7 papers
BACH-V: Bridging Abstract and Concrete Human-Values in Large Language Models
Junyu Zhang, Yipeng Kang, Jiong Guo +2
Do large language models (LLMs) genuinely understand abstract concepts, or merely manipulate them as statistical patterns? We introduce an abstraction-grounding framework that deco…
When Reasoning Meets Its Laws
Junyu Zhang, Yifan Sun, Tianang Leng +4
Despite the superior performance of Large Reasoning Models (LRMs), their reasoning behaviors are often counterintuitive, leading to suboptimal reasoning capabilities. To theoretica…
Generalized Multi-agent Social Simulation Framework
Gang Li, Jie Lin, Yining Tang +6
Multi-agent social interaction has clearly benefited from Large Language Models. However, current simulation systems still face challenges such as difficulties in scaling to divers…
EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents
Rui Yang, Hanyang Chen, Junyu Zhang +10
Leveraging Multi-modal Large Language Models (MLLMs) to create embodied agents offers a promising avenue for tackling real-world tasks. While language-centric embodied agents have…
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Junyu Zhang, Runpei Dong, Han Wang +8
This paper presents AlphaOne (1), a universal framework for modulating reasoning progress in large reasoning models (LRMs) at test time. 1 first introduces moment, whi…
Task Matters: Investigating Human Questioning Behavior in Different Household Service for Learning by Asking Robots
Yuanda Hu, Hou Jiani, Zhang Junyu +3
Learning by Asking (LBA) enables robots to identify knowledge gaps during task execution and acquire the missing information by asking targeted questions. However, different tasks…