5 papers
IndustryNav: Exploring Spatial Reasoning of Embodied Agents in Dynamic Industrial Navigation
Yifan Li, Lichi Li, Anh Dao +15
While Visual Large Language Models (VLLMs) show great promise as embodied agents, they continue to face substantial challenges in spatial reasoning. Existing embodied benchmarks la…
CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters
Ao Sun, Xiaoyu Wang, Zhe Tan +4
As Large Language Models (LLMs) serve a global audience, alignment must transition from enforcing universal consensus to respecting cultural pluralism. We demonstrate that dense mo…
QianfanHuijin Technical Report: A Novel Multi-Stage Training Paradigm for Finance Industrial LLMs
Shupeng Li, Weipeng Lu, Linyun Liu +16
Domain-specific enhancement of Large Language Models (LLMs) within the financial context has long been a focal point of industrial application. While previous models such as Bloomb…
SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents
Dawei Li, Zhen Tan, Peijia Qian +4
While multi-agent systems have been shown to significantly enhance the performance of Large Language Models (LLMs) across various tasks and applications, the dense interaction betw…
Understanding Reasoning in Chain-of-Thought from the Hopfieldian View
Lijie Hu, Liang Liu, Shu Yang +5
Large Language Models have demonstrated remarkable abilities across various tasks, with Chain-of-Thought (CoT) prompting emerging as a key technique to enhance reasoning capabiliti…