5 papers
Graph-Based Chain-of-Thought Pruning for Reducing Redundant Reflections in Reasoning LLMs
Hongyuan Yuan, Xinran He, Run Shao +6
Extending CoT through RL has been widely used to enhance the reasoning capabilities of LLMs. However, due to the sparsity of reward signals, it can also induce undesirable thinking…
Asking like Socrates: Socrates helps VLMs understand remote sensing images
Run Shao, Ziyu Li, Zhaoyang Zhang +9
Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (RS) tasks, we observe widespread pseud…
RS-WorldModel: a Unified Model for Remote Sensing Understanding and Future Sense Forecasting
Linrui Xu, Zhongan Wang, Fei Shen +4
Remote sensing world models aim to both explain observed changes and forecast plausible futures, two tasks that share spatiotemporal priors. Existing methods, however, typically ad…
Learning on the Job: An Experience-Driven Self-Evolving Agent for Long-Horizon Tasks
Cheng Yang, Xuemeng Yang, Licheng Wen +9
Large Language Models have demonstrated remarkable capabilities across diverse domains, yet significant challenges persist when deploying them as AI agents for real-world long-hori…
Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering
Bolei He, Xinran He, Run Shao +5
Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios. Retrieval-Augmented Generation (RAG) introduces external knowledge but suff…