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Researcher profile

Naiming Liu

Naiming Liu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computation and LanguageComputer Visioncond-mat.mtrl-sciMachine Learning

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Published work

4 published item(s)

preprint2026arXiv

MalruleLib: Large-Scale Executable Misconception Reasoning with Step Traces for Modeling Student Thinking in Mathematics

Student mistakes in mathematics are often systematic: a learner applies a coherent but wrong procedure and repeats it across contexts. We introduce MalruleLib, a learning-science-grounded framework that translates documented misconceptions into executable procedures, drawing on 67 learning-science and mathematics education sources, and generates step-by-step traces of malrule-consistent student work. We formalize a core student-modeling problem as Malrule Reasoning Accuracy (MRA): infer a misconception from one worked mistake and predict the student's next answer under cross-template rephrasing. Across nine language models (4B-120B), accuracy drops from 66% on direct problem solving to 40% on cross-template misconception prediction. MalruleLib encodes 101 malrules over 498 parameterized problem templates and produces paired dual-path traces for both correct reasoning and malrule-consistent student reasoning. Because malrules are executable and templates are parameterizable, MalruleLib can generate over one million instances, enabling scalable supervision and controlled evaluation. Using MalruleLib, we observe cross-template degradations of 10-21%, while providing student step traces improves prediction by 3-15%. We release MalruleLib as infrastructure for educational AI that models student procedures across contexts, enabling diagnosis and feedback that targets the underlying misconception.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

MedHorizon: Towards Long-context Medical Video Understanding in the Wild

Medical multimodal large language models (MLLMs) have advanced image understanding and short-video analysis, but real clinical review often requires full-procedure video understanding. Unlike general long videos, medical procedures contain highly redundant anatomical views, while decisive evidence is temporally sparse, spatially subtle, and context dependent. Existing benchmarks often assume this evidence has already been localized through images, short clips, or pre-segmented videos, leaving the retrieval-before-reasoning problem under-tested. We introduce MedHorizon, an in-the-wild benchmark for long-context medical video understanding. MedHorizon preserves 759 hours of full-length clinical procedures and provides 1,253 evidence-grounded multiple-choice questionsthat jointly evaluate sparse evidence understanding and multi-hop clinical reasoning. Its evidence is extremely sparse, with only 0.166% evidence frames on average, requiring models to search noisy procedural streams before interpreting and aggregating findings. We evaluate representative general-domain, medical-domain, and long-video MLLMs. The best model reaches only 41.1% accuracy, showing that current systems remain far from robust full-procedure understanding. Further analysis yields four key findings: performance does not scale reliably with more frames, evidence retrieval and clinical interpretation remain primary bottlenecks; these bottlenecks are rooted in weak procedural reasoning and attention drift under redundancy, and generic sampling methods only partially balances local detail with global coverage. MedHorizon provides a rigorous testbed for MLLMs that retrieve sparse evidence and reason over complete clinical workflows.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

NeuroView-RNN: It's About Time

Recurrent Neural Networks (RNNs) are important tools for processing sequential data such as time-series or video. Interpretability is defined as the ability to be understood by a person and is different from explainability, which is the ability to be explained in a mathematical formulation. A key interpretability issue with RNNs is that it is not clear how each hidden state per time step contributes to the decision-making process in a quantitative manner. We propose NeuroView-RNN as a family of new RNN architectures that explains how all the time steps are used for the decision-making process. Each member of the family is derived from a standard RNN architecture by concatenation of the hidden steps into a global linear classifier. The global linear classifier has all the hidden states as the input, so the weights of the classifier have a linear mapping to the hidden states. Hence, from the weights, NeuroView-RNN can quantify how important each time step is to a particular decision. As a bonus, NeuroView-RNN also offers higher accuracy in many cases compared to the RNNs and their variants. We showcase the benefits of NeuroView-RNN by evaluating on a multitude of diverse time-series datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The physical and mechanical properties of hafnium orthosilicate: experiments and first-principles calculations

Hafnium orthosilicate (HfSiO4: hafnon) has been proposed as an environmental barrier coating (EBC) material to protect silicon coated, silicon-based ceramic materials at high temperatures and as a candidate dielectric material in microelectronic devices. It can naturally form at the interface between silicon dioxide (SiO2) and hafnia (HfO2). When used in these applications, its coefficient of thermal expansion (CTE) should match that of silicon and SiC composites to reduce the stored elastic strain energy, and thus risk of failure of these systems. The physical, mechanical, thermodynamic and thermal transport properties of hafnon have been investigated using a combination of both density functional theory (DFT) calculations and experimental assessments. The average linear coefficient of thermal expansion (CTE) calculated using the quasi-harmonic approximation increase from 3.06 10-6 K-1 to 6.36 10-6 K-1, as the temperature increases from 300 to 1500 K, in agreement with both X-ray diffraction lattice parameter and dilatometry measurements. The predicted thermal conductivity from Boltzmann transport theory was approximately 18 W/m.K at 300K. Both hot disk and laser flash measurements gave a thermal conductivity of 13.3 W/m.K. This slightly lower value is indicative of residual disorder in the experimental samples that was absent in the theoretical analysis. First-principles calculations and nanoindentation techniques were used to assess the ambient temperature elastic constants and bulk modulus respectively. The elastic properties obtained by both approaches agreed to within 5% validating the computational approach and its future use for study of the thermomechanical properties of other oxides or silicates.

0 citations0 reviewsNo code linkedOpen access