InteractEval
Define interactive world modeling and human-centered AI evaluation.
Worldeology Lab / Faculty
Co-Founder & Chief Cognitive Scientist of AlpsLab · Founder of Worldeology
Xuanming Zhang is Co-Founder and Chief Cognitive Scientist of AlpsLab. His research program, Cognizer, explores how AI can move beyond closed-ended pattern matching and distribution alignment through a test-time cognition flywheel that infers latent states, organizes chains of logic, recursively examines and revises its reasoning, models long-horizon consequences, transfers learned patterns across domains, and creates structures that do not yet exist.
His work has appeared in NeurIPS, EMNLP, ACL, ICLR, and PNAS, including two Oral/Spotlight awards. He earned his bachelor’s degree from the University of Wisconsin–Madison and was a joint undergraduate researcher at Stanford University. His prior research spans Qwen (Subjectivity & Interactivity Team), Anthropic (Interpretability Team), Amazon (AGI Team), ByteDance (Coze Team), and Zhipu AI (Emohaa Team).
His long-term ambition is to build AI with cognition, agency, and enduring world models—systems capable not only of reasoning, but of sustained innovation and organization in the real world. Outside AI research, he is a professional psychological counselor and has worked with more than 50 clients.
01
Define interactive world modeling and human-centered AI evaluation.
Accepted to EMNLP 2026 in the top 0.45%; released with an official QwenLM implementation.
Accepted to NeurIPS 2026, extending metacognitive reasoning into an auditable rollback-and-refine process.
Work on deceptive interaction and covertly misaligned advice appeared at ICLR and in PNAS.
Received a NeurIPS Spotlight for modeling human social thought with metacognitive multi-agent systems.
02
Cognition is not a single answer. It is a trajectory that begins with another mind, passes through a revisable act of reasoning, and continues into the world that action changes.
Xuanming’s work connects model mechanisms, social cognition, and interactive world modeling. The goal is to make latent cognitive states observable, reasoning processes correctable, and downstream consequences measurable—then use those signals to train more capable and trustworthy systems.
Inferring beliefs, intentions, preferences, emotions, and awareness before a system acts.
Making reasoning inspectable, revisable, and able to improve its own cognitive process.
Evaluating how actions alter people, relationships, and long-horizon trajectories.
Moving AI from closed-ended response optimization toward innovation, organization, and creation.
03
Xuanming Zhang, Sining Zhoubian, Yuxuan Chen, Tianyi Tang, An Yang, et al.
Xuanming Zhang, Yuxuan Chen, Samuel Yeh
Yang Xu*, Xuanming Zhang*, Samuel Yeh, Jwala Dhamala, Ousmane Dia, Rahul Gupta
Sahand Sabour, June M. Liu, Siyang Liu, Chris Z. Yao, Shiyao Cui, Xuanming Zhang, et al.
Xuanming Zhang, Yuxuan Chen, Samuel Yeh
Jinfeng Zhou, Yuxuan Chen, Yihan Shi, Xuanming Zhang, et al.
04
NeurIPS Top Reviewer 2025; program committee service for ICLR, ACL, OOPSLA, AAAI, TMLR, and IEEE TII. Invited talks include WAIC, Fudan University, and ByteDance.