1 citations · 1 across the 11 of their papers we have counts for
13 papers
AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration
Jiaqi Liu, Shi Qiu, Mairui Li +33
Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail…
EvolveMem:Self-Evolving Memory Architecture via AutoResearch for LLM Agents
Jiaqi Liu, Xinyu Ye, Peng Xia +4
Long-term memory is essential for LLM agents that operate across multiple sessions, yet existing memory systems treat retrieval infrastructure as fixed: stored content evolves whil…
On Safety Risks in Experience-Driven Self-Evolving Agents
Weixiang Zhao, Yichen Zhang, Yingshuo Wang +8
Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduc…
Omni-SimpleMem: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory
Jiaqi Liu, Zipeng Ling, Shi Qiu +9
AI agents increasingly operate over extended time horizons, yet their ability to retain, organize, and recall multimodal experiences remains a critical bottleneck. Building effecti…
When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning
Shoubin Yu, Yue Zhang, Zun Wang +4
Despite rapid progress in MLLMs, visual spatial reasoning remains unreliable when correct answers depend on how a scene would appear under unseen or alternative viewpoints. Recent…
SimpleMem: Efficient Lifelong Memory for LLM Agents
Jiaqi Liu, Yaofeng Su, Peng Xia +5
To support long-term interaction in complex environments, LLM agents require memory systems that manage historical experiences. Existing approaches either retain full interaction h…
Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning
Jiaqi Liu, Kaiwen Xiong, Peng Xia +6
Vision-language agents have achieved remarkable progress in a variety of multimodal reasoning tasks; however, their learning remains constrained by the limitations of human-annotat…
Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails
Siwei Han, Kaiwen Xiong, Jiaqi Liu +9
As Large Language Model (LLM) agents increasingly gain self-evolutionary capabilities to adapt and refine their strategies through real-world interaction, their long-term reliabili…
REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation
Puzhen Yuan, Angyuan Ma, Yunchao Yao +3
Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the enviro…
MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation
Haibo Tong, Zhaoyang Wang, Zhaorun Chen +11
Recent advancements in video generation have significantly improved the ability to synthesize videos from text instructions. However, existing models still struggle with key challe…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
GRAPE: Generalizing Robot Policy via Preference Alignment
Zijian Zhang, Kaiyuan Zheng, Zhaorun Chen +7
Despite the recent advancements of vision-language-action (VLA) models on a variety of robotics tasks, they suffer from critical issues such as poor generalizability to unseen task…
WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Driving
Yiheng Li, Cunxin Fan, Chongjian Ge +9
Language models uncover unprecedented abilities in analyzing driving scenarios, owing to their limitless knowledge accumulated from text-based pre-training. Naturally, they should…