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Xiarui Zhan

Publications and source records attributed to Xiarui Zhan.

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Metal-Insulator Coexistence and Gap-Crossing Domain-Wall Modes in an Aubry-Andr\'{e} Model with Nonlocal Hopping

Nonequilibrium transport remains a central theme in modern physics, spanning from condensed matter to synthetic systems. Here, we investigate particle transport in an extended Aubry-Andr\'e model with system-scale hopping, namely nonlocal hopping with a range proportional to the system size, and uncover a metal-insulator coexistence regime in real space, where metallic and insulating spatial domains coexist within the same system and are separated by sharp spatial boundaries. In the insulating region, particles exhibit flat-band-like localization in the absence of quasiperiodic potentials, while a quasiperiodic potential induces distinct multi-point localization, different from conventional exponential localization. Meanwhile, particles can freely propagate and tunnel across spatially disconnected metallic domains separated by the insulating region. Beyond this coexistence phase, we identify unconventional gap-crossing domain-wall modes with comb-like spatial profiles that mediate nonlocal, multi-point transport across separated metallic domains. Our findings reveal a rich interplay between localization, nonlocality, and transport in systems with nonlocal hopping.

cond-mat.mes-hall

PRBench: End-to-end Paper Reproduction in Physics Research

AI agents powered by large language models exhibit strong reasoning and problem-solving capabilities, enabling them to assist scientific research tasks such as formula derivation and code generation. However, whether these agents can reliably perform end-to-end reproduction from real scientific papers remains an open question. We introduce PRBench, a benchmark of 30 expert-curated tasks spanning 11 subfields of physics. Each task requires an agent to comprehend the methodology of a published paper, implement the corresponding algorithms from scratch, and produce quantitative results matching the original publication. Agents are provided only with the task instruction and paper content, and operate in a sandboxed execution environment. All tasks are contributed by domain experts from over 20 research groups at the School of Physics, Peking University, each grounded in a real published paper and validated through end-to-end reproduction with verified ground-truth results and detailed scoring rubrics. Using an agentified assessment pipeline, we evaluate a set of coding agents on PRBench and analyze their capabilities across key dimensions of scientific reasoning and execution. The best-performing agent, OpenAI Codex powered by GPT-5.3-Codex, achieves a mean overall score of 34%. All agents exhibit a zero end-to-end callback success rate, with particularly poor performance in data accuracy and code correctness. We further identify systematic failure modes, including errors in formula implementation, inability to debug numerical simulations, and fabrication of output data. Overall, PRBench provides a rigorous benchmark for evaluating progress toward autonomous scientific research.

cs.CL