We study the problem of generating robust counterfactual explanations for deep learning models subject to model changes. We focus on plausible model changes altering model parameters and propose a novel framework to reason about the robustness property in this setting. To motivate our solution, we begin by showing for the first time that computing the robustness of counterfactuals with respect to model changes is NP-hard. As this (practically) rules out the existence of scalable algorithms for exactly computing robustness, we propose a novel probabilistic approach which is able to provide tight estimates of robustness with strong guarantees while preserving scalability. Remarkably, and differently from existing solutions targeting plausible model changes, our approach does not impose requirements on the network to be analysed, thus enabling robustness analysis on a wider range of architectures, including state-of-the-art tabular transformers. A thorough experimental analysis on four binary classification datasets reveals that our method improves the state of the art in generating robust explanations, outperforming existing methods.
{"title":"Probabilistically robust counterfactual explanations under model changes","authors":"Luca Marzari , Francesco Leofante , Ferdinando Cicalese , Alessandro Farinelli","doi":"10.1016/j.artint.2025.104459","DOIUrl":"10.1016/j.artint.2025.104459","url":null,"abstract":"<div><div>We study the problem of generating robust counterfactual explanations for deep learning models subject to model changes. We focus on <em>plausible model changes</em> altering model parameters and propose a novel framework to reason about the robustness property in this setting. To motivate our solution, we begin by showing for the first time that computing the robustness of counterfactuals with respect to model changes is NP-hard. As this (practically) rules out the existence of scalable algorithms for exactly computing robustness, we propose a novel probabilistic approach which is able to provide tight estimates of robustness with strong guarantees while preserving scalability. Remarkably, and differently from existing solutions targeting plausible model changes, our approach does not impose requirements on the network to be analysed, thus enabling robustness analysis on a wider range of architectures, including state-of-the-art tabular transformers. A thorough experimental analysis on four binary classification datasets reveals that our method improves the state of the art in generating robust explanations, outperforming existing methods.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"351 ","pages":"Article 104459"},"PeriodicalIF":4.6,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-07DOI: 10.1016/j.artint.2025.104460
Manel Rodriguez-Soto , Roxana Rădulescu , Filippo Bistaffa , Oriol Ricart , Arnau Mayoral-Macau , Maite Lopez-Sanchez , Juan A. Rodriguez-Aguilar , Ann Nowé
This paper addresses the problem of ensuring that autonomous learning agents align with multiple moral values. Specifically, we present the theoretical principles and algorithmic tools necessary for creating an environment where we ensure that the agent learns a behaviour aligned with multiple moral values while striving to achieve its individual objective. To address this value alignment problem, we adopt the Multi-Objective Reinforcement Learning framework and propose a novel algorithm that combines techniques from Multi-Objective Reinforcement Learning and Linear Programming. In addition, we illustrate our value alignment process with an example involving an autonomous vehicle. Here, we demonstrate that the agent learns to behave in alignment with the ethical values of safety, achievement, and comfort, with achievement representing the agent’s individual objective. Such ethical behaviour differs depending on the ordering between values. We also use a synthetic multi-objective environment to evaluate the computational costs of guaranteeing ethical learning as the number of values increases.
{"title":"Multi-objective reinforcement learning for provably incentivising alignment with value systems","authors":"Manel Rodriguez-Soto , Roxana Rădulescu , Filippo Bistaffa , Oriol Ricart , Arnau Mayoral-Macau , Maite Lopez-Sanchez , Juan A. Rodriguez-Aguilar , Ann Nowé","doi":"10.1016/j.artint.2025.104460","DOIUrl":"10.1016/j.artint.2025.104460","url":null,"abstract":"<div><div>This paper addresses the problem of ensuring that autonomous learning agents align with multiple moral values. Specifically, we present the theoretical principles and algorithmic tools necessary for creating an environment where we ensure that the agent learns a behaviour aligned with multiple moral values while striving to achieve its individual objective. To address this value alignment problem, we adopt the Multi-Objective Reinforcement Learning framework and propose a novel algorithm that combines techniques from Multi-Objective Reinforcement Learning and Linear Programming. In addition, we illustrate our value alignment process with an example involving an autonomous vehicle. Here, we demonstrate that the agent learns to behave in alignment with the ethical values of safety, achievement, and comfort, with achievement representing the agent’s individual objective. Such ethical behaviour differs depending on the ordering between values. We also use a synthetic multi-objective environment to evaluate the computational costs of guaranteeing ethical learning as the number of values increases.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"351 ","pages":"Article 104460"},"PeriodicalIF":4.6,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1016/j.artint.2025.104456
Lydia Blümel, Markus Ulbricht
{"title":"Defining Defense and Defeat in Abstract Argumentation From Scratch – A Generalizing Approach","authors":"Lydia Blümel, Markus Ulbricht","doi":"10.1016/j.artint.2025.104456","DOIUrl":"https://doi.org/10.1016/j.artint.2025.104456","url":null,"abstract":"","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"26 1","pages":""},"PeriodicalIF":14.4,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-30DOI: 10.1016/j.artint.2025.104458
Alexis De Colnet , Sebastian Ordyniak , Stefan Szeider
Ordered Binary Decision Diagrams (OBDDs) are dynamic data structures with many application areas. The literature suggested that OBDDs of bounded width equate to Boolean circuits of bounded pathwidth. In this paper, we show that this relationship holds only for complete OBDDs. Additionally, we demonstrate that similar limitations affect the claimed equivalence between Sentential Decision Diagrams (SDDs) of bounded width and Boolean circuits of bounded treewidth.
{"title":"OBDDs, SDDs, and circuits of bounded width: Completeness matters","authors":"Alexis De Colnet , Sebastian Ordyniak , Stefan Szeider","doi":"10.1016/j.artint.2025.104458","DOIUrl":"10.1016/j.artint.2025.104458","url":null,"abstract":"<div><div>Ordered Binary Decision Diagrams (OBDDs) are dynamic data structures with many application areas. The literature suggested that OBDDs of bounded width equate to Boolean circuits of bounded pathwidth. In this paper, we show that this relationship holds only for complete OBDDs. Additionally, we demonstrate that similar limitations affect the claimed equivalence between Sentential Decision Diagrams (SDDs) of bounded width and Boolean circuits of bounded treewidth.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"351 ","pages":"Article 104458"},"PeriodicalIF":4.6,"publicationDate":"2025-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-22DOI: 10.1016/j.artint.2025.104454
Hui Chen , Xuhui Fan , Hengyu Liu , Yaqiong Li , Zhilin Zhao , Feng Zhou , Christopher John Quinn , Longbing Cao
Temporal point processes (TPPs) are effective for modeling event occurrences over time but struggle with sparse and uncertain events in federated systems, where privacy is a major concern. To address this, we propose FedPP, a federated neural nonparametric point process model. FedPP integrates neural embeddings into sigmoidal Gaussian Cox processes (SGCPs) on the client side. SGCPs is a flexible and expressive class of TPPs, allowing FedPP to generate highly flexible intensity functions that capture client-specific event dynamics and uncertainties while efficiently summarizing historical records. For global aggregation, FedPP introduces a divergence-based mechanism to communicate the distributions of kernel hyperparameters in SGCPs between the server and clients, while keeping client-specific parameters local to ensure privacy and personalization. FedPP effectively captures event uncertainty and sparsity. Extensive experiments demonstrate its superior performance in federated settings, showing global aggregation with the KL divergence and the Wasserstein distance.
{"title":"Federated neural nonparametric point processes","authors":"Hui Chen , Xuhui Fan , Hengyu Liu , Yaqiong Li , Zhilin Zhao , Feng Zhou , Christopher John Quinn , Longbing Cao","doi":"10.1016/j.artint.2025.104454","DOIUrl":"10.1016/j.artint.2025.104454","url":null,"abstract":"<div><div>Temporal point processes (TPPs) are effective for modeling event occurrences over time but struggle with sparse and uncertain events in federated systems, where privacy is a major concern. To address this, we propose <em>FedPP</em>, a federated neural nonparametric point process model. FedPP integrates neural embeddings into sigmoidal Gaussian Cox processes (SGCPs) on the client side. SGCPs is a flexible and expressive class of TPPs, allowing FedPP to generate highly flexible intensity functions that capture client-specific event dynamics and uncertainties while efficiently summarizing historical records. For global aggregation, FedPP introduces a divergence-based mechanism to communicate the distributions of kernel hyperparameters in SGCPs between the server and clients, while keeping client-specific parameters local to ensure privacy and personalization. FedPP effectively captures event uncertainty and sparsity. Extensive experiments demonstrate its superior performance in federated settings, showing global aggregation with the KL divergence and the Wasserstein distance.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"351 ","pages":"Article 104454"},"PeriodicalIF":4.6,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1016/j.artint.2025.104455
Xinyuan Zhao , Hanlin Gu , Lixin Fan , Yuxing Han , Qiang Yang
Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data privacy. Yet the wide applicability of FL is hindered by the entanglement of data distributions across different clients. This paper demonstrates for the first time that by disentangling data distributions, FL can in principle achieve efficiencies comparable to those of distributed systems, requiring only one round of communication. To this end, we propose a novel FedDistr algorithm, which employs diffusion models to decouple and recover data distributions. Empirical results on the CIFAR100, DomainNet, OfficeHome, and ISIC2020 datasets show that FedDistr significantly enhances model utility and efficiency in both disentangled and near-disentangled scenarios while ensuring privacy, outperforming traditional federated learning methods.
{"title":"Disentangling data distribution for optimal and communication-efficient federated learning","authors":"Xinyuan Zhao , Hanlin Gu , Lixin Fan , Yuxing Han , Qiang Yang","doi":"10.1016/j.artint.2025.104455","DOIUrl":"10.1016/j.artint.2025.104455","url":null,"abstract":"<div><div>Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data privacy. Yet the wide applicability of FL is hindered by the entanglement of data distributions across different clients. This paper demonstrates for the first time that by disentangling data distributions, FL can in principle achieve efficiencies comparable to those of distributed systems, requiring only one round of communication. To this end, we propose a novel FedDistr algorithm, which employs diffusion models to decouple and recover data distributions. Empirical results on the CIFAR100, DomainNet, OfficeHome, and ISIC2020 datasets show that FedDistr significantly enhances model utility and efficiency in both disentangled and near-disentangled scenarios while ensuring privacy, outperforming traditional federated learning methods.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"351 ","pages":"Article 104455"},"PeriodicalIF":4.6,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1016/j.artint.2025.104457
Predrag Teovanović , Srdjan Vesic , Bruno Yun
This paper presents a comprehensive examination of human compliance with normative principles of argumentation across two experimental studies. The first study investigated whether fundamental argumentation principles such as anonymity, independence, void precedence, and maximality align with human reasoning. Additionally, it explored whether graph-based representations of arguments facilitate better understanding and adherence to these principles compared to textual representations of arguments alone and examined the role of individual cognitive differences in compliance with these principles. Our experiments revealed that graph-based representations significantly improved compliance with argumentation principles, particularly among individuals with higher cognitive reflection. The second study replicated and extended the first study’s findings, introducing new principles such as skeptical precedence and simple reinstatement, and explored the effects of presenting arguments solely in graphical form, as well as the impact of a short tutorial on argumentation theory. The study also assessed participants’ ability to perform graphical tasks and how this influenced their compliance with normative principles. Results partially replicated the first study’s findings, confirming that graphical representations enhance compliance, but also revealed that the effect does not generalize to the new principles. We found evidence that in the absence of a graphical representation, performing graphical tasks can improve compliance with principles; especially drawing the argumentation graph. Moreover, a brief tutorial significantly improved performance on several principles, indicating that even minimal instruction can enhance understanding and compliance. However, the difficulties observed with the simple reinstatement principle hint that the participants’ intuition about the notion of defense diverges significantly from that of the researchers and that more careful thoughts must be put in crafting them. These studies collectively suggest that while argumentation principles can be intuitive to some extent, their comprehension and application are significantly influenced by the instruction given as well as by graphical representations and processes used to obtain them. These findings have important implications for the design of future argumentation-based tools and our understanding of how to bridge human reasoning and formal argumentation.
{"title":"Human compliance with computational argumentation principles","authors":"Predrag Teovanović , Srdjan Vesic , Bruno Yun","doi":"10.1016/j.artint.2025.104457","DOIUrl":"10.1016/j.artint.2025.104457","url":null,"abstract":"<div><div>This paper presents a comprehensive examination of human compliance with normative principles of argumentation across two experimental studies. The first study investigated whether fundamental argumentation principles such as anonymity, independence, void precedence, and maximality align with human reasoning. Additionally, it explored whether graph-based representations of arguments facilitate better understanding and adherence to these principles compared to textual representations of arguments alone and examined the role of individual cognitive differences in compliance with these principles. Our experiments revealed that graph-based representations significantly improved compliance with argumentation principles, particularly among individuals with higher cognitive reflection. The second study replicated and extended the first study’s findings, introducing new principles such as skeptical precedence and simple reinstatement, and explored the effects of presenting arguments solely in graphical form, as well as the impact of a short tutorial on argumentation theory. The study also assessed participants’ ability to perform graphical tasks and how this influenced their compliance with normative principles. Results partially replicated the first study’s findings, confirming that graphical representations enhance compliance, but also revealed that the effect does not generalize to the new principles. We found evidence that in the absence of a graphical representation, performing graphical tasks can improve compliance with principles; especially drawing the argumentation graph. Moreover, a brief tutorial significantly improved performance on several principles, indicating that even minimal instruction can enhance understanding and compliance. However, the difficulties observed with the simple reinstatement principle hint that the participants’ intuition about the notion of defense diverges significantly from that of the researchers and that more careful thoughts must be put in crafting them. These studies collectively suggest that while argumentation principles can be intuitive to some extent, their comprehension and application are significantly influenced by the instruction given as well as by graphical representations and processes used to obtain them. These findings have important implications for the design of future argumentation-based tools and our understanding of how to bridge human reasoning and formal argumentation.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"351 ","pages":"Article 104457"},"PeriodicalIF":4.6,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1016/j.artint.2025.104453
Andre Opris
This article addresses theory in evolutionary many-objective optimization and focuses on the role of crossover operators. The advantages of using crossover are hardly understood and rigorous runtime analyses with crossover are lagging far behind its use in practice, specifically in the case of more than two objectives. We present two many-objective problems and , and a theoretical runtime analysis of the GSEMO and the widely used NSGA‑III algorithm, to demonstrate that one point crossover on , as well as uniform crossover on , can yield an exponential speedup in the runtime. In particular, when the number of objectives is constant, this algorithms can find the Pareto set of both problems in expected polynomial time when using crossover, while without crossover they require exponential time to even find a single Pareto-optimal point. For either problem, we also demonstrate a significant performance gap in certain superconstant parameter regimes for the number of objectives. To the best of our knowledge, this is the first rigorous runtime analysis in many-objective optimization which demonstrates an exponential performance gap when using crossover for more than two objectives. Additionally, it is the first runtime analysis involving crossover in many-objective optimization where the number of objectives is not necessarily constant.
{"title":"Many-objective problems where crossover is provably essential","authors":"Andre Opris","doi":"10.1016/j.artint.2025.104453","DOIUrl":"10.1016/j.artint.2025.104453","url":null,"abstract":"<div><div>This article addresses theory in evolutionary many-objective optimization and focuses on the role of crossover operators. The advantages of using crossover are hardly understood and rigorous runtime analyses with crossover are lagging far behind its use in practice, specifically in the case of more than two objectives. We present two many-objective problems <span><math><msub><mtext>RR</mtext><mrow><mi>MO</mi></mrow></msub></math></span> and <span><math><msub><mtext>URR</mtext><mrow><mi>MO</mi></mrow></msub></math></span>, and a theoretical runtime analysis of the GSEMO and the widely used NSGA‑III algorithm, to demonstrate that one point crossover on <span><math><msub><mtext>RR</mtext><mrow><mi>MO</mi></mrow></msub></math></span>, as well as uniform crossover on <span><math><msub><mtext>URR</mtext><mrow><mi>MO</mi></mrow></msub></math></span>, can yield an exponential speedup in the runtime. In particular, when the number of objectives is constant, this algorithms can find the Pareto set of both problems in expected polynomial time when using crossover, while without crossover they require exponential time to even find a single Pareto-optimal point. For either problem, we also demonstrate a significant performance gap in certain superconstant parameter regimes for the number of objectives. To the best of our knowledge, this is the first rigorous runtime analysis in many-objective optimization which demonstrates an exponential performance gap when using crossover for more than two objectives. Additionally, it is the first runtime analysis involving crossover in many-objective optimization where the number of objectives is not necessarily constant.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"350 ","pages":"Article 104453"},"PeriodicalIF":4.6,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145461589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-31DOI: 10.1016/j.artint.2025.104443
Junjun Guo , Yifan Liu , Zhengtao Yu
Domain-specific Multimodal Neural Machine Translation (DMNMT) aims to translate text in specialized domains by leveraging both linguistic context and associated visual information to resolve domain-specific ambiguities and enhance terminological accuracy. Although accompanying images often provide sparse and fragmented visual cues that could potentially anchor critical domain semantics, the semantic mapping from images to textual domain semantics typically exhibits sparse multi-focal alignment challenges. Existing general-domain multimodal neural machine translation (MNMT) models and large language models (LLMs) struggle to achieve accurate aggregation of domain-salient information, often resulting in near-equivalent yet imprecise terminology translations or outright errors. To bridge this sparse domain semantic correspondence gap, we introduce the Asymmetric Siamese Multimodal Fusion (ASMF) framework, which decouples domain representation learning into two complementary branches that both consume text: a domain-specific virtual visual content generation (DVVG) branch and a terminology-aware textual (TAT) branch. The DVVG branch distills sparse, localized visual features into modality-agnostic semantic anchors through mask-constrained multi-focal distillation, while the TAT branch captures terminology-dense textual context. We introduce a novel Domain-Virtualized Pivot-driven Hierarchical Fusion (DVPH) strategy that progressively injects distilled visual anchors across encoder layers. This asymmetric dual-branch design effectively couples spatially fragmented visual details with terminology-rich text, enabling accurate and domain-consistent translations even for low-frequency terms. Extensive experiments were conducted on four benchmark datasets covering three distinct scenarios: two domain-specific datasets (Fashion-MMT and EMMT), one general-domain dataset (Multi30K), and one multi-domain dataset (WIT). Comprehensive evaluations demonstrate that the proposed approach outperforms existing MNMT, DMNMT and LLMs, achieving state-of-the-art (SOTA) results across all datasets. In-depth analyses validate its robustness and generalization capabilities across diverse scenarios, including visually noisy or image-free conditions.
{"title":"Bridging sparse domain semantics via an asymmetric siamese framework with virtual anchor guidance for domain-specific multimodal translation","authors":"Junjun Guo , Yifan Liu , Zhengtao Yu","doi":"10.1016/j.artint.2025.104443","DOIUrl":"10.1016/j.artint.2025.104443","url":null,"abstract":"<div><div>Domain-specific Multimodal Neural Machine Translation (DMNMT) aims to translate text in specialized domains by leveraging both linguistic context and associated visual information to resolve domain-specific ambiguities and enhance terminological accuracy. Although accompanying images often provide sparse and fragmented visual cues that could potentially anchor critical domain semantics, the semantic mapping from images to textual domain semantics typically exhibits sparse multi-focal alignment challenges. Existing general-domain multimodal neural machine translation (MNMT) models and large language models (LLMs) struggle to achieve accurate aggregation of domain-salient information, often resulting in near-equivalent yet imprecise terminology translations or outright errors. To bridge this sparse domain semantic correspondence gap, we introduce the Asymmetric Siamese Multimodal Fusion (ASMF) framework, which decouples domain representation learning into two complementary branches that both consume text: a domain-specific virtual visual content generation (DVVG) branch and a terminology-aware textual (TAT) branch. The DVVG branch distills sparse, localized visual features into modality-agnostic semantic anchors through mask-constrained multi-focal distillation, while the TAT branch captures terminology-dense textual context. We introduce a novel Domain-Virtualized Pivot-driven Hierarchical Fusion (DVPH) strategy that progressively injects distilled visual anchors across encoder layers. This asymmetric dual-branch design effectively couples spatially fragmented visual details with terminology-rich text, enabling accurate and domain-consistent translations even for low-frequency terms. Extensive experiments were conducted on four benchmark datasets covering three distinct scenarios: two domain-specific datasets (Fashion-MMT and EMMT), one general-domain dataset (Multi30K), and one multi-domain dataset (WIT). Comprehensive evaluations demonstrate that the proposed approach outperforms existing MNMT, DMNMT and LLMs, achieving state-of-the-art (SOTA) results across all datasets. In-depth analyses validate its robustness and generalization capabilities across diverse scenarios, including visually noisy or image-free conditions.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"350 ","pages":"Article 104443"},"PeriodicalIF":4.6,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145404964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-26DOI: 10.1016/j.artint.2025.104441
Sebastian Ordyniak , Giacomo Paesani , Mateusz Rychlicki , Stefan Szeider
We develop a general algorithmic framework that allows us to obtain fixed-parameter tractability for computing smallest symbolic models that represent given data. Our framework applies to all ML model types that admit a certain extension property. By establishing this extension property for decision trees, decision sets, decision lists, and binary decision diagrams, we obtain that minimizing these fundamental model types is fixed-parameter tractable. Our framework even applies to ensembles, which combine individual models by majority decision.
{"title":"A General Theoretical Framework for Learning Smallest Interpretable Models","authors":"Sebastian Ordyniak , Giacomo Paesani , Mateusz Rychlicki , Stefan Szeider","doi":"10.1016/j.artint.2025.104441","DOIUrl":"10.1016/j.artint.2025.104441","url":null,"abstract":"<div><div>We develop a general algorithmic framework that allows us to obtain fixed-parameter tractability for computing smallest symbolic models that represent given data. Our framework applies to all ML model types that admit a certain extension property. By establishing this extension property for decision trees, decision sets, decision lists, and binary decision diagrams, we obtain that minimizing these fundamental model types is fixed-parameter tractable. Our framework even applies to ensembles, which combine individual models by majority decision.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"350 ","pages":"Article 104441"},"PeriodicalIF":4.6,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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