6 papers
LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent
Wanli Li, Bince Qu, Bo Pan +5
Reinforcement Learning (RL) has emerged as a powerful training paradigm for LLM-based agents. However, scaling agentic RL for deep research remains constrained by two coupled chall…
Vibe Calibration: Autonomous Bring-up of a 112-Qubit Superconducting Quantum Processor by a Skill-Orchestrating Language Agent
Huikai Xu, Jiaxiu Han, Shigang Ou +11
Superconducting quantum computing is one of the most mature solid-state platforms for quantum computation, with processors exceeding one hundred qubits. Yet further scaling toward…
Emergent Slow Thinking in LLMs as Inverse Tree Freezing
Sihan Hu, Xiansheng Cai, Yuan Huang +5
Reinforcement learning with verifiable rewards (RLVR) enables large language models to acquire slow, multi-step reasoning from sparse final-answer signals. We provide a statistical…
Inverse Knowledge Search over Verifiable Reasoning: Synthesizing a Scientific Encyclopedia from a Long Chains-of-Thought Knowledge Base
Yu Li, Yuan Huang, Tao Wang +19
Most scientific materials compress reasoning, presenting conclusions while omitting the derivational chains that justify them. This compression hinders verification by lacking expl…
Learning-at-Criticality in Large Language Models for Quantum Field Theory and Beyond
Xiansheng Cai, Sihan Hu, Tao Wang +4
Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles. While artificial intelligence (AI) offers promise, its typical n…
Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learning
Junqi He, Yujie Zhang, Jialu Wang +6
Two-dimensional (2D) materials and heterostructures exhibit unique physical properties, necessitating efficient and accurate characterization methods. Leveraging advancements in ar…