{"total":24,"rows":[{"id":"2308.04748","title":"Fuzz4All: Universal Fuzzing with Large Language Models","created":"2023-08-09","cited_by_count":88,"area":null},{"id":"2310.15991","title":"WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models","created":"2023-10-24","cited_by_count":37,"area":null},{"id":"2404.09894","title":"Glitch Tokens in Large Language Models: Categorization Taxonomy and Effective Detection","created":"2024-04-15","cited_by_count":29,"area":null},{"id":"2303.07476","title":"Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision","created":"2023-03-13","cited_by_count":11,"area":null},{"id":"2506.14813","title":"Training with Confidence: Catching Silent Errors in Deep Learning Training with Automated Proactive Checks","created":"2025-06-06","cited_by_count":10,"area":null},{"id":"2307.04963","title":"DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization","created":"2023-07-11","cited_by_count":9,"area":null},{"id":"2302.02261","title":"NeuRI: Diversifying DNN Generation via Inductive Rule Inference","created":"2023-02-04","cited_by_count":7,"area":null},{"id":"2509.11173","title":"Your Compiler is Backdooring Your Model: Understanding and Exploiting Compilation Inconsistency Vulnerabilities in Deep Learning Compilers","created":"2025-09-14","cited_by_count":7,"area":null},{"id":"2404.17871","title":"Deep Learning Library Testing: Definition, Methods and Challenges","created":"2024-04-27","cited_by_count":5,"area":null},{"id":"2506.15961","title":"TrainVerify: Equivalence-Based Verification for Distributed LLM Training","created":"2025-06-19","cited_by_count":3,"area":null},{"id":"2404.09151","title":"Productively Deploying Emerging Models on Emerging Platforms: A Top-Down Approach for Testing and Debugging","created":"2024-04-14","cited_by_count":2,"area":null},{"id":"2505.01892","title":"DiTOX: Fault Detection and Localization in the ONNX Optimizer","created":"2025-05-03","cited_by_count":1,"area":null},{"id":"2506.09280","title":"TTrace: Lightweight Error Checking and Diagnosis for Distributed Training","created":"2025-06-10","cited_by_count":1,"area":null},{"id":"2602.10478","title":"GPU-Fuzz: Finding Memory Errors in Deep Learning Frameworks","created":"2026-02-11","cited_by_count":1,"area":null},{"id":"2306.01697","title":"Exploring Robustness of Image Recognition Models on Hardware Accelerators","created":"2023-06-02","cited_by_count":0,"area":null},{"id":"2411.10764","title":"ML$^2$Tuner: Efficient Code Tuning via Multi-Level Machine Learning Models","created":"2024-11-16","cited_by_count":0,"area":null},{"id":"2508.09505","title":"Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference","created":"2025-08-13","cited_by_count":0,"area":null},{"id":"2508.20340","title":"Once4All: Skeleton-Guided SMT Solver Fuzzing with LLM-Synthesized Generators","created":"2025-08-28","cited_by_count":0,"area":null},{"id":"2603.24595","title":"M2K: Making the Model-Kernel Interface Explicit for Reliable CUDA Kernel Verification","created":"2026-03-06","cited_by_count":0,"area":null},{"id":"2604.08720","title":"Demystifying the Silence of Correctness Bugs in PyTorch Compiler","created":"2026-04-09","cited_by_count":0,"area":null},{"id":"2606.22519","title":"Detecting and Understanding Vulnerabilities in Fully Homomorphic Encryption Frameworks","created":"2026-06-21","cited_by_count":0,"area":null},{"id":"2609.22220","title":"Measuring the Checker: Mutation Analysis for GPU-Kernel Benchmark Oracles","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.22991","title":"Silent Failures Beyond the 32-Bit Index Range: A Differential Characterization of Large-Tensor Matrix Multiplication in PyTorch's MPS Backend","created":"2026-09-19","cited_by_count":0,"area":null},{"id":"2610.15808","title":"Grounding Deep Learning Framework Testing in Data Flow","created":"2026-10-11","cited_by_count":0,"area":null}],"next_cursor":null}