arXiv subjects

Zhaoming Xu

Publications and source records attributed to Zhaoming Xu.

3 recordsLinked to original sources

arXiv2026

ElectrolyteMD-Bench: How Well Do AI Agents Conduct Molecular Dynamics Studies across Electrolyte Solvation Regimes?

AI agents are advancing the automation of molecular dynamics (MD) research, with the potential to accelerate discovery and design of lithium-battery electrolytes. However, whether they can autonomously complete scientifically reliable electrolyte MD studies remains insufficiently evaluated. We introduce ElectrolyteMD-Bench, which unifies dilute, high-concentration, and localized high-concentration electrolytes in a comparative study across distinct solvation regimes. The benchmark connects local coordination, ionic association, single-particle dynamics, and collective transport within one continuous research process. Agents autonomously perform system construction, equilibration, production sampling, property analysis, and interpretation under a frozen scientific model, while deciding whether to correct errors, continue sampling, or stop. We audit each study along four dimensions: simulation validity, analysis validity, evidence integrity, and outcome calibration. Across eight model--harness configurations, all 24 system runs produced valid production trajectories, yet none of the eight studies satisfied all scientific requirements. Failures included missing analyses, incorrect physical definitions or numerical implementations, insufficient statistical support, and completion claims inconsistent with retained evidence. These results expose a gap between successful MD workflow execution and reliable scientific study completion. ElectrolyteMD-Bench therefore provides a foundational test and defines a key capability threshold that autonomous electrolyte simulation must cross to advance toward computation-first materials exploration.

physics.chem-ph↗

arXiv2026

Weather-Conditioned Depth Anything

Monocular depth estimation foundation models, such as the Depth Anything series, have achieved remarkable performance across diverse domains. However, they still suffer from critical failures under adverse weather conditions, such as fog, rain, snow, or at night. To address this, we present Weather-Conditioned Depth Anything (DA-W), a framework that explicitly disentangles style from content for weather-robust depth estimation. Specifically, we introduce a Style Filter trained on a curated mix of real and synthetic degradation datasets to extract content-independent, degradation-aware weather embeddings. This style embedding is then injected into the Depth Anything backbone using a parameter-efficient, zero-initialized adapter. Such a lightweight modulation allows a single unified model to robustly adapt to diverse conditions, including fog, rain, snow, and low-light, while avoiding catastrophic forgetting of its core generalization abilities in normal conditions. We train the adapter using a pseudo-label distillation and alignment strategy. Our comprehensive experiments demonstrate that our proposed DA-W achieves state-of-the-art robust depth estimation, improving AbsRel by an average of 3.7% on our curated weather benchmarks, while matching or slightly outperforming performance on standard clean benchmarks. Our project page is available at https://zhaoming-tamu.github.io/WCDA/.

cs.CV↗

arXiv2026

4KLSDB: A Large-Scale Dataset for 4K Image Restoration and Generation

High-resolution datasets are essential for advancing super-resolution (SR) and text-to-image (T2I) diffusion research. However, current publicly available datasets lack both the native 4K resolution and the extensive scale necessary for training state-of-the-art models. To address this gap, we introduce a 4K Large Scale Dataset and Benchmark (4KLSDB), a large-scale, diverse dataset consisting of 129,484 carefully curated 4K resolution images spanning multiple categories such as nature, urban scenes, people, food, artwork, and CGI, alongside distinct validation and test sets containing 2,000 and 1,984 images respectively. Images were sourced from established open datasets including Photo Concept Bucket, Laion2B, and PD12M. 4KLSDB underwent rigorous multi-stage automated filtering and annotation pipelines involving both human annotators and Large Multimodal Models (LMMs) to ensure high aesthetic quality and dataset consistency. We demonstrate 4KLSDB's effectiveness by training representative super-resolution and diffusion models, observing significant improvements in performance on native 4K benchmarks. Comprehensive experiments illustrate a positive correlation between training on true 4K resolution data and improved fidelity in image restoration task, especially on 4K resolution. We provide the research community a valuable resource to drive progress toward genuinely high-fidelity image synthesis and restoration by providing 4KLSDB. Our project page is available at: https://4klsdb.github.io/.

cs.CV↗