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Zhijie Huang

Zhijie Huang contributes to research discovery and scholarly infrastructure.

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Artificial Intelligence math.DG Computation and Language Computational Engineering, Finance, and Science Distributed, Parallel, and Cluster Computing hep-th math.AG math.AP math.CV Multimedia

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preprint2026arXiv

KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistry

Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building an AI-powered chemical brain, which frames chemical intelligence around four core capabilities: information extraction, semantic parsing, knowledge-based QA, and reasoning & planning. We argue that domain knowledge and logic are essential pillars for enabling such a system to assist and accelerate scientific discovery. To initiate this effort, we introduce our first generation of large language models for chemistry: KALE-LM-Chem and KALE-LM-Chem-1.5, which have achieved outstanding performance in tasks related to the field of chemistry. We hope that our work serves as a strong starting point, helping to realize more intelligent AI and promoting the advancement of human science and technology, as well as societal development.

preprint2026arXiv

TierCheck: Tiered Checkpointing for Fault Tolerance in Large Language Model Training

Large Language Model (LLM) training is frequently interrupted by a heterogeneous spectrum of failures, from common GPU crashes to catastrophic cluster-wide outages. Existing checkpointing systems rely on monolithic, single-tier storage backend, forcing a trade-off between state-saving overhead and recovery speed. We propose TierCheck, a cluster-aware tiered checkpointing system that aligns storage placement with failure heterogeneity. TierCheck adopts a three-tier design that maintains lightweight differential checkpoints in local and peer memory for fast localized recovery, while asynchronously migrating heavyweight base checkpoints to remote persistent storage. It also ensures strict global consistency across tiers without stalling training, and achieves fast cluster-aware checkpoint restoration during recovery. Evaluations on models up to 40 billion parameters show that TierCheck achieves low training overhead, reduces end-to-end checkpointing time to under 10s, and supports high-frequency checkpointing, ultimately striking an optimal balance between low-overhead persistence and fast recovery.

preprint2021arXiv

A Construction of Infinitely Many Solutions to the Strominger System

In this paper we construct explicit smooth solutions to the Strominger system on generalized Calabi-Gray manifolds, which are compact non-Kähler Calabi-Yau 3-folds with infinitely many distinct topological types and sets of Hodge numbers.

preprint2020arXiv

An Efficient QP Variable Convolutional Neural Network Based In-loop Filter for Intra Coding

In this paper, a novel QP variable convolutional neural network based in-loop filter is proposed for VVC intra coding. To avoid training and deploying multiple networks, we develop an efficient QP attention module (QPAM) which can capture compression noise levels for different QPs and emphasize meaningful features along channel dimension. Then we embed QPAM into the residual block, and based on it, we design a network architecture that is equipped with controllability for different QPs. To make the proposed model focus more on examples that have more compression artifacts or is hard to restore, a focal mean square error (MSE) loss function is employed to fine tune the network. Experimental results show that our approach achieves 4.03\% BD-Rate saving on average for all intra configuration, which is even better than QP-separate CNN models while having less model parameters.

preprint2011arXiv

Gradient estimates for the porous medium equations on Riemannian manifolds

In this paper we study gradient estimates for the positive solutions of the porous medium equation: $$u_t=Δu^m$$ where $m>1$, which is a nonlinear version of the heat equation. We derive local gradient estimates of the Li-Yau type for positive solutions of porous medium equations on Riemannian manifolds with Ricci curvature bounded from below. As applications, several parabolic Harnack inequalities are obtained. In particular, our results improve the ones of Lu, Ni, Vázquez and Villani in [10]. Moreover, our results recover the ones of Davies in [4], Hamilton in [5] and Li and Xu in [7].

Zhijie Huang

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5 published item(s)

KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistry

TierCheck: Tiered Checkpointing for Fault Tolerance in Large Language Model Training

A Construction of Infinitely Many Solutions to the Strominger System

An Efficient QP Variable Convolutional Neural Network Based In-loop Filter for Intra Coding

Gradient estimates for the porous medium equations on Riemannian manifolds