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IJCAI 2026

IKnowFlow: Trustworthy RAG for Sensitive Domains

Yash Saxena

RAG systems ground LLM responses in external evidence, yet their trustworthiness remains underspecified. Retrieval, re-ranking, and generation are optimized independently with no unified account of whether the system is interpretable, grounded, and traceable. My thesis operationalizes trustworthy RAG through three current sub-goals, each at the stage where it is most vulnerable. For traceability, REASONS benchmarks LLMs on 20K papers, showing attribution failures are systematic even under retrieval augmentation. For interpretability, IMRNNs decomposes dense embeddings via learned adapters, exposing human-readable query-document matching at retrieval. For grounding, METEORA replaces opaque re-ranking with rationale-driven selection, ensuring generation operates over explicitly justified context. I unify these under Interpretable Knowledge Flow (IKnowFlow), where trustworthiness is preserved across stage boundaries rather than checked at output. IKnowFlow will next address two longstanding blind spots: negation-awareness in retrieval, where surface similarity masks semantic inversion, and provenance-aware verification through structured source graphs.

IJCAI 2026

Recovering Formal Guarantees for Neural Network-Controlled Systems Under Changing Dynamics

Sterre Lutz

Neural networks enable high-performance decision-making, but their deployment in safety-critical settings requires formal guarantees that may become invalid after changes in the system dynamics. This research studies how such guarantees can be recovered using information from the initial verification, enabling post-deployment analysis to assess preservation, quantify degradation, and restore compliant behavior without recomputing verification from scratch.

IJCAI 2026

Trustworthy AI for Post-Stroke Care

Victor F. Lopes de Souza

As AI systems are increasingly deployed in high-stakes settings, two concerns become particularly important: cautiousness (awareness of model limitations for risk-aware decisions) and explainability (providing human-understandable justifications). My PhD research develops methods that enable both, from theoretical and application perspectives. On the theoretical side, I formalize cautious and interpretable models for multiple uncertainty frameworks, with algorithms for explanation and preference elicitation under uncertainty. On the application side, I implement these methods in post-stroke rehabilitation, designing AI systems that analyze multimodal clinical data to support individualized rehabilitation planning.

IJCAI 2026

Towards Cognitively-aligned AI with Analogical Reasoning Abilities

Mohammadhossein Khojasteh

Analogical reasoning, the ability to map different domains using their structural similarity, is a core mechanism of human cognition that supports tasks such as problem-solving, argumentation, and creativity. Despite recent advances in AI systems, including Large Language Models (LLMs), they still struggle to perform analogical reasoning reliably. In this project, we get inspiration by cognitive theories of analogy and try to develop models that can better capture and perform analogical reasoning. As a key component, we investigate abstraction levels and study how to best extract and represent them. In addition, we systematically analyze the limitations of LLMs by developing a dataset annotated with analogical structure and explore fine-tuning approaches to improve their analogical reasoning abilities.

IJCAI 2026

KG-MultiMS: a Knowledge Graph-Enhanced Multimodal Framework for Multiple Sclerosis

Riccardo Francia

Despite advances in the computational analysis of multiple sclerosis, the ability to accurately predict disease progression remains limited due to unimodal approaches in the context of deep learning. KG-MultiMS proposes as a multimodal deep-learning framework that integrates data from three heterogeneous sources: (1) MRI volumes using the Swin-UNETR architecture to learn both semantic embeddings and segmentation masks of brain lesions; (2) tabular clinical variables using the FT-Tokenizer architecture to learn clinical embeddings; (3) medical knowledge graph based on the Harvard-Oxford atlas using a Heterogeneous Graph Transformer to learn contextual embeddings from patient-specific subgraphs. A cross-attention mechanism will allow for leveraging multimodal contributions by dynamically weighting them based on the anatomical-functional concordance represented in the contextual embeddings.

IJCAI 2026

Towards Comprehensive Benchmarking of Domain Generalization and Adaptation for Time-Series Forecasting in Clinical Multivariate Time-Series Forecasting

Mayra Elwes

Deep learning models for clinical time-series forecasting face significant challenges in generalizability when deployed across different clinical settings. Various choices in the deep learning pipeline can affect a model's out-of-distribution performance. This abstract introduces a comprehensive benchmarking framework to assess domain generalization and adaptation under varying distribution shifts in multivariate time-series forecasting using real-world data. Preliminary empirical results on the influence of backbone model architecture and a proxy metric, the robustness slope, are presented alongside remaining challenges.

IJCAI 2026

Ontology-Aware LLMs for Human-Usable Knowledge Graph Systems

Luiz do Valle Miranda

Knowledge graphs (KGs) promise machine-readable semantics together with human-readable labels and relations, yet domain experts struggle to use them due to technical barriers, especially in automatically constructed graphs with unlabeled nodes and complex connections. While LLMs enable KG interfaces such as retrieval-augmented generation, their potential to leverage ontology definitions to improve graph readability remains unexplored. This research investigates LLMs as ontology-aware verbalization interfaces that generate human-readable and usable textual information encoded in the KG structure.

IJCAI 2026

Configuring Neuro-Symbolic Systems with Guaranteed Properties with Application in Hybrid Intelligence

Johannes E. Bendler

Neuro-symbolic systems are a promising paradigm for Hybrid Intelligence (HI), combining the complementary strengths of symbolic reasoning and machine learning in human-AI interactions. However, designing architectures that satisfy desired properties currently relies on informal reasoning, rather than a rigorous framework. Our research addresses this gap by developing a formal intermediate layer that links architecture design to system properties. Building on the visual pattern language of the Boxology for neuro-symbolic systems, we develop a mathematical formalization that is grounded in Category Theory, in order to enable proof-based guarantees of properties. The soundness of this formalism will be demonstrated through empirical case studies of existing Hybrid Intelligence systems. This work advances the theoretical foundations of neuro-symbolic AI by supporting principled design of HI systems.

IJCAI 2026

Cybersecurity in AI-Enabled Digital Ecosystems: Evolutionary Game-Theoretic Modelling and Multi-Agent Defence

Adeela Bashir

In this era of Artifical Intelligence (AI), cyber threats are no longer static, as modern attackers are intelligent, adaptive, and capable of exploiting vulnerabilities faster than traditional defences can respond. My research investigates how attack and defence strategies co-evolve in complex, adversarial environments by integrating evolutionary game theory (EGT) with AI-driven multi-agent systems (MAS). This combined framework allows me to model strategic interactions between competing actors, analyse how defensive decisions influence overall security outcomes, and identify the conditions under which defences hold or break down. A particular focus of my work is the emergence of coordinated behaviours in multi-agent AI systems that can compromise system reliability in ways that existing safety approaches do not adequately address. Through theoretical modelling and controlled multi-agent experiments, my research proposes mechanisms that reduce attack success and strengthen defensive resilience. This work contributes to the understanding of adaptive cyber conflicts and the development of more robust and trust-worthy AI-enabled digital systems.

IJCAI 2026

Equivariant Graph Neural Networks for Protein Interaction Modeling and Structure-Aware Molecular Design

Animesh Animesh

Equivariant graph neural networks (EGNNs) have revolutionized molecular property prediction by encoding 3D structures while preserving physical symmetry. However, existing EGNNs struggle to capture fine-grained geometric features needed for tasks like understanding protein interactions, and designing molecules that modulate them are central challenges in computational drug discovery. This thesis develops a progressive framework based on equivariant graph neural networks (EqGNNs) that respects the geometric symmetries of molecular structures. Our contributions progress along two axes: from discriminative to generative and from basic representations to physics-informed, language-conditioned generation. In this study, we propose novel methods for both discriminative (E(Q)AGNN-PPIS, GDEGAN) and generative tasks (PhysFlow). Together, these advance symmetry-aware geometric deep learning for drug discovery.

IJCAI 2026

Disentangling Data Distribution for Optimal and Communication-Efficient Federated Learning

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 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 stable diffusion models to decouple and recover data distributions. Empirical results on the CIFAR100 and DomainNet 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.

IJCAI 2026

Procedural Fairness in Machine Learning

Ziming Wang, Changwu Huang, Ke Tang, Xin Yao

Fairness in machine learning (ML) has garnered significant attention. However, current research has mainly concentrated on the distributive fairness of ML models, with limited focus on another dimension of fairness, i.e., procedural fairness. In this paper, we first define the procedural fairness of ML models by drawing from the established understanding of procedural fairness in philosophy and psychology fields, and then give formal definitions of individual and group procedural fairness. Based on the proposed definition, we further propose a novel metric to evaluate the group procedural fairness of ML models, called GPFFAE, which utilizes a widely used explainable artificial intelligence technique, namely feature attribution explanation (FAE), to capture the decision process of ML models. We validate the effectiveness of GPFFAE on a synthetic dataset and eight real-world datasets. Our experimental studies have revealed the relationship between procedural and distributive fairness of ML models. After validating the proposed metric for assessing the procedural fairness of ML models, we then propose a method for identifying the features that lead to the procedural unfairness of the model and propose two methods to improve procedural fairness based on the identified unfair features. Our experimental results demonstrate that we can accurately identify the features that lead to procedural unfairness in the ML model, and both of our proposed methods can significantly improve procedural fairness while also improving distributive fairness, with a slight sacrifice on the model performance

IJCAI 2026

Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles

Siddharth Mehrotra, Jin Huang, Xuelong Fu, Roel Dobbe, Clara I. Sánchez, Maarten de Rijke

Background: Trustworthy AI serves as a foundational pillar for two major AI ethics conferences: AIES and FAccT. However, current research often adopts techno-centric approaches, focusing primarily on technical attributes such as reliability, robustness, and fairness, while overlooking the sociotechnical dimensions critical to understanding AI trustworthiness in real-world contexts. Objectives: This scoping review aims to examine how the AIES and FAccT communities conceptualize, measure, and validate AI trustworthiness, identifying major gaps and opportunities for advancing a holistic understanding of trustworthy AI systems. Methods: We conduct a scoping review of AIES and FAccT conference proceedings to date, systematically analyzing how trustworthiness is defined, operationalized, and applied across different research domains. Our analysis focuses on conceptualization approaches, measurement methods, verification and validation techniques, application areas, and underlying values. Results: While significant progress has been made in defining technical attributes such as transparency, accountability, and robustness, our findings reveal critical gaps. Current research often predominantly emphasizes technical precision at the expense of social and ethical considerations. The sociotechnical nature of AI systems remains less explored and trustworthiness emerges as a contested concept shaped by those with the power to define it. Conclusions: An interdisciplinary approach combining technical rigor with social, cultural, and institutional considerations is essential for advancing trustworthy AI. We propose actionable measures for the AI ethics community to adopt holistic frameworks that genuinely address the complex interplay between AI systems and society, ultimately promoting responsible technological development that benefits all stakeholders.

IJCAI 2026

Multi-Excitation Projective Simulation with a Many-Body Physics-Inspired Inductive Bias

Philip A. LeMaitre, Marius Krumm, Hans J Briegel

With the impressive progress of deep learning, applications relying on machine learning are increasingly being integrated into daily life. However, most deep learning models have an opaque, oracle-like nature making it difficult to interpret and understand their decisions. This problem led to the development of the field known as eXplainable Artificial Intelligence (XAI). One method in this field known as Projective Simulation (PS) models a chain-of-thought as a random walk of a particle on a graph with vertices that have concepts attached to them. While this description has various benefits, including the possibility of quantization, it cannot be naturally used to model thoughts that combine several concepts simultaneously. To overcome this limitation, we introduce Multi-Excitation Projective Simulation (mePS), a generalization that considers a chain-of-thought to be a random walk of several particles on a hypergraph. A definition for a dynamic hypergraph is put forward to describe the agent's training history along with applications to AI and hypergraph visualization. An inductive bias inspired by the remarkably successful few-body interaction models used in quantum many-body physics is formalized for our classical mePS framework and employed to tackle the exponential complexity associated with naive implementations of hypergraphs. We prove that our inductive bias reduces the complexity from exponential to polynomial, with the exponent representing the cutoff on how many particles can interact. We numerically apply our method to two toy environments and a more complex scenario modelling the diagnosis of a broken computer. These environments demonstrate the resource savings provided by an appropriate choice of inductive bias, as well as showcasing aspects of interpretability. A quantum model for mePS is also briefly outlined and some future directions for it are discussed.

IJCAI 2026

Asymptotically Fair and Truthful Allocation of Public Goods

Pouya Kananian, Arnesh Sujanani, Seyed Majid Zahedi

We study the fair and truthful allocation of m divisible public items among n agents, each with distinct preferences for the items. To aggregate agents' preferences fairly, we focus on finding a core solution. For divisible items, a core solution always exists and can be calculated by maximizing the Nash welfare objective. However, such a solution is easily manipulated; agents might have incentives to misreport their preferences. To mitigate this, the current state-of-the-art finds an approximate core solution with high probability while ensuring approximate truthfulness. However, this approach has two main limitations. First, due to several approximations, the approximation error in the core could grow with n, resulting in a non-asymptotic core solution. This limitation is particularly significant as public-good allocation mechanisms are frequently applied in scenarios involving a large number of agents, such as the allocation of public tax funds for municipal projects. Second, implementing the current approach for practical applications proves to be a highly nontrivial task. To address these limitations, we introduce PPGA, a (differentially) Private Public-Good Allocation algorithm, and show that it attains asymptotic truthfulness and finds an asymptotic core solution with high probability. Additionally, to demonstrate the practical applicability of our algorithm, we implement PPGA and empirically study its properties using municipal participatory budgeting data.

IJCAI 2026

The Value of Real-Time Automated Explanations in Stochastic Planning

Claudia V. Goldman, Ronit Bustin, Wenyuan Qi, Zhengyu Xing, Rachel McPhearson-White, Sally Rogers

Recently, we are witnessing an increase in computation power and memory, leading to strong AI algorithms becoming applicable in areas affecting our daily lives. We focus on AI planning solutions for complex, real-life decision-making problems under uncertainty, such as autonomous driving. Human trust in such AI-based systems is essential for their acceptance and market penetration. Moreover, users need to establish appropriate levels of trust to benefit the most from these systems. Previous studies have motivated this work, showing that users can benefit from receiving (handcrafted) information about the reasoning of a stochastic AI planner, for example, controlling automated driving maneuvers. Our solution to automating these hand-crafted notifications with explainable AI algorithms, XAI, includes studying: (1) what explanations can be generated from an AI planning system, applied to a real-world problem, in real-time? What is that content that can be processed from a planner's reasoning that can help users understand and trust the system controlling a behavior they are experiencing? (2) when can this information be displayed? and (3) how shall we display this information to an end user? The value of these computed XAI notifications has been assessed through an online user study with 800 participants, experiencing simulated automated driving scenarios. Our results show that real time XAI notifications decrease significantly subjective misunderstanding of participants compared to those that received only a dynamic HMI display. Also, our XAI solution significantly increases the level of understanding of participants with prior ADAS experience and of participants that lack such experience but have non-negative prior trust to ADAS features. The level of trust significantly increases when XAI was provided to a more restricted set of the participants, including those over 60 years old, with prior ADAS experience and non-negative prior trust attitude to automated features.

IJCAI 2026

LiSA: Leveraging Link Recommender to Attack Graph Neural Networks via Subgraph Injection (Extended Abstract)

Wenlun Zhang, Enyan Dai, Kentaro Yoshioka

Graph Neural Networks (GNNs) have demonstrated remarkable proficiency in modeling data with graph structures, yet recent research reveals their susceptibility to adversarial attacks. Traditional attack methodologies, which rely on manipulating the original graph or adding links to artificially created nodes, often prove impractical in real-world settings. This paper introduces a novel adversarial scenario involving the injection of an isolated subgraph to deceive both the link recommender and the node classifier within a GNN system. Specifically, the link recommender is mislead to propose links between targeted victim nodes and the subgraph, encouraging users to unintentionally establish connections and that would degrade the node classification accuracy, thereby facilitating a successful attack. To address this, we present the LiSA framework, which employs a dual surrogate model and bi-level optimization to simultaneously meet two adversarial objectives. Extensive experiments on real-world datasets demonstrate the effectiveness of our method.

IJCAI 2026

PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN (Extended Abstract)

Jiayu Zhang, Zhiyu Zhu, Xinyi Wang, Silin Liao, Zhibo Jin, Flora Salim, Huaming Chen

Deep neural networks are vulnerable to adversarial examples, posing serious security risks. While GAN-based attacks such as AdvGAN enable fast adversarial example generation, they typically operate in a single step, limiting attack effectiveness and transferability. In this work, we propose PAR-AdvGAN, a progressive auto-regressive GAN framework that iteratively refines adversarial perturbations. By conditioning each iteration on the previous output, PAR-AdvGAN produces stronger, more transferable attacks while maintaining low visual distortion and high generation speed. Experiments on multiple models and datasets demonstrate that PAR-AdvGAN significantly outperforms baseline methods in attack success rate, achieving up to 335.5 frames per second, highlighting its practical efficiency for black-box attack scenarios.

IJCAI 2026

Improving Group Robustness on Spurious Correlation via Evidential Alignment (Extended Abstract)

Wenqian Ye, Guangtao Zheng, Aidong Zhang

Deep neural networks often rely on spurious correlations, i.e., superficial associations between non-causal features and prediction targets. While yielding high overall accuracy during training, such reliance degrades generalization on minority groups where these correlations break down. Existing methods mitigate this issue by using external group annotations or auxiliary deterministic models, but group annotations are costly to obtain and deterministic auxiliaries may fail to capture the full spectrum of biases learned by the model. We propose Evidential Alignment, a framework that leverages uncertainty quantification to identify and suppress spurious correlations without requiring group annotations. By transforming a biased ERM model from first-order to second-order risk minimization with a Dirichlet distribution and applying an evidential calibration step that reweighs samples by their epistemic uncertainty, Evidential Alignment debiases the model while preserving core features. We provide theoretical guarantees and demonstrate strong worst-group accuracy across diverse architectures and modalities.

IJCAI 2026

CollabLLM: From Passive Responders to Active Collaborators (Extended Abstract)

Shirley Wu, Michel Galley, Baolin Peng, Hao Cheng, Gavin Li, Yao Dou, Weixin Cai, James Zou, Jure Leskovec, Jianfeng Gao

Large Language Models are typically trained with next-turn rewards, limiting their ability to optimize for long-term interaction. As a result, they often respond passively to ambiguous or open-ended user requests, failing to help users reach their ultimate intents and leading to inefficient conversations. To address these limitations, we introduce CollabLLM, a novel and general training framework that enhances multiturn human-LLM collaboration. Its key innovation is a collaborative simulation that estimates the long-term contribution of responses using Multiturn-aware Rewards. By reinforcement fine-tuning these rewards, CollabLLM goes beyond responding to user requests and actively uncovers user intent and offers insightful suggestions--a key step toward more human-centered AI. We also devise a multiturn interaction benchmark with three challenging tasks such as document creation. COLLABLLM significantly outperforms our baselines with averages of 18.5% higher task performance and 44.3% improved interactivity by LLM judges. Finally, we conduct a large user study with 201 judges, where CollabLLM increases user satisfaction by 17.6% and reduces user spent time by 10.4%.

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