21 papers
Mapping the City Through the Lens of Language Models
Wanqi Liu, Rong Zhao, Zhizhou Sha +2
Language models often complete an underspecified reference to a city with unstated assumptions about urban size, form, infrastructure, environment, and function. We measure those a…
ISO: An RLVR-Native Optimization Stack
Hanqing Zhu, Wenyan Cong, Zhizhou Sha +8
Reinforcement learning with verifiable rewards (RLVR) is rapidly advancing the reasoning capabilities of language models, yet the optimization layer that converts reward feedback i…
Learning from the Self-future: On-policy Self-distillation for dLLMs
Yifu Luo, Zeyu Chen, Haoyu Wang +4
On-policy self-distillation (OPSD) has proven effective for post-training large language models (LLMs), yet its application to diffusion LLMs (dLLMs) remains unexplored. Existing O…
No Hidden Prompts Needed! You Can Game AI Peer Review with Presentation-Only Revisions
Xu Yang, Zhizhou Sha, Junbo Li +10
As AI-generated reviews move from experimental tools into peer-review infrastructure, most robustness concerns have focused on explicit attacks such as hidden instructions and prom…
Culturally uneven urban perception in large language models
Rong Zhao, Wanqi Liu, Zhizhou Sha +3
Large language models (LLMs) are increasingly used to describe and evaluate cities, yet the cultural structure of their urban judgments remains understudied. Here we introduce a me…
MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games
Yunfei Xie, Kevin Wang, Bobby Cheng +9
Multi-turn, multi-agent LLM game evaluations often exhibit substantial run-to-run variance. In long-horizon interactions, small early deviations compound across turns and are ampli…