{"total":76,"rows":[{"id":"2103.06624","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","created":"2021-03-11","cited_by_count":228,"area":null},{"id":"2309.14348","title":"Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM","created":"2023-09-18","cited_by_count":192,"area":"alignment"},{"id":"2104.02610","title":"On the Robustness of Vision Transformers to Adversarial Examples","created":"2021-03-31","cited_by_count":167,"area":null},{"id":"2103.15670","title":"On the Adversarial Robustness of Vision Transformers","created":"2021-03-29","cited_by_count":106,"area":null},{"id":"2305.11391","title":"A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation","created":"2023-05-19","cited_by_count":93,"area":null},{"id":"2105.03743","title":"Certified Robustness to Text Adversarial Attacks by Randomized [MASK]","created":"2021-05-08","cited_by_count":57,"area":null},{"id":"2110.02797","title":"Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs","created":"2021-10-06","cited_by_count":57,"area":null},{"id":"2108.12777","title":"Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution","created":"2021-08-29","cited_by_count":56,"area":null},{"id":"2405.21063","title":"Neural Network Verification with Branch-and-Bound for General Nonlinearities","created":"2024-05-31","cited_by_count":47,"area":null},{"id":"2210.07394","title":"Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation","created":"2022-10-13","cited_by_count":45,"area":null},{"id":"2206.12227","title":"Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective","created":"2022-06-24","cited_by_count":42,"area":null},{"id":"2104.08782","title":"On the Sensitivity and Stability of Model Interpretations in NLP","created":"2021-04-18","cited_by_count":30,"area":null},{"id":"2104.05062","title":"Achieving Model Robustness through Discrete Adversarial Training","created":"2021-04-11","cited_by_count":23,"area":null},{"id":"2203.06414","title":"A Survey of Adversarial Defences and Robustness in NLP","created":"2022-03-12","cited_by_count":23,"area":null},{"id":"2307.07171","title":"Certified Robustness for Large Language Models with Self-Denoising","created":"2023-07-14","cited_by_count":23,"area":null},{"id":"2104.08645","title":"Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training","created":"2021-04-17","cited_by_count":21,"area":null},{"id":"2211.02878","title":"Textual Manifold-based Defense Against Natural Language Adversarial Examples","created":"2022-11-05","cited_by_count":19,"area":null},{"id":"2303.01713","title":"Convex Bounds on the Softmax Function with Applications to Robustness Verification","created":"2023-03-03","cited_by_count":19,"area":null},{"id":"2303.11126","title":"Robustifying Token Attention for Vision Transformers","created":"2023-03-20","cited_by_count":19,"area":null},{"id":"2102.07818","title":"Certified Robustness to Programmable Transformations in LSTMs","created":"2021-02-15","cited_by_count":18,"area":null},{"id":"2110.15156","title":"Blending Anti-Aliasing into Vision Transformer","created":"2021-10-28","cited_by_count":16,"area":null},{"id":"2203.10378","title":"On Robust Prefix-Tuning for Text Classification","created":"2022-03-19","cited_by_count":15,"area":null},{"id":"2403.06925","title":"Transformers Learn Low Sensitivity Functions: Investigations and Implications","created":"2024-03-11","cited_by_count":14,"area":null},{"id":"2202.13817","title":"Robust Textual Embedding against Word-level Adversarial Attacks","created":"2022-02-28","cited_by_count":12,"area":null},{"id":"2203.12709","title":"Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation","created":"2022-03-23","cited_by_count":12,"area":null},{"id":"2403.20254","title":"Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions","created":"2024-03-29","cited_by_count":12,"area":null},{"id":"2201.00768","title":"Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions","created":"2022-01-03","cited_by_count":11,"area":null},{"id":"2201.13351","title":"LinSyn: Synthesizing Tight Linear Bounds for Arbitrary Neural Network Activation Functions","created":"2022-01-31","cited_by_count":10,"area":null},{"id":"2210.16140","title":"Localized Randomized Smoothing for Collective Robustness Certification","created":"2022-10-28","cited_by_count":10,"area":null},{"id":"2305.10157","title":"Efficient Error Certification for Physics-Informed Neural Networks","created":"2023-05-17","cited_by_count":10,"area":null},{"id":"2311.17400","title":"Improving the Robustness of Transformer-based Large Language Models with Dynamic Attention","created":"2023-11-29","cited_by_count":10,"area":null},{"id":"2104.05232","title":"Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation","created":"2021-04-12","cited_by_count":9,"area":null},{"id":"2206.03044","title":"CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustness","created":"2022-06-07","cited_by_count":9,"area":null},{"id":"2211.05523","title":"Impact of Adversarial Training on Robustness and Generalizability of Language Models","created":"2022-11-10","cited_by_count":9,"area":null},{"id":"2302.02568","title":"Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend","created":"2023-02-06","cited_by_count":9,"area":null},{"id":"2109.05698","title":"Detecting Textual Adversarial Examples through Randomized Substitution and Vote","created":"2021-09-13","cited_by_count":8,"area":null},{"id":"2211.05417","title":"Can Transformers Reason in Fragments of Natural Language?","created":"2022-11-10","cited_by_count":8,"area":"lr"},{"id":"2302.02023","title":"TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification","created":"2023-02-03","cited_by_count":8,"area":null},{"id":"2305.04003","title":"ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification","created":"2023-05-06","cited_by_count":6,"area":null},{"id":"2305.10773","title":"Rate-Adaptive Coding Mechanism for Semantic Communications With Multi-Modal Data","created":"2023-05-18","cited_by_count":6,"area":null},{"id":"2202.03932","title":"Are Transformers More Robust? Towards Exact Robustness Verification for Transformers","created":"2022-02-08","cited_by_count":5,"area":null},{"id":"2207.07539","title":"3DVerifier: Efficient Robustness Verification for 3D Point Cloud Models","created":"2022-07-15","cited_by_count":5,"area":null},{"id":"2405.18548","title":"Transformer Encoder Satisfiability: Complexity and Impact on Formal Reasoning","created":"2024-05-28","cited_by_count":5,"area":null},{"id":"2412.03154","title":"SoundnessBench: A Soundness Benchmark for Neural Network Verifiers","created":"2024-12-04","cited_by_count":5,"area":null},{"id":"2201.08555","title":"Identifying Adversarial Attacks on Text Classifiers","created":"2022-01-21","cited_by_count":4,"area":null},{"id":"2210.14957","title":"Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness","created":"2022-10-26","cited_by_count":4,"area":null},{"id":"2508.09320","title":"Exact Verification of Graph Neural Networks with Incremental Constraint Solving","created":"2025-08-12","cited_by_count":4,"area":null},{"id":"2312.16364","title":"Robustness Verification for Knowledge-Based Logic of Risky Driving Scenes","created":"2023-12-27","cited_by_count":3,"area":null},{"id":"2201.03829","title":"Quantifying Robustness to Adversarial Word Substitutions","created":"2022-01-11","cited_by_count":2,"area":null},{"id":"2210.12233","title":"TCAB: A Large-Scale Text Classification Attack Benchmark","created":"2022-10-21","cited_by_count":2,"area":null},{"id":"2210.12396","title":"ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation","created":"2022-10-22","cited_by_count":2,"area":null},{"id":"2212.13989","title":"AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs","created":"2022-12-13","cited_by_count":2,"area":null},{"id":"2510.23389","title":"Floating-Point Neural Network Verification at the Software Level","created":"2025-10-27","cited_by_count":2,"area":null},{"id":"2605.10974","title":"Vertex-Softmax: Tight Transformer Verification via Exact Softmax Optimization","created":"2026-05-08","cited_by_count":2,"area":null},{"id":"2310.01452","title":"Fooling the Textual Fooler via Randomizing Latent Representations","created":"2023-10-02","cited_by_count":1,"area":null},{"id":"2402.07621","title":"Correctness Verification of Neural Networks Approximating Differential Equations","created":"2024-02-12","cited_by_count":1,"area":null},{"id":"2402.15929","title":"Certifying Knowledge Comprehension in LLMs","created":"2024-02-24","cited_by_count":1,"area":null},{"id":"2405.17361","title":"A One-Layer Decoder-Only Transformer is a Two-Layer RNN: With an Application to Certified Robustness","created":"2024-05-27","cited_by_count":1,"area":null},{"id":"2509.18836","title":"Bounded PCTL Model Checking of Large Language Model Outputs","created":"2025-09-23","cited_by_count":1,"area":null},{"id":"2510.06756","title":"Verifying Memoryless Sequential Decision-making of Large Language Models","created":"2025-10-08","cited_by_count":1,"area":null},{"id":"2512.20755","title":"Bridging Efficiency and Safety: Formal Verification of Neural Networks with Early Exits","created":"2025-12-23","cited_by_count":1,"area":"inference"},{"id":"2602.14318","title":"In Transformer We Trust? A Perspective on Transformer Architecture Failure Modes","created":"2026-02-15","cited_by_count":1,"area":null},{"id":"2609.34457","title":"ZonoGPT: Towards An Abstract Domain for Verifying Large GPT Models","created":"2026-09-28","cited_by_count":1,"area":null},{"id":"2109.01558","title":"Learning Neural Models for Natural Language Processing in the Face of Distributional Shift","created":"2021-09-03","cited_by_count":0,"area":null},{"id":"2206.14575","title":"Why Robust Natural Language Understanding is a Challenge","created":"2022-06-21","cited_by_count":0,"area":null},{"id":"2209.12708","title":"Faith: An Efficient Framework for Transformer Verification on GPUs","created":"2022-09-23","cited_by_count":0,"area":null},{"id":"2401.05338","title":"STR-Cert: Robustness Certification for Deep Text Recognition on Deep Learning Pipelines and Vision Transformers","created":"2023-11-28","cited_by_count":0,"area":null},{"id":"2404.11538","title":"GenFighter: A Generative and Evolutive Textual Attack Removal","created":"2024-04-17","cited_by_count":0,"area":null},{"id":"2505.12767","title":"Language Models That Walk the Talk: A Framework for Formal Fairness Certificates","created":"2025-05-19","cited_by_count":0,"area":null},{"id":"2509.17413","title":"Distributionally Robust Safety Verification of Neural Networks via Worst-Case CVaR","created":"2025-09-22","cited_by_count":0,"area":null},{"id":"2509.23806","title":"Influence-Guided Concolic Testing of Transformer Robustness","created":"2025-09-28","cited_by_count":0,"area":null},{"id":"2603.10689","title":"Contract And Conquer: How to Provably Compute Adversarial Examples for a Black-Box Model?","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2605.14294","title":"Precise Verification of Transformers through ReLU-Catalyzed Abstraction Refinement","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2607.18195","title":"Certified Training for Convolutional Perturbations","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2607.20286","title":"Sound Probabilistic Safety Bounds for Large Language Models","created":"2026-07-22","cited_by_count":0,"area":"safety"},{"id":"2609.26112","title":"Certified Mechanistic Interpretability: Lifting Single-Input Findings to Bounded Neighbourhoods","created":"2026-08-17","cited_by_count":0,"area":"mechinterp"}],"next_cursor":null}