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Yeming Li, Chenxi Liu, Jie Zou, Cheng Long, Chaoning Zhang, Peng Wang, Yang Yang

Conversational Recommender Systems (CRS) aim to provide personalized recommendations by interacting with users through natural language dialogue. However, in scenarios requiring deep geospatial awareness, existing methods, including those based on Large Language Models (LLMs), still face significant challenges in effectively fusing heterogeneous, multimodal geographic information with dynamic dialogue context. Simple fusion strategies struggle to resolve the asymmetric dependencies between dynamic user intent and static geographic context and fail to bridge the semantic gap between LLMs and structured geospatial data. To address these issues, we propose a framework for geography-aware CRS, named GeoCRS. Our core idea is to empower a frozen LLM with powerful geospatial reasoning capabilities by conditioning it on a dynamic, multimodal guidance signal generated by an external fusion architecture, all without altering the LLM's internal parameters. Specifically, we first design a hierarchical geographical encoder to uniformly represent heterogeneous geographic data. Subsequently, we introduce a contextual feature modulation module that asymmetrically injects the geographic context into the user's dialogue intent via a novel modulation mechanism to improve conversational recommendation via both geographic and dialogue context. Extensive experiments on public benchmark datasets demonstrate that our proposed GeoCRS significantly outperforms state-of-the-art baselines on the geography-aware conversational recommendation task.

Xu Li, Leendert Van der Torre, Liuwen Yu

In agent theory, epistemic trust is used to infer beliefs, for example by filtering out the information the agent receives from untrustworthy agents. Moreover, trust itself can be inferred from other information. We introduce a simple information filtering architecture that clearly distinguishes the relation between the two kinds of inference. We provide a logical analysis of the architecture, based on a new family of input/output logics. We then explore information filtering and belief manipulation within this formal framework. Our key finding is that with this architecture, some of the widely debated logical rules for trust inference are redundant with respect to information-filtering mechanisms and some others are redundant with respect to belief manipulation.

Weijie Li, Zhongqing Wang, Guodong Zhou

Most commonsense reasoning models overlook the influence of personality traits, limiting their effectiveness in personalized systems such as dialogue generation. To address this limitation, we introduce the Personality-aware Commonsense Knowledge Graph (PCoKG), a structured dataset comprising 521,316 quadruples. We begin by employing three evaluators to score and filter events from the ATOMIC dataset, selecting those that are likely to elicit diverse reasoning patterns across different personality types. For knowledge graph construction, we leverage the role-playing capabilities of large language models (LLMs) to perform reasoning tasks. To enhance the quality of the generated knowledge, we incorporate a debate mechanism consisting of a proponent, an opponent, and a judge, which iteratively refines the outputs through feedback loops. We evaluate the dataset from multiple perspectives and conduct fine-tuning and ablation experiments using multiple LLM backbones to assess PCoKG's robustness and the effectiveness of its construction pipeline. Our LoRA-based fine-tuning results indicate a positive correlation between model performance and the parameter scale of the base models. Finally, we apply PCoKG to persona-based dialogue generation, where it demonstrates improved consistency between generated responses and reference outputs. This work bridges the gap between commonsense reasoning and individual cognitive differences, enabling the development of more personalized and context-aware AI systems.

Qian Li, Cheng Ji, Zhaoji Liang, Yuzheng Zhang, Zhuo Chen, Siyuan Liang

Multi-modal imbalanced cross-source entity alignment aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs) that encompass diverse data sources with imbalanced modality, which poses significant challenges due to the non-uniform distribution of information across different modalities. Existing methods encounter major limitations in aligning entities across MMKGs, where missing data and modality-specific inconsistencies thus create information gaps. These gaps, stemming from disparities in neighborhood structure and attribute availability, result in reduced alignment performance. To address these challenges, we propose a novel multi-modal fact knowledge generation framework to advance imbalanced cross-source entity alignment. Utilizing large language models (LLMs) for comprehensive knowledge completion, our framework enriches MMKGs by synthesizing missing neighboring entities and relational attributes, enabling precise one-to-one similarity comparisons across all relations and attributes. Specifically, neighbor entity completion generates probable neighboring entities to fill structural gaps, while attribute completion synthesizes missing relational attributes to improve alignment. The facts evaluation module assesses generated triples, ensuring that only high-quality information supports the alignment. Extensive experiments on benchmark datasets demonstrate that our framework significantly outperforms strong competitors, achieving superior entity alignment performance.

Jinpeng Li, Hang Yu, Ziqi Ma, Peng Qi

Knowledge graph construction (KGC) aims to extract valuable information from text and organize it into structured knowledge graphs (KGs). Recent methods have leveraged the strong generative capabilities of large language models (LLMs) to improve the generalization and reduce the labor costs. However, constrained by the input length of LLMs, existing methods mainly focus on extracting knowledge within individual texts and lack the capability to discover latent knowledge across texts. To fill this gap, we propose a novel method for open knowledge graph construction, termed KG-DLF. The core idea of this method is to enhance the knowledge graph construction process by discovering new facts that are consistent with the underlying contextual logic. Specifically, we first design a knowledge extractor to extract knowledge from the text. Then, a knowledge normalizer performs schema alignment on the extracted knowledge. Next, we explore a knowledge discoverer based on a clue search strategy, which leverages the logical consistency of context to mine latent facts. Finally, we design a counterfactual-based knowledge corrector, enabling the model to purify knowledge and reduce factual errors. Experimental results show that KG-DLF is capable of extracting comprehensive knowledge in open-world scenarios across three KGC benchmarks.

Chenyi Li, Wanli Ma, Zichen Wang, Zaiwen Wen

While large language models (LLMs) have shown progress in mathematical reasoning, they still face challenges in formalizing theorems that arise from instantiating abstract structures in concrete settings. With the goal of auto-formalizing mathematical results at the research level, we develop a framework for structure-to-instance theorem autoformalization (SITA), which systematically bridges the gap between abstract mathematical theories and their concrete applications in Lean proof assistant. Formalized abstract structures are treated as modular templates that contain definitions, assumptions, operations, and theorems. These templates serve as reusable guides for the formalization of concrete instances. Given a specific instantiation, we generate corresponding Lean definitions and instance declarations, integrate them using Lean’s typeclass mechanism, and construct verified theorems by checking structural assumptions. We incorporate LLM-based generation with feedback-guided refinement to ensure both automation and formal correctness. Experiments on a dataset of optimization problems demonstrate that SITA effectively formalizes diverse instances grounded in abstract structures.

Mario Leiva, Noel Ngu, Joshua Shay Kricheli, Aditya Taparia, Ransalu Senanayake, Paulo Shakarian, Nathaniel D. Bastian, John Corcoran, Gerardo Simari

The deployment of pre-trained perception models in novel environments often leads to performance degradation due to distributional shifts. Although recent artificial intelligence approaches for metacognition use logical rules to characterize and filter model errors, improving precision often comes at the cost of reduced recall. This paper addresses the hypothesis that leveraging multiple pre-trained models can mitigate this recall reduction. We formulate the challenge of identifying and managing conflicting predictions from various models as a consistency-based abduction problem, building on the idea of abductive learning (ABL) but applying it to test-time instead of training. The input predictions and the learned error detection rules derived from each model are encoded in a logic program. We then seek an abductive explanation—a subset of model predictions—that maximizes prediction coverage while ensuring the rate of logical inconsistencies (derived from domain constraints) remains below a specified threshold. We propose two algorithms for this knowledge representation task: an exact method based on Integer Programming (IP) and an efficient Heuristic Search (HS). Through extensive experiments on a simulated aerial imagery dataset featuring controlled, complex distributional shifts, we demonstrate that our abduction-based framework outperforms individual models and standard ensemble baselines, achieving, for instance, average relative improvements of approximately 13.6% in F1-score and 16.6% in accuracy across 15 diverse test datasets when compared to the best individual model. Our results validate the use of consistency-based abduction as an effective mechanism to robustly integrate knowledge from multiple imperfect models in challenging, novel scenarios.

Viet-Man Le, Lukas André Feldgrill, Alexander Felfernig

Detecting minimal conflict sets is essential for providing meaningful feedback in knowledge-based configuration. While lazy conflict detection addresses runtime efficiency by predetermining conflict sets offline using a genetic algorithm, it suffers from low conflict coverage, stagnation, and instability. We propose a robust enhancement that integrates multi-conflict extraction and genetic diversity control to overcome these limitations. Our method extends conflict discovery per evaluation and introduces three diversity mechanisms: full population reproduction, weighted genetic operators, and adaptive extinction. Empirical evaluations on five real-world configuration knowledge bases show that our approach recovers up to 85% of conflict sets, reduces solver calls by up to 73%, and achieves higher result stability. These improvements demonstrate the scalability and reliability of enhanced lazy conflict detection for interactive configuration systems.

Václav Kůla, Qipeng Kuang, Yuyi Wang, Yuanhong Wang, Ondřej Kuželka

The Weighted First-Order Model Counting Problem (WFOMC) asks to compute the weighted sum of models of a given first-order logic sentence over a given domain. Conditioning WFOMC on evidence—fixing the truth values of a set of ground literals—has been shown impossible in time polynomial in the domain size (unless ♯P ⊆ FP) even for fragments of logic that are otherwise tractable for WFOMC without evidence. In this work, we address the barrier by restricting the binary evidence to the case where the underlying Gaifman graph has bounded treewidth. We present a polynomial-time algorithm in the domain size for computing WFOMC for the two-variable fragments ??² and ?² conditioned on such binary evidence. Furthermore, we show the applicability of our algorithm in combinatorial problems by solving the stable seating arrangement problem on bounded-treewidth graphs of bounded degree, which was an open problem. We also conducted experiments to show the scalability of our algorithm compared to the existing model counting solvers.

Patrick Koopmann, Yasir Mahmood, Axel-Cyrille Ngonga Ngomo, Balram Tiwari

We introduce the notion of contrastive ABox explanations to answer questions of the type “Why is a an instance of C, but b is not?”. While there are various approaches for explaining positive entailments (why is C(a) entailed by the knowledge base) as well as missing entailments (why is C(b) not entailed) in isolation, contrastive explanations consider both at the same time, which allows them to focus on the relevant commonalities and differences between a and b. We develop an appropriate notion of contrastive explanations for the special case of ABox reasoning with description logic ontologies, and analyze the computational complexity for different variants under different optimality criteria, considering lightweight as well as more expressive description logics. We implemented a first method for computing one variant of contrastive explanations, and evaluated it on generated problems for realistic knowledge bases.

MohammadHossein Karimian, Shakil M Khan, Yves Lesperance

Responsibility is a central concept in accountable decision making for multiagent systems. As modern AI systems grow in complexity and autonomy, there is a growing demand for them to address issues in AI ethics, prompting researchers to formalize responsibility from diverse perspectives, including strategic responsibility. However, causal responsibility, i.e. responsibility due to actual causal contribution, has received much less attention. In this paper, we study variants of responsibility attribution from both strategic and causal perspectives within a synchronous game-theoretic logic framework that allows concurrent moves by multiple agents. Our formalization is based on Situation Calculus Synchronous Game Structures (SCSGS). We show that by combining these perspectives, one can obtain novel forms of responsibility attribution that are grounded on actual causation. While doing this, we propose an account of actual causation in SCSGS. We prove that our formalization handles the issues associated with preemption and over-determination well. We also study some key properties of responsibility and demonstrate that causal, strategic, and combined notions of responsibility are extensionally distinct.

Ruiqi Jin, Shuyi Li, Yongmei Liu

ATL and Strategy Logic (SL) are important languages for representation and reasoning about strategic abilities of coalitions in multi-agent systems. In analyzing strategies of agents in multi-agent systems, an important concept to consider is rationality. Strategy Logic can express rationality concepts such as Nash Equilibrium (NE). Recently, there has been work on logics for joint abilities incorporating rationality concepts based on iterated elimination of dominated strategies (IEDS). Each of NE and IEDS has its strengths and limitations. However, when the payoff is binary, e.g., whether a goal is satisfied, IEDS has more distinguishing power than NE. In this work, we propose Strategy Logic with IEDS (SL_{IEDS}), an extension of Strategy Logic with an IEDS operator, where we can reason about rational strategies that survive IEDS. We prove that SL_{IEDS} is strictly more expressive than SL. Finally, we prove that model checking memoryless SL_{IEDS} is EXPTIME-complete.

Antonio Ielo, Giuseppe Mazzotta, Rafael Peñaloza, Francesco Ricca

Linear Temporal Logic over finite traces (LTLf) is a widely used formalism with applications in AI, process mining, model checking, and more. The primary reasoning task for LTLf is satisfiability checking; yet, the recent focus on explainable AI has increased interest in analyzing inconsistent formulae, making the enumeration of minimal explanations for unsatisfiability a relevant task also for LTLf. We introduce a novel technique for enumerating minimal unsatisfiable cores (MUCs) of an LTLf specification. The main idea is to encode an LTLf formula into an Answer Set Programming (ASP) specification, such that the minimal unsatisfiable subsets (MUSes) of the ASP program directly correspond to the MUCs of the original LTLf specification. Leveraging recent advancements in ASP solving yields an MUC enumerator achieving good performance in experiments conducted on established benchmarks from the literature.

Yazmín Ibáñez-García, Jean Christoph Jung, Vincent Michielini, Filip Murlak

We clarify the complexity of answering unions of conjunctive queries over knowledge bases formulated in the description logic S, the extension of ALC with transitive roles. Contrary to what existing partial results suggested, we show that the problem is, in fact, 2ExpTime-complete; hardness already holds in the presence of two transitive roles and for Boolean conjunctive queries. We complement this result by showing that the problem remains in coNExpTime when the input query is rooted or is restricted to use at most one transitive role (but may use arbitrarily many non-transitive roles).

Joshua Hirschbrunn, Yevgeny Kazakov

We propose an extension of Description Logics (DLs) with generic concepts and conditional axioms. Inspired by object-oriented languages, generic concepts allow a compact definition of concepts with similar structures. For example, one can define a generic concept Owner[X] to describe objects that own another object from X, and later use a specific replacement of the parameter X, such as Owner[Pet] representing pet owners. Conditional axioms can be used to set bounds on the values that replace the generic parameters. For example, we could restrict replacements of X in a concept Keeper[X] to only subconcepts of Pet. As the set of possible parameter replacements can be infinite and even uncountable, the generic extensions are, in general, undecidable. To identify decidable generic DLs, we focus on the case of terminologies, requiring that variables are only used in definitions of generic concepts. We formulate restrictions that allow a reduction of generic entailment to classical entailment and further conditions that ensure decidability.

Bernardo Cuenca Grau, Eva Feng, Przemysław Andrzej Wałęga

Graph Neural Networks (GNNs) address two key challenges in applying deep learning to graph-structured data: they handle varying size input graphs and ensure invariance under graph isomorphism. While GNNs have demonstrated broad applicability, understanding their expressive power remains an important question. In this paper, we propose GNN architectures that correspond precisely to prominent fragments of first-order logic (FO), including various modal logics as well as more expressive two-variable fragments. To establish these results, we apply methods from finite model theory of first-order and modal logics to the domain of graph representation learning. Our results provide a unifying framework for understanding the logical expressiveness of GNNs within FO.

Piotr Gorczyca, Hannes Strass

Standpoint logics offer unified modal logic-based formalisms for representing multiple heterogeneous viewpoints. At the same time, many non-monotonic reasoning frameworks can be naturally captured using modal logics — in particular using the modal logic S4F. In this work, we propose a novel formalism called S4F Standpoint Logic, which generalises both S4F and standpoint propositional logic and is therefore capable of expressing multi-viewpoint, non-monotonic semantic commitments. We define its syntax and semantics and analyze its computational complexity, obtaining the result that S4F Standpoint Logic is not computationally harder than its constituent logics, whether in monotonic or non-monotonic form. We also outline mechanisms for credulous and sceptical acceptance and illustrate the framework with an example.

Preesha Gehlot, Anna Rapberger, Fabrizio Russo, Francesca Toni

Assumption‐Based Argumentation (ABA) is a powerful structured argumentation formalism, but exact computation of extensions under stable semantics is intractable for large frameworks. We present the first Graph Neural Network (GNN) approach to approximate credulous acceptance in ABA. To leverage GNNs, we model ABA frameworks via a dependency graph representation encoding assumptions, claims and rules as nodes, with heterogeneous edge labels distinguishing support, derive and attack relations. We propose two GNN architectures—ABAGCN and ABAGAT—that stack residual heterogeneous convolution or attention layers, respectively, to learn node embeddings. Our models are trained on the ICCMA 2023 benchmark, augmented with synthetic ABAFs, with hyperparameters optimised via Bayesian search. Empirically, both ABAGCN and ABAGAT outperform a state‐of‐the‐art GNN baseline that we adapt from the abstract argumentation iterature, achieving a node‐level F1 score of up to 0.71 on the ICCMA instances. Finally, we develop a sound polynomial time extension‐reconstruction algorithm driven by our predictor: it reconstructs stable extensions with F1 above 0.85 on small ABAFs and maintains an F1 of about 0.58 on large frameworks. Our work opens new avenues for scalable approximate reasoning in structured argumentation.

Robert Ganian, Hung P. Hoang, Simon Wietheger

We study the computational problem of computing a fair means clustering of discrete vectors, which admits an equivalent formulation as editing a colored matrix into one with few distinct color-balanced rows by changing at most k values. While NP-hard in both the fairness-oblivious and the fair settings, the problem is well-known to admit a fixed-parameter algorithm in the former "vanilla" setting. As our first contribution, we exclude an analogous algorithm even for highly restricted fair means clustering instances. We then proceed to obtain a full complexity landscape of the problem, and establish tractability results which capture three means of circumventing our obtained lower bound: placing additional constraints on the problem instances, fixed-parameter approximation, or using an alternative parameterization targeting tree-like matrices.

Rustam Galimullin, Munyque Mittelmann, Laurent Perrussel

In diffusion auctions, sellers can leverage an underlying social network to broaden participation, thereby increasing their potential revenue. Specifically, sellers can incentivise participants in their auction to diffuse information about the auction through the network. While numerous variants of such auctions have been recently studied in the literature, the formal verification and strategic reasoning perspectives have not been investigated yet. Our contribution is threefold. First, we introduce a logical formalism that captures the dynamics of diffusion and its strategic dimension. Second, for such a logic, we provide model-checking procedures that allow one to verify properties like the Nash equilibrium, and that pave the way towards checking the existence of sellers' strategies. Third, we establish computational complexity results for the presented algorithms.