Selected work

Representative Papers

Solution Space Partitioning for Extremal Set Theory

Aug 05, 2026

This work addresses long-standing open problems in extremal set theory, such as Chvátal’s conjecture, by introducing a novel paradigm for customized search space partitioning based on solution construction strategies, replacing conventional domain-agnostic lookahead-based methods. By integrating this approach with a proof-generating exact mixed-integer linear programming (MILP) solver, the proposed framework substantially enhances search efficiency. Empirical evaluation demonstrates successful verification of the largest finite instance of Chvátal’s conjecture to date, marking significant progress toward resolving this fundamental problem in combinatorics.

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Learning Lookahead Lemmas for Neural Network Verification

Jul 31, 2026

This work addresses the limited efficiency and scalability of branch-and-bound methods in neural network verification by proposing an in-processing framework grounded in lookahead reasoning. By deriving novel lemmas over unstable ReLU nodes, the approach constructs an implication graph to intelligently prune the search space and activate Boolean cutting planes. Notably, it introduces for the first time a lookahead lemma-learning mechanism into neural network verification, effectively integrating implication-based reasoning with cutting plane generation. When integrated into the Marabou and α-β-CROWN verifiers, the method significantly enhances performance on standard benchmarks, proving up to 34% more instances unsatisfiable compared to the base solvers.

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The incremental voter model: mean-field analysis and convergence to equilibrium

May 27, 2026

This study addresses the challenge of modeling social influence and opinion polarization dynamics in discrete-opinion systems by proposing the Incremental Voter Model (IVM), wherein agents update their discrete opinions in unit steps (±1) under the influence of randomly selected persuaders. For the first time, a bounded-step updating mechanism is introduced into discrete-opinion interaction models, accompanied by a rigorous mean-field theoretical framework. Through multi-agent simulations and limiting analysis of stochastic processes, the authors derive a system of mean-field ordinary differential equations governing the evolution of opinion distributions in large populations, and rigorously establish its convergence and equilibrium properties. This work significantly advances the theoretical understanding of the mechanisms underlying opinion polarization.

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Viverra: Text-to-Code with Guarantees

May 14, 2026

This work addresses the lack of correctness guarantees in existing text-to-code generation methods, which still rely heavily on manual inspection and thus hinder development efficiency. To bridge this gap, the paper introduces verifiable formal assertions into the code generation process for the first time: a large language model simultaneously produces C code and candidate assertions, which are then jointly verified using a bounded model checker. This approach provides partial correctness guarantees and enhances code interpretability. Experiments across 18 programming tasks demonstrate that the method efficiently generates code accompanied by verifiable assertions. Furthermore, a user study involving over 400 participants confirms that these assertions significantly improve developers’ understanding of the generated code.

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The Luna Bound Propagator for Formal Analysis of Neural Networks

Mar 24, 2026

This work addresses the limited deployability of existing alpha-CROWN implementations, which are restricted to Python and thus challenging to integrate into formal verification tools and production systems. To overcome this limitation, we propose Luna—the first general-purpose, C++-based bound propagator that supports Interval Bound Propagation (IBP), CROWN, and parameterized alpha-CROWN across neural networks with arbitrary computational graph structures. Luna delivers the first high-performance C++ implementation of alpha-CROWN, substantially enhancing its feasibility for industrial deployment. Empirical evaluation on the VNN-COMP 2025 benchmark demonstrates that Luna achieves boundary tightness and computational efficiency on par with state-of-the-art implementations, while offering the robustness and speed benefits inherent to compiled C++ code.

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Recent publications

Latest Papers

Solution Space Partitioning for Extremal Set Theory

Aug 05, 2026

This work addresses long-standing open problems in extremal set theory, such as Chvátal’s conjecture, by introducing a novel paradigm for customized search space partitioning based on solution construction strategies, replacing conventional domain-agnostic lookahead-based methods. By integrating this approach with a proof-generating exact mixed-integer linear programming (MILP) solver, the proposed framework substantially enhances search efficiency. Empirical evaluation demonstrates successful verification of the largest finite instance of Chvátal’s conjecture to date, marking significant progress toward resolving this fundamental problem in combinatorics.

0 citationsRead paper

Learning Lookahead Lemmas for Neural Network Verification

Jul 31, 2026

This work addresses the limited efficiency and scalability of branch-and-bound methods in neural network verification by proposing an in-processing framework grounded in lookahead reasoning. By deriving novel lemmas over unstable ReLU nodes, the approach constructs an implication graph to intelligently prune the search space and activate Boolean cutting planes. Notably, it introduces for the first time a lookahead lemma-learning mechanism into neural network verification, effectively integrating implication-based reasoning with cutting plane generation. When integrated into the Marabou and α-β-CROWN verifiers, the method significantly enhances performance on standard benchmarks, proving up to 34% more instances unsatisfiable compared to the base solvers.

0 citationsRead paper

The incremental voter model: mean-field analysis and convergence to equilibrium

May 27, 2026

This study addresses the challenge of modeling social influence and opinion polarization dynamics in discrete-opinion systems by proposing the Incremental Voter Model (IVM), wherein agents update their discrete opinions in unit steps (±1) under the influence of randomly selected persuaders. For the first time, a bounded-step updating mechanism is introduced into discrete-opinion interaction models, accompanied by a rigorous mean-field theoretical framework. Through multi-agent simulations and limiting analysis of stochastic processes, the authors derive a system of mean-field ordinary differential equations governing the evolution of opinion distributions in large populations, and rigorously establish its convergence and equilibrium properties. This work significantly advances the theoretical understanding of the mechanisms underlying opinion polarization.

0 citationsRead paper

Viverra: Text-to-Code with Guarantees

May 14, 2026

This work addresses the lack of correctness guarantees in existing text-to-code generation methods, which still rely heavily on manual inspection and thus hinder development efficiency. To bridge this gap, the paper introduces verifiable formal assertions into the code generation process for the first time: a large language model simultaneously produces C code and candidate assertions, which are then jointly verified using a bounded model checker. This approach provides partial correctness guarantees and enhances code interpretability. Experiments across 18 programming tasks demonstrate that the method efficiently generates code accompanied by verifiable assertions. Furthermore, a user study involving over 400 participants confirms that these assertions significantly improve developers’ understanding of the generated code.

0 citationsRead paper

The Luna Bound Propagator for Formal Analysis of Neural Networks

Mar 24, 2026

This work addresses the limited deployability of existing alpha-CROWN implementations, which are restricted to Python and thus challenging to integrate into formal verification tools and production systems. To overcome this limitation, we propose Luna—the first general-purpose, C++-based bound propagator that supports Interval Bound Propagation (IBP), CROWN, and parameterized alpha-CROWN across neural networks with arbitrary computational graph structures. Luna delivers the first high-performance C++ implementation of alpha-CROWN, substantially enhancing its feasibility for industrial deployment. Empirical evaluation on the VNN-COMP 2025 benchmark demonstrates that Luna achieves boundary tightness and computational efficiency on par with state-of-the-art implementations, while offering the robustness and speed benefits inherent to compiled C++ code.

0 citationsRead paper