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Peixin Wang

5 accepted papers

2026

Neural Theorem Proving for Verification Conditions: A Real-World Benchmark

ICLR 2026poster

Theorem proving is fundamental to program verification, where the automated proof of Verification Conditions (VCs) remains a primary bottleneck. Real-world program verification frequently encounters hard VCs that existing Automated Theorem Provers cannot prove, leading to a critical need for extensi…

Cited by 0SourcecodeScholar
2025

A Variable Stiffness Supernumerary Robotic Limb with Pneumatic-Tendon Coupled Actuation *

IROS 2025

Supernumerary robotic limbs (SRLs) can assist humans in achieving efficient and comfortable work in daily life or industrial assembly scenarios, requiring SRLs to switch between rigidity and flexibility to perform compliant movements while also providing stable support for humans to reduce fatigue f

Cited by 0SourceScholar
2025

UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System

EMNLP 2025

Recent advances in large language models (LLMs) have significantly improved automated code generation, enabling tools such as GitHub Copilot and CodeWhisperer to assist developers in a wide range of programming tasks. However, the translation of complex mobile UI designs into high-fidelity front-end

Cited by 0SourcePDFScholar
2024

Robustness Verification of Deep Reinforcement Learning Based Control Systems Using Reward Martingales

AAAI 2024technical

Deep Reinforcement Learning (DRL) has gained prominence as an effective approach for control systems. However, its practical deployment is impeded by state perturbations that can severely impact system performance. Addressing this critical challenge requires robustness verification about system perf…

Cited by 1SourcePDFScholar
2023

Boosting Verification of Deep Reinforcement Learning via Piece-Wise Linear Decision Neural Networks

NeurIPS 2023poster

Formally verifying deep reinforcement learning (DRL) systems suffers from both inaccurate verification results and limited scalability. The major obstacle lies in the large overestimation introduced inherently during training and then transforming the inexplicable decision-making models, i.e., deep…

Cited by 2SourcePDFScholar