Selected work

Representative Papers

AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems

Apr 17, 2026

This work proposes a post-training framework that integrates synthetic data generation with reinforcement learning to enable large language models to automatically translate natural language descriptions of operations research (OR) problems into formal optimization models—including linear, mixed-integer, and nonlinear formulations—thereby substantially reducing reliance on specialized OR expertise. The approach innovatively leverages solver feedback as a reward signal and introduces a curriculum-based reinforcement learning strategy tailored for nonlinear dynamical problems, achieving, for the first time, effective automated modeling in this challenging domain. Evaluated on an 8B-parameter model, the method matches or exceeds state-of-the-art performance across six standard OR benchmarks, rivaling results from significantly larger models, and boosts solution accuracy on nonlinear dynamics tasks from near 0% to within solvable ranges.

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Scalable Geospatial Data Generation Using AlphaEarth Foundations Model

Aug 15, 2025

To address the limited geographic coverage and scarcity of high-quality labels in global geospatial annotation data, this paper proposes a cross-regional transfer framework based on the AlphaEarth Foundations (AEF) model—marking the first use of its global geospatial representation capability to generalize a fine-grained vegetation classification model (80 classes), trained in the U.S., to Canada. Methodologically, the framework integrates high-density geographic features extracted by AEF with lightweight classifiers—including random forests and logistic regression—thereby circumventing reliance on region-specific annotations. Experiments demonstrate classification accuracies of 81% on the U.S. validation set and 73% on the Canadian validation set. Qualitative analysis confirms strong alignment between predicted labels and ground-truth vegetation distributions. This work significantly enhances the scalability and cross-domain generalization capacity of geospatial models, establishing a reusable technical pathway for remote sensing interpretation in low-resource regions.

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Recent publications

Latest Papers

AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems

Apr 17, 2026

This work proposes a post-training framework that integrates synthetic data generation with reinforcement learning to enable large language models to automatically translate natural language descriptions of operations research (OR) problems into formal optimization models—including linear, mixed-integer, and nonlinear formulations—thereby substantially reducing reliance on specialized OR expertise. The approach innovatively leverages solver feedback as a reward signal and introduces a curriculum-based reinforcement learning strategy tailored for nonlinear dynamical problems, achieving, for the first time, effective automated modeling in this challenging domain. Evaluated on an 8B-parameter model, the method matches or exceeds state-of-the-art performance across six standard OR benchmarks, rivaling results from significantly larger models, and boosts solution accuracy on nonlinear dynamics tasks from near 0% to within solvable ranges.

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Scalable Geospatial Data Generation Using AlphaEarth Foundations Model

Aug 15, 2025

To address the limited geographic coverage and scarcity of high-quality labels in global geospatial annotation data, this paper proposes a cross-regional transfer framework based on the AlphaEarth Foundations (AEF) model—marking the first use of its global geospatial representation capability to generalize a fine-grained vegetation classification model (80 classes), trained in the U.S., to Canada. Methodologically, the framework integrates high-density geographic features extracted by AEF with lightweight classifiers—including random forests and logistic regression—thereby circumventing reliance on region-specific annotations. Experiments demonstrate classification accuracies of 81% on the U.S. validation set and 73% on the Canadian validation set. Qualitative analysis confirms strong alignment between predicted labels and ground-truth vegetation distributions. This work significantly enhances the scalability and cross-domain generalization capacity of geospatial models, establishing a reusable technical pathway for remote sensing interpretation in low-resource regions.

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