term LLM Agents
Haoran Sun, Zekun Zhang, Shaoning Zeng
One of the key factors influencing the reasoning capabilities of LLM-based agents is their ability to leverage long-term memory. Integrating long-term memory mechanisms allows agen…
Hierarchical Memory for High-Efficiency Long-Term Reasoning in LLM Agents
Haoran Sun, Shaoning Zeng
Long-term memory is one of the key factors influencing the reasoning capabilities of Large Language Model Agents (LLM Agents). Incorporating a memory mechanism that effectively int…
An Uncertainty-Driven Adaptive Self-Alignment Framework for Large Language Models
Haoran Sun, Zekun Zhang, Shaoning Zeng
Large Language Models (LLMs) have demonstrated remarkable progress in instruction following and general-purpose reasoning. However, achieving high-quality alignment with human inte…
A Novel Self-Evolution Framework for Large Language Models
Haoran Sun, Zekun Zhang, Shaoning Zeng
The capabilities of Large Language Models (LLMs) are limited to some extent by pre-training, so some researchers optimize LLMs through post-training. Existing post-training strateg…
Introspection of Thought Helps AI Agents
Haoran Sun, Shaoning Zeng
AI Agents rely on Large Language Models (LLMs) and Multimodal-LLMs (MLLMs) to perform interpretation and inference in text and image tasks without post-training, where LLMs and MLL…
DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images
Haoran Sun, Haoyu Bian, Shaoning Zeng +4
Common knowledge indicates that the process of constructing image datasets usually depends on the time-intensive and inefficient method of manual collection and annotation. Large m…