Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences最新文献_第5页

Concerning uncertainty-a systematic survey of uncertainty-aware XAI. 关于不确定性——不确定性感知的系统综述。

IF 3.7 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2026-08-27 DOI: 10.1098/rsta.2025.0079

Helena Löfström, Tuwe Löfström, Anders Hjort, Fatima Rabia Yapicioglu

{"title":"Concerning uncertainty-a systematic survey of uncertainty-aware XAI.","authors":"Helena Löfström, Tuwe Löfström, Anders Hjort, Fatima Rabia Yapicioglu","doi":"10.1098/rsta.2025.0079","DOIUrl":"https://doi.org/10.1098/rsta.2025.0079","url":null,"abstract":"This paper surveys uncertainty-aware explainable artificial intelligence (UAXAI), examining how uncertainty is incorporated into explanatory pipelines and how such methods are evaluated. Across the literature, three recurring approaches to uncertainty quantification (UQ) emerge (Bayesian, Monte Carlo and conformal methods), alongside distinct strategies for integrating uncertainty into explanations: assessing trustworthiness, constraining models or explanations and explicitly communicating uncertainty. Evaluation practices remain fragmented and largely model centred, with limited attention to users and inconsistent reporting of reliability properties (e.g. calibration, coverage and explanation stability). Recent work leans towards calibration, distribution-free techniques and recognizes explainer variability as a central concern. We argue that progress in UAXAI requires unified evaluation principles that link uncertainty propagation, robustness and human decision-making, and highlight counterfactual and calibration approaches as promising avenues for aligning interpretability with reliability. This article is part of the theme issue 'Advancing uncertainty quantification in AI systems'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"384 2327","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148831292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty-aware large language models: a scoping review of conformal prediction methods. 不确定性感知的大型语言模型：共形预测方法的范围审查。

IF 3.7 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2026-08-27 DOI: 10.1098/rsta.2026.0061

Alice Evelyn Ashby, Khuong An Nguyen, Zhiyuan Luo

{"title":"Uncertainty-aware large language models: a scoping review of conformal prediction methods.","authors":"Alice Evelyn Ashby, Khuong An Nguyen, Zhiyuan Luo","doi":"10.1098/rsta.2026.0061","DOIUrl":"https://doi.org/10.1098/rsta.2026.0061","url":null,"abstract":"In recent years, there has been growing interest in applying conformal prediction (CP) to large language models (LLMs) across diverse domains to enhance the trustworthiness of their predictions. However, the literature still lacks a comprehensive survey of this rapidly emerging area. Thus, to fill this gap, this article presents a comprehensive review and analysis of CP for LLMs. We review over 106 studies and propose a novel taxonomy that categorises existing methods into six groups. In addition, we observe trends in the literature with regard to LLM, dataset, and task selection, and make recommendations for researchers to improve task diversity and address gaps in black-box LLM uncertainty estimation. Interestingly, we find that logit-free methods tend to outperform logit-based methods, with a lower mean absolute coverage error and prediction set size. This suggests that logit-free methods, which rely on uncertainty signals based on self-consistency sampling, semantic diversity, and other methods, may sidestep known issues with miscalibrated token probabilities and may have advantageous robustness to tokenisation and decoding idiosyncrasies, particularly for open-ended generation where the performance gap is more pronounced. This indicates that black-box uncertainty signals may more directly capture semantic correctness or answer stability. We also find that on average, conformal methods for large vision language models (LVLMs) have higher overcoverage error than LLMs, and almost non-existent undercoverage error, suggesting that methods for LVLMs may be more conservative. While we do not claim a definitive causal explanation, empirical evidence suggests that conformal methods for LVLMs exhibit a stronger coverage-informativeness trade-off than those for LLMs. This article is part of the discussion meeting issue 'Advancing uncertainty quantification in AI systems'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"384 2327","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148831366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The interplay between Bayesian inference and conformal prediction. 贝叶斯推理与保形预测的相互作用。

IF 3.7 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2026-08-27 DOI: 10.1098/rsta.2025.0073

Nina Deliu, Brunero Liseo

{"title":"The interplay between Bayesian inference and conformal prediction.","authors":"Nina Deliu, Brunero Liseo","doi":"10.1098/rsta.2025.0073","DOIUrl":"https://doi.org/10.1098/rsta.2025.0073","url":null,"abstract":"Conformal prediction (CP) has emerged as a cutting-edge methodology in statistics and machine learning, providing prediction intervals with finite-sample frequentist coverage guarantees. Yet, its interplay with Bayesian statistics-often criticized for lacking frequentist guarantees-remains underexplored. Recent work has suggested that CP can 'calibrate' Bayesian prediction regions, thereby imparting frequentist validity and motivating deeper investigation into frequentist-Bayesian hybrids. On the other side, Bayesian procedures have the potential to enhance CP with more informative intervals, towards nearly optimal solutions under a decision-theoretic framework. Thus, the two paradigms can be jointly used for a principled balance between validity and efficiency. This work provides a unified treatment of this emerging interface with open directions. After surveying existing ideas, we consolidate the literature with a Bayesian version of split CP and present a simple analysis of the binomial model, investigating priors' role, efficiency and computational complexity. This article is part of the theme issue 'Advancing uncertainty quantification in AI systems'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"384 2327","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148831290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conformal risk control for non-monotonic losses. 非单调损失的保形风险控制。

IF 3.7 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2026-08-27 DOI: 10.1098/rsta.2025.0068

Anastasios N Angelopoulos

引用次数: 0

Preface to 'advancing uncertainty quantification in artificial intelligence systems using conformal prediction'. 前言“利用保形预测推进人工智能系统的不确定性量化”。

IF 3.7 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2026-08-27 DOI: 10.1098/rsta.2025.0067

Khuong An Nguyen, Rina Foygel Barber, Vicky Copley, Alex Gammerman, Vladimir Vovk, Johanna Ziegel

{"title":"Preface to 'advancing uncertainty quantification in artificial intelligence systems using conformal prediction'.","authors":"Khuong An Nguyen, Rina Foygel Barber, Vicky Copley, Alex Gammerman, Vladimir Vovk, Johanna Ziegel","doi":"10.1098/rsta.2025.0067","DOIUrl":"https://doi.org/10.1098/rsta.2025.0067","url":null,"abstract":"As artificial intelligence (AI) systems are being widely deployed in safety-critical and high-stakes applications (e.g. medical diagnosis, autonomous vehicles, financial risk assessment), there is a growing demand for providing reliable and trustworthy machine predictions. However, since AI models become more complex in structure (a prominent example being deep neural networks) and bigger in size (e.g. large language model systems such as ChatGPT and Gemini), being able to understand, explain and quantify confidence in their predictions are ongoing challenges. Therefore, this special issue is dedicated to exploring the forefront of reliable uncertainty quantification in AI systems, using conformal prediction (CP), a leading statistical framework that offers predictions with valid coverage guarantees under minimal assumptions. The issue comprises the most recent, most novel and most practical developments of CP-based methods in cutting-edge AI applications, highlighting improvements over traditional methods.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"384 2327","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148831260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty quantification in train delay prediction with conformal prediction. 保形预测列车延误预测中的不确定性量化。

IF 3.7 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2026-08-27 DOI: 10.1098/rsta.2025.0075

Rui Luo, Xiaoyi Su, Khuong An Nguyen

{"title":"Uncertainty quantification in train delay prediction with conformal prediction.","authors":"Rui Luo, Xiaoyi Su, Khuong An Nguyen","doi":"10.1098/rsta.2025.0075","DOIUrl":"https://doi.org/10.1098/rsta.2025.0075","url":null,"abstract":"Predicting train delays is crucial for railway operations and passenger experience, but point predictions fail to capture uncertainty, limiting their use in risk-aware decisions. Existing uncertainty quantification (UQ) methods often rely on unverifiable assumptions and produce miscalibrated intervals. This paper evaluates conformal prediction (CP) as a distribution-free framework for generating prediction intervals with rigorous coverage guarantees. Using a large-scale dataset from Southeastern Railway in the UK, we show that UQ methods such as quantile regression (QR), Monte Carlo dropout and deep ensembles (DE) frequently under- or over-cover nominal levels. By contrast, CP corrects miscalibration across models, ensuring valid marginal coverage. Conformalized QR (CQR) achieves the best efficiency by producing adaptively sized intervals while maintaining calibration. To address heterogeneity across stations, we apply Mondrian CP (MCP), which enforces conditional coverage within strata. Empirical results confirm MCP delivers reliable intervals for each subgroup. Our work demonstrates that CP is a model-agnostic, robust framework for trustworthy UQ, offering a practical pathway to reliable risk assessment in transportation and other high-stakes domains. This article is part of the theme issue 'Advancing uncertainty quantification in AI systems'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"384 2327","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148831265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synthetic data assurance with conformal prediction. 具有适形预测的综合数据保证。

IF 3.7 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2026-08-27 DOI: 10.1098/rsta.2025.0074

Vicky Copley, Ben Hiett

{"title":"Synthetic data assurance with conformal prediction.","authors":"Vicky Copley, Ben Hiett","doi":"10.1098/rsta.2025.0074","DOIUrl":"https://doi.org/10.1098/rsta.2025.0074","url":null,"abstract":"In this paper, we apply the methods of conformal prediction to the task of synthetic data assurance using images augmented with synthetic weather effects, and examine their fidelity and diversity with respect to both clear weather and adverse weather real image data. It can be expensive and time-consuming to collect enough data to train a machine learning (ML) model so that it makes accurate predictions in all contexts in which it is applied, and this has led to much interest in the use of synthetic data. Good-quality synthetic data must be similar to, but sufficiently different from any available real-world data in order to add value to model development; various methods for synthetic data assurance have been proposed. Such methods frequently do not offer performance guarantees on the quality of their output or focus on narrowly defined metrics. With conformal methods, we find that our adverse weather real data and synthetic augmentations are not sufficiently similar, indicating a requirement for improved methods of synthetic augmentation. Conformal approaches provide a holistic view of synthetic data quality, enable curation of synthetic datasets with theoretical guarantees and are generally applicable, making them a valuable tool in synthetic data assurance. This article is part of the theme issue 'Advancing uncertainty quantification in AI systems'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"384 2327","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148831322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conformal prediction for multi-label learning: a review of methods and guarantees. 多标签学习的保形预测：方法和保证的回顾。

IF 3.7 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2026-08-27 DOI: 10.1098/rsta.2025.0071

Harris Papadopoulos

{"title":"Conformal prediction for multi-label learning: a review of methods and guarantees.","authors":"Harris Papadopoulos","doi":"10.1098/rsta.2025.0071","DOIUrl":"https://doi.org/10.1098/rsta.2025.0071","url":null,"abstract":"Multi-label learning (MLL) is a machine learning paradigm that aims to predict a set of labels for each instance, rather than a single class. Such tasks arise in a wide range of real-world applications and pose significant challenges, including an exponentially large output space, dependence among labels and often severe label imbalance. These challenges amplify predictive uncertainty, making reliable uncertainty quantification essential. Conformal prediction (CP) is an attractive answer: it converts model outputs into prediction regions with distribution-free, finite-sample guarantees under the sole assumption of data exchangeability. Several adaptations of CP to the multi-label setting have been proposed. Yet these vary widely in scoring constructions, output types and targeted guarantees. This review consolidates the landscape of CP adaptations for MLL. It places existing approaches under a unified framework, examining the types of outputs and guarantees they provide, where label dependencies are incorporated, and how inference cost scales with the number of labels. It provides an in-depth analysis of all approaches using common notation, identifying their key characteristics along with their practical implications and assessing their strengths and limitations. Finally, it compares approaches side-by-side, highlighting trade-offs among guarantee types, precision of regions, compactness of outputs and scalability. This article is part of the theme issue 'Advancing uncertainty quantification in AI systems'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"384 2327","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148831251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Review of Clifford algebra-based AI methods in signal analysis. 基于Clifford代数的人工智能信号分析方法综述。

IF 3.7 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2026-08-13 DOI: 10.1098/rsta.2025.0099

Roman Byrtus, Francesco Prinzi, Petr Vasik, Salvatore Vitabile

引用次数: 0

Geometric algebra in electrical engineering: a comprehensive review of power systems applications. 几何代数在电气工程：电力系统应用的全面回顾。

IF 3.7 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2026-08-13 DOI: 10.1098/rsta.2025.0112

Francisco G Montoya, Alfredo Alcayde, Francisco M Arrabal-Campos

{"title":"Geometric algebra in electrical engineering: a comprehensive review of power systems applications.","authors":"Francisco G Montoya, Alfredo Alcayde, Francisco M Arrabal-Campos","doi":"10.1098/rsta.2025.0112","DOIUrl":"10.1098/rsta.2025.0112","url":null,"abstract":"This review provides a comprehensive overview of the applications of geometric algebra (GA) in electrical engineering, with a particular focus on power systems. The paper covers the fundamental concepts of GA and its advantages over traditional mathematical approaches in power system analysis. Key topics explored include: an introduction to GA and its relevance to electrical engineering; AC circuit theory using GA, covering GA formulations for power flow analysis in non-sinusoidal conditions including harmonic and interharmonic analysis for single-phase and multi-phase systems; power-free current compensation techniques that move beyond traditional reactive power concepts; parameter identification in electrical circuits using GA; GA-based perspectives on electrical transformations (Clarke or Fortescue) and the analysis of electrical curves and their geometric invariants. The review synthesizes recent advancements, including contributions from the authors and colleagues, discusses future directions and highlights the potential of GA to address current and emerging challenges in power system analysis, control, smart grids and renewable energy systems. We aim to provide both a theoretical foundation and practical insights for researchers and practitioners in the field. This article is part of the theme issue 'Modern applications of geometric algebra'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"384 2326","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148725516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0