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IJCAI 2026

Uniform Interpolation Closure for Branching Time Temporal Logics

Renyan Feng, Yisong Wang, Mingsen Deng, Erman Acar

Computation Tree Logic (CTL) is a fundamental formalism for specifying and reasoning about the behavior of non-terminating systems. Due to the lack of uniform interpolation (UI) property, CTL is usually limited in modular reasoning and system abstraction. This paper studies uniform interpolation in CTL fragments with restricted temporal operators from the point of knowledge forgetting, aiming to identify the minimal logic extensions of these fragments that preserve UI property (referred to as their uniform interpolation closure). Our results demonstrate that: (1) CTL(X), the fragment of CTL allowing only the “neXt” operator, enjoys the UI property; (2) the UI closure of the fragment CTL(F<, X) is the bisimulation-invariant fragment of quantified CTL in prenex-normal-form, where F< is the operator “next Future”; (3) CTL(F<) fails to possess Craig interpolation, and every extension of CTL(F<) that possesses the Craig interpolation property is necessarily an extension of CTL(U) which contains only the “Until” operator. These findings provide a precise characterization of the expressive power required to achieve modularity in CTL.

IJCAI 2026

When Incompleteness Does Not Matter: The Case of Incomplete Abstract Argumentation Framework (Under the Possible Perspective)

Bettina Fazzinga, Sergio Flesca, Filippo Furfaro

Incomplete Abstract Argumentation Frameworks (iAAFs) extend Abstract Argumentation Frameworks (AAFs) by allowing arguments and attacks to be specified as uncertain, enabling a compact representation of alternative argumentation scenarios. Despite extensive work on reasoning with iAAFs, their expressive power relative to standard AAFs is still unclear. We address this question by studying whether the reasoning based on possible-extensions in iAAFs can be reduced to classical reasoning on AAFs. Our analysis relates iAAFs and AAFs via two comparison notions: equivalence, where an AAF yields as extensions exactly the possible extensions of an iAAF, and projection-equivalence, where such extensions are obtained by projecting away auxiliary arguments. We characterize the semantics under which these relationships hold, and, in these cases, provide constructive transformations from iAAFs to AAFs, thus also enabling classical argumentation tools to be applied to qualitative-uncertainty reasoning.

IJCAI 2026

Neural Decision-Propagation for Answer Set Programming

Thomas Eiter, Katsumi Inoue, Sota Moriyama

Integration of Answer Set Programming (ASP) with neural networks has emerged as a promising tool in Neuro-symbolic AI. While existing approaches extend the capabilities of ASP to real world domains, their reasoning pipelines depend on classical solvers, which is a bottleneck for scalability. To tackle this problem, we propose a new method to compute stable models, called decision-propagation (DProp), which alternates falsity decisions and truth propagations. Successful DProp computations are shown to capture the stable model semantics. We then develop Neural DProp (NDProp), a differentiable extension of DProp with neural computation for decisions and fuzzy evaluation for propagations. We evaluate the capabilities of NDProp for learning decision heuristics as well as neuro-symbolic integration, and compare it with existing neuro-symbolic approaches. The results show that NDProp can learn to efficiently compute stable models, and it improves accuracy and scalability on neuro-symbolic benchmarks.

IJCAI 2026

Optimality-preserving Logic-Based Benders Decomposition of Answer Set Programs

Carmine Dodaro, Antonio Ielo, Marco Maratea, Cinzia Marte, Alice Tarzariol

Bender Decomposition is a well-known solving technique in Operation Research that decomposes a problem into a master and a subproblem, which interact via “cuts". This technique has been extended to Logic-Based Bender Decomposition (LBBD), solving problems specified by logic-based languages and enabling a wider applicability. However, while Bender Decomposition guarantees optimality, LBBD does not: this property is problem-specific and depends on the defined decomposition and cuts. In this paper, we present a theoretical analysis of the conditions under which LBBD, including cuts, can preserve optimality in the context of Answer Set Programming (ASP), a prominent logic-based language in the field of Artificial Intelligence. We also introduce a general-purpose algorithm that employs both minimal unsatisfiable subsets and minimal correction subsets to define cuts in a fully automated, problem-independent way. The algorithm preserves the optimality guarantees of Bender Decomposition. An empirical evaluation on real-world scheduling instances shows that our approach can find significantly more solutions, and more optimal ones, compared to a standard direct ASP encoding, while also consistently reducing the execution time.

IJCAI 2026

On the Size Complexity and Decidability of First-Order Progression

Jens Claßen, Daxin Liu

Progression, the task of updating a knowledge base to reflect action effects, generally requires second-order logic. Identifying first-order special cases, by restricting either the knowledge base or action effects, has long been a central topic in reasoning about actions. It is known that local-effect, normal, and acyclic actions, three increasingly expressive classes, admit first-order progression. However, a systematic analysis of the size of such progressions, crucial for practical applications, has been missing. In this paper, using the framework of Situation Calculus, we show that under reasonable assumptions, first-order progression for these action classes grows only polynomially. Moreover, we show that when the KB belongs to decidable fragments such as two-variable first-order logic or universal theories with constants, the progression remains within the same fragment, ensuring decidability and practical applicability.

IJCAI 2026

StressEval: Failure-Driven Dynamic Benchmarking for Knowledge-Intensive Reasoning in Large Language Models

Yongrui Chen, Yangyang Ma, Xiaoying Huang, Shenyu Zhang, Huajun Chen, Haofen Wang, Guilin Qi

Static benchmarks for LLMs are increasingly compromised by contamination and overfitting, especially on knowledge-intensive reasoning tasks. While recent dynamic benchmarks can alleviate staleness, they often increase difficulty at the expense of answerability and controllability. In this paper, we propose StressEval, a failure-driven data synthesis framework that turns observed model failures into dynamic, challenging, and controlable test instances. StressEval consists of three stages: (i) it constructs a semi-structured difficulty card that identifies the failed reasoning step and its root cause; (ii) it applies a dual-perspective instance-synthesis method that targets both knowledge gaps and reasoning breakdowns while preserving the underlying difficulty factors; and (iii) it applies a gating mechanism to retain only grounded, unambiguous instances. Seeding from multiple knowledge-intensive reasoning datasets, we employ StressEval to build Dynamic-OneEval, a focused suite of challenging dynamic benchmark. Across several state-of-the-art LLMs, Dynamic-OneEval yields substantially larger performance drops than the original benchmarks while retaining explicit difficulty factors, enabling more actionable iteration.

IJCAI 2026

LLMs as Parametric Knowledge Sources for Knowledge Graph Completion

Deyu Chen, Qiyuan Li, Jinguang Gu, Meiyi Xie, Hong Zhu

Knowledge graphs (KGs) serve as crucial symbolic knowledge sources for many downstream applications but often suffer from incompleteness. Recent efforts have attempted to integrate pre-trained language models and KGs for the knowledge graph completion (KGC) task. However, existing methods introduce substantial model complexity or rely on prompt-based solutions that inject priors at a shallow level, which do not exploit the factual knowledge inherently encoded in model parameters. To address these issues, we propose a novel framework that treats LLMs as parametric knowledge sources for KGC. Our framework performs LLM knowledge elicitation to extract factual knowledge from the model’s internal representations. Then, a cross-granularity representation alignment method transforms sentence-level representations into entity-level representations and aligns them within a unified space. Finally, a dynamic learning schedule balances alignment and expressiveness throughout training. Extensive experiments on multiple benchmark datasets show that our proposed method can be integrated with diverse KGC baselines and consistently improves link prediction in both standard and lifelong settings.

IJCAI 2026

Efficient Minimization of Decision-DNNF Circuits via Semantic Hashing and Provenance Tracking

Armin Biere, Jean-Marie Lagniez, Emmanuel Lonca

Knowledge Compilation transforms propositional formulas into tractable structures like decision-DNNF to support efficient reasoning. However, these representations often suffer from exponential size, and standard minimization via SAT sweeping is computationally prohibitive for large instances. In this paper, we propose a scalable minimization framework for decision-DNNF that eliminates the need for SAT solvers. We introduce a semantic hashing technique leveraging polynomial-time model counting to rapidly filter redundancies, followed by a polynomial-time verification strategy based on CNF projection. Our experimental evaluation demonstrates that this approach efficiently compresses decision-DNNF circuits while avoiding the bottleneck of NP-hard equivalence checks.

IJCAI 2026

ASP-Based Probabilistic Policy Fixing for Norm Compliant RL

Sebastian Adam, Thomas Eiter

Reinforcement learning (RL) is commonly used to learn reward-optimizing policies. However, RL policies are not always trained with ethical behavior in mind, which can lead an agent to violate social or legal norms in pursuit of its goal. Retraining agents with additional norms is not always feasible, especially in complex stochastic environments. To mitigate this issue, we present a probabilistic policy fixing framework that adapts norm-agnostic policies online. Using Answer Set Programming (ASP), we generate policy fixes that minimize deviations from the RL policy while optimizing for norm adherence against a set of sampled worlds. Based on the Rule of Three and Hoeffding's inequality, we provide guarantees that fixed policies are near optimal, given a specified level of confidence.

IJCAI 2026

When and How to Adapt: Subject Shifts Detection and Prototype-Guided Correction for Online EEG Decoding

Shaoqi Zhang, Xiyuan Jin, Xiaojun Ning, Yilin Chen, Jing Wang

Online EEG decoding is pivotal for real-world Brain-Computer Interfaces (BCIs) but confronts significant challenges arising from continuous distribution shifts, including both inter- and intra-subject variations. Existing Unsupervised Continual Domain Adaptation (UCDA) methods typically rely on rigid hard boundaries such as subject switch labels or fixed batch sizes to trigger adaptation. Meanwhile, some online EEG decoding methods that utilize detected shifts as soft boundaries are ill-suited for unsupervised scenarios and lack effective distribution alignment strategies. To address these issues, we propose a novel Prototype-driven online EEG Decoding framework (PRED). PRED incorporates a Subject Shift Detector (SSD) based on policy stability to reliably identify latent domain shifts unsupervisedly, thereby constructing adaptive soft boundaries. Furthermore, we design a Prototype-guided Shift Correction (PSC) mechanism that leverages a multi-granular memory structure to guide distribution alignment while preserving semantic stability. Experiments on three public datasets confirm that PRED achieves superior plasticity and stability, and demonstrates significant futurity, i.e., the capability of forward transfer to a subject's future samples.

IJCAI 2026

TriHAI: A Combination Enhanced Tri-Mode Deferral for Human-AI Team

Minhui Zhang, Xuehan Zhao, Xin Zhang, Jiaqi Liu, Zhiwen Yu, Bin Guo

Learning to Defer operates by deferring AI-uncertain samples to humans, enabling the system to outperform either alone. Some works extend it by introducing human-AI combination to deferral. However, they remain limited to binary choices. Given that AI, humans, and human-AI combination are each indispensable, we extend the binary deferral paradigm to a ternary one. There are two challenges: i) how to effectively select among the three modes, especially distinguishing humans from combination? ii) how to design an enhanced combination without being degraded by unreliable model predictions? To address the challenges, we propose TriHAI, a tri-mode deferral method that routes samples to suitable modes based on normalized confidence scores obtained through a discriminative gating network, and enhances combination mode by fusing model predictions refined by human-guided Conformal Prediction and human predictions via Bayesian theory. Experiments on three datasets show that TriHAI surpasses other human-AI baselines by up to 9.57% in accuracy.

IJCAI 2026

EyeCue: Driver Cognitive Distraction Detection via Gaze-Empowered Egocentric Video Understanding

Lang Zhang, JinYi Yoon, Matthew Corbett, Abhijit Sarkar, Bo Ji

Driver cognitive distraction is a major cause of road collisions and remains difficult to detect. Unlike manual or visual distraction, cognitive distraction is diverted by thoughts unrelated to driving, even when the driver appears visually attentive and exhibits no explicit physical movements. In this work, we propose EyeCue, a gaze-empowered egocentric video understanding framework, to detect driver cognitive distraction. A key insight is that cognitive distraction manifests in the interaction between eye gaze and visual context. To capture this interaction, EyeCue integrates eye gaze with egocentric video to enable context-aware modeling of the driver's attention over time. Furthermore, to tackle the limited scale and diversity of existing datasets, we introduce CogDrive, a comprehensive multi-scenario dataset that augments four existing driving datasets with cognitive distraction annotations. Through extensive evaluations on CogDrive, we show that EyeCue achieves the highest accuracy of 74.38%, outperforming 11 baselines from 6 model families by over 7%. Notably, EyeCue can achieve an accuracy of over 70% across various driving scenarios (different road types, times of day, and weather conditions) with strong generalizability. These results highlight the importance of modeling gaze-context interactions and the effectiveness of cross-modal interaction modeling for multimodal cognitive distraction detection. Our codes and CogDrive dataset resources are available at https://github.com/langzhang2000/EyeCue.

IJCAI 2026

Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing

Peilin Zhan, Wei Chen, Weilin Chen, Shuyi Pan, Ruichu Cai

Knowledge Tracing (KT) is fundamental to intelligent education systems, yet relies on educational logs that are selectively observed. The non-random nature of exercise recommendations and student choices inevitably induces severe selection bias. Most existing KT methods neglect this issue, training on observed logs using standard empirical risk, which yields biased mastery estimates and accumulates errors in subsequent recommendations. To address this, we introduce a doubly robust (DR) formulation for KT that integrates a propensity model with an error imputation model, theoretically guaranteeing unbiasedness if either model is accurate. Beyond unbiasedness, in the sequential setting of KT, we identify that the estimator's performance is compromised by variance-dependent stochastic deviations that accumulate over time, thereby causing training instability and limiting performance. To mitigate this, we derive a generalization bound that explicitly characterizes the impact of estimator variance and identifies temporal smoothness as a key factor in controlling it. Building on these theoretical insights, we propose the Temporal Smoothness Doubly Robust (TSDR) framework. TSDR jointly optimizes the KT predictor and the imputation model with a smoothness regularizer, effectively reducing variance while preserving the unbiasedness guarantee of DR. Experiments on multiple real-world benchmarks demonstrate that TSDR consistently enhances various state-of-the-art KT backbones, underscoring the vital role of principled bias correction in KT.

IJCAI 2026

An Emotion-Preserving Conditional Information Bottleneck for Domain-Generalizable Speech Emotion Recognition

Zhichen Yuan, C. L. Philip Chen, Shuzhen Li, Tong Zhang

Domain-generalizable speech emotion recognition (DG-SER) aims to ensure the robustness of SER models across unknown domains, which is essential for real-world human-machine interaction systems. Most DG-SER approaches employ alignment or adversarial strategies with domain labels to promote generalization. However, these strategies often confine generalization to predefined domains, limiting robustness under diverse real-world speech variations. To address these challenges, this paper proposes an emotion-preserving conditional information bottleneck framework (EP-CIB) for domain-free DG-SER. Specifically, EP-CIB introduces a nuisance proxy representation learning module to learn a nuisance proxy as the broad non-emotional variability without requiring any domain annotations, covering both defined and previously unseen domains. It then extracts coarse-grained emotion features via the consistency-aware emotion representation learning module. EP-CIB further introduces an emotion-preserving conditional information bottleneck that, conditioned on the emotion label, disentangles the nuisance proxy from the coarse-grained emotion representation, improving domain-free generalization under open-ended domain shifts. EP-CIB departs from implicit domain surrogates in prior domain-free methods by explicitly learning a proxy for nuisance domains and disentangling it from emotion features, enabling domain-free emotion representation learning. The state-of-the-art performance in both speaker-independent and cross-corpus settings, including an 18% improvement on EmoDB-to-CASIA transfer, demonstrates the effectiveness of EP-CIB for DG-SER.

IJCAI 2026

Towards Defining, Measuring and Leveraging Creativity in AI Applications

Jingyi Yang, Alexander Tuzhilin

Despite extensive research, computational creativity still lacks a conceptually grounded, practically relevant, and empirically applicable formal definition across contexts such as art, scientific research, and games. Existing approaches to creativity often focus on proposing informal conceptual definitions without providing formal computational concepts. Alternatively, they propose formal definitions without carefully examining their underlying assumptions or providing validation of these definitions by testing their performance in specific application settings. To address these problems, this paper proposes several formal postulates of creativity grounded in prior psychological work on creativity, especially as related to Boden’s three core criteria of creativity: newness, value, and surprise. Furthermore, we propose a conceptual mathematical definition of creativity that aligns with these postulates. We also validate our proposed creativity formula in two applications: determining the creativity of particular chess moves, and assessing the creativity of paintings by some artists, such as Picasso, Renoir, and other painters. Experimental results demonstrate that our formula of creativity successfully captures creative patterns across both the chess and the art domains. This suggests that creativity can be effectively characterized through a unified mathematical structure based on three fundamental criteria, namely newness, value, and surprise, whose specific measurements are adapted to the particular domains.

IJCAI 2026

From Discrete to Continuous: Progressive Hybrid-Distributional Learning for Gradual Emotion Transitions

Yunhe Xie, Yang Li

Modeling emotional dynamics in multi-turn dialogues remains challenging due to emotion shifts, where emotional states evolve gradually rather than change abruptly. Existing methods often rely on one-hot supervision, which fails to reflect the progressive nature of human emotions, particularly for neutral-valence emotions with subtle trajectories. To address this limitation, we propose a pseudo soft-label guided Progressive Hybrid-Distributional (PHD) learning framework. PHD reconstructs discrete labels into hybrid distributional representations that encode inter-emotion relations and mixed emotional tendencies. Based on these representations, a progressive training strategy is introduced to guide the model from learning blended emotional states toward the standard one-to-one prediction objective. Furthermore, we design a Graded Contrastive Learning mechanism that replaces rigid binary allocation with graded supervision to alleviate label conflicts. Experiments on benchmark datasets demonstrate PHD consistently improves diverse baseline models, yielding average w-F1 gains of 1.29%. Our framework highlights the critical importance of modeling emotional dynamics as a gradual, distributional process.

IJCAI 2026

Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks

Kai Sun, Peibo Duan, Yongsheng Huang, Guowei Zhang, Benjamin Smith, Nanxu Gong, Levin Kuhlmann

Spiking neural networks (SNNs), which are brain-inspired and spike-driven, achieve high energy efficiency. However, a performance gap between SNNs and artificial neural networks (ANNs) still remains. Knowledge distillation (KD) is commonly adopted to improve SNN performance, but existing methods typically enforce uniform alignment across all timesteps, either from a teacher network or through inter-temporal self-distillation, implicitly assuming that per-timestep predictions should be treated equally. In practice, SNN predictions vary and evolve over time, and intermediate timesteps need not all be individually correct even when the final aggregated output is correct. Under such conditions, effective distillation should not force every timestep toward the same supervision target, but instead provide corrective guidance to erroneous timesteps while preserving useful temporal dynamics. To address this issue, we propose Selective Alignment Knowledge Distillation (SeAl-KD), which selectively aligns class-level and temporal knowledge by equalizing competing logits at erroneous timesteps and reweighting temporal alignment based on confidence and inter-timestep similarity. Extensive experiments on static image and neuromorphic event-based datasets demonstrate consistent improvements over existing distillation methods. The code is available at https://github.com/KaiSUN1/SeAl.

IJCAI 2026

Safety-Aware Shared Autonomy via World-Model Constrained Planning

Zechen Shi, Jiawei Ye, Zeyang Liu, Xingyu Chen, Xuguang Lan

Safety-aware shared autonomy aims to enable an autonomous agent to collaborate with a human operator, completing tasks under safety constraints while maximally preserving human intent. However, existing methods often underperform in long-horizon tasks that require balancing task performance under safety constraints with the degree of intervention—particularly when accurately predicting future unsafe events is critical. This limitation largely stems from their reliance on the current state alone to decide when to intervene and how to modify actions. We propose World Model Assisted Safety Planning (WASP), a model-predictive shared autonomy framework for explicit safety constraint satisfaction. We first formulate a safety-aware world model, and leverage its online predictive reasoning to decide when to intervene by jointly assessing prospective safety violations and degradation in task performance, thereby intervening only when necessary while enforcing safety constraints at decision time. Once intervention is triggered, WASP plans a short-horizon residual correction using world model rollouts, filters out unsafe candidates, and executes the least-deviating correction among the remaining high-return options in a receding-horizon loop. Experiments across diverse vision-based safety-critical domains show that WASP substantially reduces safety violations while preserving task performance and reducing unnecessary interventions over prior shared autonomy baselines.

IJCAI 2026

MFMSRNet: An Interpretable Multi-frequency and Multi-scale Riemannian Network for Motor Imagery Decoding

Wenhao Rao, Xujie Zhao, Jianhui Zhao, Bo Du, Feixiang Tang

Motor imagery (MI) electroencephalography (EEG) decoding has benefited from deep learning, yet methods operate in Euclidean space and behave as opaque black boxes, neglecting the intrinsic geometry of functional brain connectivity. EEG connectivity descriptors, such as phase synchrony and covariance matrices, naturally reside on the manifold of symmetric positive definite (SPD) matrices, where Euclidean operations are geometrically inconsistent and hinder interpretability. This work proposes the Multi-frequency and Multi-scale Riemannian Network (MFMSRNet), an interpretable end-to-end geometry-aware framework for MI EEG decoding on the SPD manifold. The method constructs kernelized phase-locking value (KPLV) functional connectivity (FC) matrices to capture nonlinear phase synchrony while ensuring positive definiteness. An attention-based Riemannian fusion mechanism adaptively integrates information across multiple frequency bands in the tangent space. Furthermore, a multi-scale Riemannian network extracts global, hemispheric, and local connectivity patterns via manifold-preserving bilinear mappings and smooth eigenvalue rectification. Extensive experiments indicate that MFMSRNet yields more expressive and interpretable representations for robust MI decoding, offering a promising solution for reliable brain–computer interface applications. The code is available at https://github.com/Raeno-Rao/MFMSRNet.

IJCAI 2026

Dual-Process Distribution Calibration: Bridging Slow-Fast Thinking for Few-Shot Learning

Yuchen Liu, Weining Weng, Lingxing Chen, Qianzhong Chen, Shiyang Li, Yuan Ma, Yang Gu

Artificial intelligence models typically perform well on large-scale datasets, yet their effectiveness tends to degrade in real-world scenarios with scarce data, such as medical diagnostics. In contrast, humans can learn and reason effectively from few examples. Even when novel objects differ significantly from prior observations, humans can quickly infer category distributions accurately. Inspired by this, we explore how to leverage human-inspired cognitive mechanisms to improve the distribution calibration in few-shot learning models. Based on the ``fast-slow thinking'' dual-process theory, we propose a novel cognition-inspired few-shot learning framework. It mimics the human cognition when handling novel information: it first rapidly screens relevant knowledge through ``fast thinking'' and then infers inter-class relationships and calibrates distributions via ``slow thinking''. Specifically, the fast-thinking stage employs a gating mechanism to quickly match input samples with known base-class prototypes, activating relevant candidate knowledge. In the slow-thinking stage, the model aggregates inter-class edge embeddings into a summary relation graph and then applies divergent Gaussian sampling to generate multiple relation graphs representing different association strengths, thereby achieving distribution calibration for few-shot classes. Experiments on public few-shot benchmarks and medical image datasets show competitive performance. Visualization analyses further reveal that our framework exhibits meaningful interpretability.

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