Knowledge Tree
robustness
Facts (33)
Sources
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 17 facts
measurementThe minimum distance to an adversarial example quantifies the smallest perturbation necessary to alter a model’s prediction and serves as a direct indicator of robustness.
perspectiveThe authors of 'A Comprehensive Review of Neuro-symbolic AI for Robustness' identify three foundational factors essential for establishing neuro-symbolic AI as a trustworthy paradigm: robustness, uncertainty quantification (UQ), and intervenability.
claimNeuro-symbolic AI offers a promising alternative to conventional deep learning frameworks for addressing challenges related to model robustness, uncertainty quantification, and human intervenability.
referenceMohapatra, Weng, Chen, Liu, and Daniel (2020) propose a method for verifying the robustness of neural networks against a family of semantic perturbations.
perspectiveTsipras et al. argued that there is a fundamental trade-off between standard accuracy and robustness, suggesting that conventional accuracy metrics alone provide an incomplete assessment of model reliability.
claimRobustness is a critical component of trustworthy AI because it directly impacts the dependability and consistency of AI-driven decisions, particularly in high-stakes fields like healthcare, finance, and autonomous vehicles.
referenceWang, Ai, Lu, Su, Yu, Zhang, Zhu, and Liu (2024) provide a survey of methods for assessing neural network robustness in image recognition tasks.
claimNeuro-symbolic AI methods integrate the adaptive learning capabilities of neural networks with the structured, rule-based reasoning of symbolic systems to enhance system robustness, provide reliable uncertainty measures, and facilitate human intervention.
claimK. Acharya and H. Song authored the article 'A Comprehensive Review of Neuro-symbolic AI for Robustness, Uncertainty Quantification, and Intervenability', which was published in the Arab Journal of Science and Engineering, volume 51, pages 35–67, in 2026.
claimThe article 'A Comprehensive Review of Neuro-symbolic AI for Robustness, Uncertainty Quantification, and Intervenability' is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, provided appropriate credit is given to the original authors and source.
claimRobustness in AI models is defined as the ability to maintain stable and reliable performance when subjected to varied and unexpected conditions, extending beyond training data accuracy to include generalization across real-world scenarios.
referenceWenzel et al. explored the use of hyperparameter ensembles for robustness and uncertainty quantification in their 2020 paper.
claimThe research article 'A Comprehensive Review of Neuro-symbolic AI for Robustness, Uncertainty Quantification, and Intervenability' was partially supported by the U.S. National Science Foundation through Grant No. 2317117.
referenceThe paper 'A Comprehensive Review of Neuro-symbolic AI for Robustness' reviews techniques for modeling robustness, quantifying uncertainty, and enabling intervenability, while examining how logic, probability, and learning can be integrated into unified or modular architectures to support transparent, adaptive reasoning.
claimRobustness in AI models is defined as the ability to maintain performance under varied and unforeseen conditions, while Uncertainty Quantification (UQ) provides a measure of confidence in model predictions, and intervenability enables human operators to effectively intervene in AI system operations.
referenceFreiesleben and Grote (2023) propose a theory of robustness in machine learning that extends beyond simple generalization.
claimRobustness, uncertainty quantification (UQ), and intervenability are identified as the three interdependent pillars crucial for enhancing the trustworthiness of AI-driven decision-making.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 5 facts
claimRobustness in Neuro-Symbolic AI (NSAI) systems measures reliability and resilience to disruptions such as noisy data, adversarial inputs, or dynamic environments.
procedureThe study evaluates Neuro Symbolic Neuro architectures against criteria including generalization, scalability, data efficiency, reasoning, robustness, transferability, and interpretability.
claimNeuro-Symbolic AI (NSAI) systems aim to provide enhanced generalization, interpretability, and robustness by combining the adaptability of neural networks with the explicit reasoning capabilities of symbolic methods.
claimThe Neuro Symbolic Neuro architecture is the best-performing model, consistently achieving high ratings across data efficiency, reasoning, robustness, transferability, and interpretability criteria.
claimNeuro Symbolic Neuro architectures address the critical need for transparency and robustness in complex real-world applications by utilizing multi-agent collaboration.
A Survey on the Theory and Mechanism of Large Language Models arxiv.org Mar 12, 2026 5 facts
claimThe current landscape of large language models presents new challenges for defining and formalizing concepts like 'robustness', 'fairness', and 'privacy' compared to traditional machine learning, as noted by Chang et al. (2024), Anwar et al. (2024), Dominguez-Olmedo et al. (2025), and Hardt and Mendler-Dünner (2025).
claimTraditional machine learning literature extensively analyzed robustness (Muravev and Petiushko, 2021; Ruan et al., 2021), fairness (Kleinberg et al., 2016; Liu et al., 2019), and privacy (Li et al., 2017; Kairouz et al., 2015) because these concepts were well-defined and formalizable using precise mathematical objectives.
claimWolf et al. (2023) introduced the 'behavior expectation bounds' theoretical framework to formally investigate the fundamental limitations of robustness in Large Language Models.
claimLarge Language Models may be overfitting to the specific artifacts of a test set rather than the underlying task, leading to a fundamental lack of robustness, according to Lunardi et al. (2025).
claimIn the current landscape of Large Language Models, definitions of robustness, fairness, and privacy are often ambiguous and lack simple closed-form mathematical representations compared to traditional machine learning.
Track: Poster Session 3 - aistats 2026 virtual.aistats.org 2 facts
claimLearning-augmented algorithms must exhibit three key properties: consistency, robustness, and smoothness.
referenceSharmila Duppala, Juan Luque, John Dickerson, and Seyed Esmaeili introduced an algorithm for fair clustering with provable robustness guarantees that allows decision makers to trade off between robustness and clustering quality in settings where group memberships are noisy.
Combining large language models with enterprise knowledge graphs frontiersin.org Aug 26, 2024 1 fact
perspectiveAI solutions should be accompanied by a high degree of explainability, robustness, and precision to ensure that enrichment systems are transparent and reliable.
Construction of intelligent decision support systems through ... - Nature nature.com Oct 10, 2025 1 fact
claimThe 'quality of decision' dimension in the IKEDS evaluation framework is measured by correctness (congruence with expert advice), optimality (distance from provably optimal solutions), and robustness (insensitivity to input perturbations).
Building Trustworthy NeuroSymbolic AI Systems - arXiv arxiv.org 1 fact
claimZhang et al. (2023) identified reliability in LLMs by examining tendencies regarding hallucination, truthfulness, factuality, honesty, calibration, robustness, and interpretability.
Papers - Dr Vaishak Belle vaishakbelle.github.io 1 fact
referenceVaishak Belle and P. Barcelo authored the paper 'A Uniform Language for Safety, Robustness and Explainability', published in JELIA in 2025.