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IJCAI 2026

A Theory of Response Sampling in LLMs: Part Descriptive and Part Prescriptive (Extended Abstract)

Sarath Sivaprasad, Pramod Kaushik, Sahar Abdelnabi, Mario Fritz

Large Language Models (LLMs) are often used in autonomous decision-making, where they have to sample options from vast action spaces. Here we present a summary of the work studying the heuristics that guide this sampling process and show it resembles that of human decision-making: comprising a descriptive component (reflecting statistical norm) and a prescriptive component (implicit ideal encoded in the LLM). We show that the deviation of a sample from the statistical norm towards a prescriptive component consistently appears in concepts across diverse real-world domains. To further illustrate the theory, we demonstrate that concept prototypes in LLMs are affected by prescriptive norms, similar to the concept of normality in humans. This finding has implications in LLM based agentic systems, explaining their exploration behavior and biases in decision making.

IJCAI 2026

Beyond Top-1: Addressing Inconsistencies in Evaluating Counterfactual Explanations for Recommender Systems (Extended Abstract)

Amir Reza Mohammadi, Andreas Peintner, Michael M. Müller, Eva Zangerle

Counterfactual explanations have become an important paradigm for improving the transparency of machine learning models by showing how small input changes can alter model outputs. While substantial progress has been made in generating such explanations, their evaluation remains insufficiently standardized, particularly for systems that produce ranked outputs rather than single-label predictions. Existing evaluation protocols in recommender systems commonly focus only on whether the top-1 recommendation changes after perturbation. We argue that this practice can lead to inconsistent and misleading conclusions, as the relative ranking of explanation methods may vary with changes in the quality of the underlying model. In this work, we revisit the evaluation of counterfactual explanations from a ranking perspective. We propose extending top-1 evaluation to list-wise top-k protocols that assess explanation effectiveness across multiple highly ranked outputs. Through experiments on multiple datasets, recommender architectures, and explanation methods, we show that top-k evaluation substantially improves consistency and yields more reliable comparisons between competing explainers. Our findings highlight a broader methodological lesson for explainable AI: when models return ranked results, explanation quality should be assessed using ranking-aware evaluation protocols rather than top-1 criteria alone.

IJCAI 2026

Soft Condorcet Optimization for Ranking of General Agents (Extended Abstract)

Marc Lanctot, Kate Larson, Michael Kaisers, Quentin Berthet, Ian Gemp, Manfred Diaz, Roberto-Rafael Maura-Rivero, Yoram Bachrach, Anna Koop, Doina Precup

Driving progress of AI models and agents requires comparing their performance on standardized benchmarks; for general agents, individual performances must be aggregated across a potentially wide variety of different tasks. In this extended abstract, we describe a ranking scheme inspired by social choice frameworks, called Soft Condorcet Optimization (SCO), to compute the optimal ranking of agents: the one that makes the fewest mistakes in predicting the agent comparisons in the evaluation data. This optimal ranking is the maximum likelihood estimate when evaluation data (which we view as votes) are interpreted as noisy samples from a ground truth ranking, a solution to Condorcet's original voting system criteria. SCO ratings are maximal for Condorcet winners when they exist, which we show is not necessarily true for the classical rating system Elo. In practice, SCO serves as an accurate approximation to the Kemeny-Young voting method, excels in the sparse data regime, and provides the best approximation to the optimal ranking compared to every baseline on a Diplomacy player ranking problem with 31,094 games and 52,958 agents.

IJCAI 2026

Every Bit Helps: Achieving the Optimal Distortion with a Few Queries (Extended Abstract)

Soroush Ebadian, Nisarg Shah

A fundamental task in multi-agent systems is to match n agents to n alternatives (e.g., resources or tasks). This is often done by eliciting agents' ordinal rankings over the alternatives rather than their exact numerical utilities. While this simplifies elicitation, the incomplete information leads to inefficiency, captured by a worst-case measure called distortion. Recent work shows that making just a few cardinal utility queries per agent can significantly improve the distortion, and in particular that O(√n) distortion is achievable with two queries per agent. We generalize this result by achieving O(n^(1/λ)) distortion with λ queries per agent, for any constant λ, which is optimal up to a constant factor given a previously known lower bound. We extend this finding to the general social choice problem of selecting one of m alternatives based on n agents' preferences, achieving O(min(n, m)^(1/λ)) distortion with λ queries per agent.

IJCAI 2026

You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control (Extended Abstract)

Giovanni De Toni, Erasmo Purificato, Emilia Gómez, Andrea Passerini, Bruno Lepri, Cristian Consonni

Recommenders are significantly shaping online information consumption. While effective at personalizing content, these systems increasingly face criticism for propagating irrelevant, unwanted, and even harmful recommendations. Such content degrades user satisfaction and contributes to significant societal issues, including misinformation, radicalization, and erosion of user trust. Although platforms offer mechanisms to mitigate exposure to undesired content, these mechanisms are often insufficiently effective and slow to adapt to users' feedback. This paper introduces an intuitive, model-agnostic, and distribution-free method that uses conformal risk control to provably bound unwanted content in personalized recommendations by leveraging simple binary feedback on items. We also address a limitation of traditional conformal risk control approaches, i.e., the fact that the recommender can provide a smaller set of recommended items, by leveraging implicit feedback on consumed items to expand the recommendation set while ensuring robust risk mitigation. Our experimental evaluation on data coming from a popular online video-sharing platform demonstrates that our approach ensures an effective and controllable reduction of unwanted recommendations with minimal effort.

IJCAI 2026

Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes (Extended Abstract)

Jaewan Chun, Seokbum Yoon, Minyoung Choe, Geon Lee, Kijung Shin

In many real-world scenarios, interactions happen in a group-wise manner with multiple entities, and therefore, hypergraphs are a suitable tool to accurately represent such interactions. Various generative models have been proposed to explore fundamental mechanisms underlying hyperedge formation. However, most existing models do not account for node attributes, which can play a significant role in hyperedge formation. As a result, they fail to capture the interactions between structure and node attributes. To address this issue, we propose NoAH, a stochastic hypergraph generative model for attributed hypergraphs. NoAH utilizes the core–fringe node hierarchy to model hyperedge formation as a series of node attachments and determines attachment probabilities based on node attributes. We further introduce NoAHFit, a parameter learning procedure for fitting NoAH to a given real-world hypergraph. Through experiments, we show that NoAH with NoAHFit more accurately reproduces the structure–attribute interplay than baseline hypergraph generative models. Our code, supplementary materials, and datasets are provided at https://github.com/jaewan01/NoAH.

IJCAI 2026

Finite Axiomatizability by Disjunctive Existential Rules (Extended Abstract)

Marco Calautti, Marco Console, Andreas Pieris

In this short paper, we discuss a model-theoretic characterization of when a collection of relational structures is axiomatizable via a finite set of disjunctive existential rules. The presented characterization uses the property of criticality, a refined version of closure under direct products, and a new property that relies on the method of diagrams.

IJCAI 2026

What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness (Extended Abstract)

Yang Cai, Alkis Kalavasis, Katerina Mamali, Anay Mehrotra, Manolis Zampetakis

Most of the widely used estimators of the average treatment effect (ATE) in causal inference rely on the assumptions of unconfoundedness and overlap. Unconfoundedness requires that the observed covariates account for all correlations between the outcome and treatment. Overlap requires the existence of randomness in treatment decisions for all individuals. Nevertheless, many types of studies frequently violate unconfoundedness or overlap; for instance, observational studies with deterministic treatment decisions, popularly known as Regression Discontinuity designs, violate overlap. In this paper, we initiate the study of general conditions that enable the identification of the average treatment effect, extending beyond unconfoundedness and overlap. In particular, following the paradigm of statistical learning theory, we provide an interpretable condition that is sufficient and necessary for the identification of ATE. Moreover, this condition can be used to characterize other treatment effects, such as the average treatment effect on the treated (ATT), as well. To illustrate the utility of our condition, we present several well-studied scenarios where our condition is satisfied and, hence, we prove that ATE can be identified in regimes that prior works could not capture. For example, under mild assumptions on the data distributions, this holds for the models proposed by Tan (2006) and Rosenbaum (2002), and the Regression Discontinuity design model introduced by Thistlethwaite and Campbell (1960). For each of these scenarios, we also show that, under natural additional assumptions, ATE can be estimated from finite samples. We believe these findings open new avenues for bridging learning-theoretic insights and causal inference methodologies, particularly in observational studies with complex treatment mechanisms.

IJCAI 2026

FAIRGAME: A Framework for AI Agents Bias Recognition Using Game Theory (Extended abstract)

Alessio Buscemi, Daniele Proverbio, Alessandro Di Stefano, The Anh Han, German Castignani, Pietro Lio

Letting AI agents interact in multi-agent settings introduces significant complexity in predicting and interpreting their collective behavior, with profound implications for trustworthy AI adoption in research and society. We present FAIRGAME (Framework for AI Agents Bias Recognition using Game Theory), an open-source framework that simulates game-theoretic scenarios with LLM-based agents to systematically uncover biases arising from model choice, language, agent personality, and more. Applied to the Prisoner’s Dilemma and Battle of the Sexes across four LLMs and five human languages, FAIRGAME reveals inconsistencies across LLM models and consistent deviations from game-theoretic predictions; it also quantifies LLM-specific behavioral tendencies through a novel scoring system. Our results show that LLMs draw on prior world knowledge beyond payoff matrices, and that language and personality significantly shape strategic outcomes, supporting the use of reproducible and controlled simulation pipelines to predict the interacting behavior of LLM agents.

IJCAI 2026

Analysing Temporal Reasoning in Description Logics Using Formal Grammars (IJCAI Extended Abstract)

Camille Bourgaux, Anton Gnatenko, Michaël Thomazo

We establish a correspondence between (fragments of) TEL^o, a temporal extension of the EL description logic with the LTL operator O^k, and some specific kinds of formal grammars, in particular, conjunctive grammars (context-free grammars equipped with the operation of intersection). This connection implies that TEL^o does not possess the property of ultimate periodicity of models, and further leads to undecidability of query answering in TEL^o, closing a question left open since the introduction of TEL^o. Moreover, it also allows to establish decidability of query answering for some new interesting fragments of TEL^o, and to reuse for this purpose existing tools and algorithms for conjunctive grammars.

IJCAI 2026

Modeling and Explaining an Industrial Workforce Allocation Problem (Extended Abstract)

Ignace Bleukx, Ryma Boumazouza, Tias Guns, Nadine Laage, Guillaume Poveda

We present an industrial case on workforce allocation and scheduling in the aircraft manufacturing industry, where available teams need to be assigned to logistical operations. This application presents several challenges, such as the scale of the problem, the need for fair workload distribution, and the need for methods to mitigate unforeseen disruptions due to technical malfunctions or incompatible weather conditions. We compare different Constraint Programming (CP) models for the allocation and scheduling problems, with extra focus on modeling the workload balancing component. Additionally, we investigate automatic rescheduling methods for restoring feasibility after a disruption invalidates the precomputed schedule. Our results show that by using appropriate modeling techniques, the problem can be solved in reasonable time, thereby producing fair schedules. Additionally, we show how invalidated schedules can be restored efficiently to help human operators in resolving disruptions to the schedule.

IJCAI 2026

Revelations: A Decidable Class of POMDPs with Omega-Regular Objectives (Extended Abstract)

Marius Belly, Nathanaël Fijalkow, Hugo Gimbert, Florian Horn, Guillermo A. Pérez, Pierre Vandenhove

Partially observable Markov decision processes (POMDPs) form a prominent model for uncertainty in sequential decision making. We are interested in constructing algorithms with theoretical guarantees to determine whether the agent has a strategy ensuring a given specification with probability 1. This well-studied problem is known to be undecidable already for very simple omega-regular objectives, because of the difficulty of reasoning on uncertain events. We introduce a revelation mechanism which restricts information loss by requiring that, almost surely, the agent has eventually full information about the current state. Our main technical results are to construct exact algorithms for two classes of POMDPs called weakly and strongly revealing. Importantly, the decidable cases reduce to the analysis of a finite belief-support Markov decision process. This yields a conceptually simple and exact algorithm for a large class of POMDPs.

IJCAI 2026

Parallelizing Multi-Objective A* Search (Extended Abstract)

Saman Ahmadi, Nathan R. Sturtevant, Andrea Raith, Daniel Harabor, Mahdi Jalili

The Multi-objective Shortest Path (MOSP) problem is a classic network optimization problem that aims to find all Pareto-optimal paths between two points in a graph with multiple edge costs. Recent studies on multi-objective search with A* (MOA*) have demonstrated strong performance on challenging MOSP instances. This paper presents a novel search framework that enables efficient parallelization of MOA* through different objective orderings and a unique upper-bounding strategy that, in certain cases, allows the problem dimensionality to be reduced to one. Results demonstrate that the proposed framework can significantly improve the performance of MOA*, with speedups increasing proportionally to the number of objectives.

IJCAI 2026

Toward Reliable Agents

Hua Wei

Learned agents that control traffic signals or call software tools are usually trained in simulators or fixed benchmarks, yet must act in worlds that differ. Many reliability concerns (robustness, safety, alignment, etc.) become one problem once agents are written as Markov decision processes (MDPs): a gap between the world an agent was built for and the world it acts in. The sim-to-real gap decomposes along state, observation, action, transition, and reward; multi-agent deployment further adds topology, population, and task gaps as neighbors learn and workloads scale. Using reinforcement learning (RL) for traffic control and large language model (LLM) agents for tool use and mobile GUIs as running examples, this paper traces how mismatches appear on each channel and how mitigations from one community (domain randomization, grounded action transformation, perturbation benchmarks) transfer to the other. Because the gap rarely closes, reliable deployment needs selective human oversight; uncertainty quantification can flag when to escalate, localize errors at the answer, reasoning, step, or agent level, and support sim-to-real transfer in RL. This paper opens an agenda for a shared MDP vocabulary, uncertainty-aware oversight, and scaling human-AI collaboration across volume, complexity, and expertise.

IJCAI 2026

Toward Data-Efficient Intelligence: From Few-Shot Learning to Agentic Systems

Yaqing Wang

Modern artificial intelligence has made rapid progress by scaling data, models, and computation. Yet scale alone does not solve one basic problem. Many intelligent systems must learn, adapt, and act when direct experience is scarce, costly, noisy, or changing. A scientist may have only a few labeled molecules. A recommender system may see only sparse interactions for a new user or item. A language-model agent may need to align with a user's preference from a short interaction history. These settings differ, but they share the same question: how can AI extract more learning signal from less experience? My research studies this question through data-efficient generalization. This article summarizes a research path from few-shot learning to meta-learning, in-context learning, and data-efficient agentic systems. The central theme is that limited supervision can be made useful by the right priors, the right adaptation mechanism, and the right way to reuse experience. I also discuss two regimes where data efficiency is not only desirable but necessary: scientific scarcity and efficient data utilization.

IJCAI 2026

Towards Reasonable AI: Foundations for Abstraction and Generalized Reasoning

Zeynep G. Saribatur

Human reasoning relies on abstraction and generalization, in order to make decisions flexible under changing conditions while ignoring irrelevant details and focusing on the essence. Developing AI systems with such abilities, while ensuring transparency and explainability on the reasoning behind the made decision, remains a central challenge. Symbolic AI provides transparent knowledge representations and formal reasoning guarantees, yet lacks principled mechanisms for abstracting away irrelevant details while preserving the information required for reasoning, explainability, and generalization. This paper presents an overview of my research towards addressing this challenge by developing formal and computational methods for abstraction in Answer Set Programming, one of the core formalisms in symbolic AI, and related logic-based frameworks. The contributions span from foundational abstraction techniques that simplify reasoning representations while preserving essential solution properties, to investigations on how such abstractions can both improve computational reasoning and support human understanding of AI decision processes.

IJCAI 2026

Human-Centred Trustworthy AI for Digital Health: Sensing, Understanding, and Empowerment

Zhao Ren

The rapid advancement of Artificial Intelligence (AI) has accelerated the development of personalised healthcare. However, the clinical adoption of deep learning remains constrained by a persistent “trust gap” surrounding model transparency, security, and data privacy. This talk presents a research vision for building trustworthy human-centred AI in computer audition and biosignal processing by integrating signal processing, machine learning, and healthcare. This vision is structured around three interconnected pillars: Sensing, Understanding, and Empowerment. The Sensing pillar focuses on enabling machines to perceive clinically relevant information from biosignals, transforming body sounds and physiological signals into non-invasive, accessible, and cost-effective windows into human health and well-being. The Understanding pillar addresses the black-box nature of modern AI systems, with the goal of ensuring transparency, efficiency, robustness, and security in clinical deployment. This is achieved through the development of explainable AI methods, knowledge distillation techniques, and defences against adversarial attacks, fostering AI systems that clinicians can trust and interpret. The Empowerment pillar seeks to restore natural communication for individuals with speech impairments, such as laryngectomy patients. By developing Silent Speech Interfaces (SSIs) that translate facial muscle activity (EMG) directly into audible speech, this research advances speech intelligibility, naturalness, real-time causal architectures, and multi-speaker speech synthesis. Collectively, these research directions aim to establish a foundation of trustworthy, human-centred AI that is not only accurate and secure, but also empathetic, accessible, and impactful, ultimately enhancing healthcare, communication, and quality of life.

IJCAI 2026

Towards Streamlined Learning and Search for Multi-Agent Optimization

Thomy Phan

Many real-world problems can be modeled as cooperative multi-agent systems (MAS), such as fleet management, industrial operations, and communication networks, where multiple agents collaborate to optimize a shared objective. Optimizing cooperative MAS is difficult due to the combinatorial nature of joint actions and environmental factors. Thus, many practical multi-agent optimization approaches specialize in particular problem classes to exploit structural properties for effective and efficient optimization. Unfortunately, such specializations can lead to complex and inflexible methods that cannot be seamlessly combined or transferred to novel domains without substantial engineering effort. In this paper, we advocate an approach towards streamlined learning and search for multi-agent optimization. Focusing on multi-agent path finding as an exemplary problem, we propose to simplify two popular approaches to MAPF, namely multi-agent reinforcement learning and adaptive search. Through these simplifications, we aim to enable seamless combination and transferability of our methods without substantial engineering.

IJCAI 2026

TrafficPDE: A Guidebook to Deployable AI-Driven Transportation Systems Through the Perception-Decision-Explanation Triangle

Ziyue Li

Deploying AI in real-world intelligent transportation systems (ITS) remains hard: traffic data is noisy and incomplete, learned policies rarely transfer to new cities, and black-box models cannot earn the trust of operators and public officials. Drawing on years of joint experience in ITS industry and academia (including traffic signal control systems deployed across multiple cities and AI for one of the world's busiest metro networks), this paper presents TrafficPDE: a practitioner's guidebook organized around the Perception-Decision-Explanation (PDE) triangle. Perception tackles how to model traffic data reliably despite noise, missing sensors, and unobserved locations. Decision develops generalizable reinforcement learning agents that transfer across cities without costly re-calibration. Explanation builds causal DAG frameworks that make AI decisions interpretable to transportation practitioners. We show the three vertices reinforce each other, and close with three golden rules for any deployable ITS model: handle long-tail cases safely, operate from day one, and explain itself to people with a transportation background. TrafficPDE is not a survey of what has been done. It is a guidebook for what must be built.

IJCAI 2026

What If. . . Counterfactual Explanations Were to Be Deployed?

Francesco Leofante

Explainable AI is now a mature and rapidly expanding field, with a wide range of methods for interpreting models and explaining their behaviour. Despite this progress, many of these methods have yet to make their way into the engineering pipelines where AI is actually deployed. This gap suggests a question that is central to this paper: what would it take for explanations to be informative, useful and dependable when deployed? I approach this question through counterfactual explanations, which I view as a promising foundation for practical XAI: by showing what would need to change for an AI decision to differ, they are easy to interpret and carry actionable information. In their standard form, however, counterfactuals come with limitations of form and scope that constrain their applicability in deployment. I therefore consider three lines of work that build on standard counterfactual explanations and ask what would need to change for them to be better aligned with deployment requirements. The first concerns robustness, ensuring that counterfactuals remain valid under the perturbations that deployment introduces. The second extends them beyond one-shot decisions to capture sequential decision-making. The third takes counterfactuals as a starting point for contestability, arguing that meaningful contestation requires more than an explanation alone. I close by reflecting on energy systems as a critical testbed for explainability, one that highlights the promises and limitations of current methods and may offer a source of criteria for the next generation of explanation methods.

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