Federated learning (FL) enables collaborative model training without centralizing raw data, but privacy regulations such as the right to be forgotten require FL systems to remove the influence of previously used training data upon request. Retraining a federated model from scratch is prohibitively expensive, motivating federated unlearning (FU). However, existing FU methods suffer from high unlearning overhead, utility degradation caused by entangled knowledge, and unintended relearning during post-unlearning recovery. In this paper, we propose FedCARE, a unified and low overhead FU framework that enables conflict-aware unlearning and relearning-resistant recovery. FedCARE leverages gradient ascent for efficient forgetting when target data are locally available and employs data free model inversion to construct class level proxies of shared knowledge. Based on these insights, FedCARE integrates a pseudo-sample generator, conflict-aware projected gradient ascent for utility preserving unlearning, and a recovery strategy that suppresses rollback toward the pre-unlearning model. FedCARE supports client, instance, and class level unlearning with modest overhead. Extensive experiments on multiple datasets and model architectures under both IID and non-IID settings show that FedCARE achieves effective forgetting, improved utility retention, and reduced relearning risk compared to state of the art FU baselines. The source code can be found at https://github.com/thechosenchu/FedCARE.
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Qualitative preferences specified in CP-theory define a partial order over outcomes based on induced dominance relations. We investigate the problem of measuring divergence between such induced orders. We introduce two formally equivalent distance metrics, each leveraging a distinct computational strategy based on different types of normalization. Preliminary empirical results support the soundness of the proposed methodologies and offer comparative insights into their computational properties. To the best of our knowledge, this is the first study to compute distance measures between qualitative preferences that jointly accounts for conditional dependencies and relative importance statements.
AI for materials science is a critical topic within AI for science, aiming to accelerate materials discovery and produce accurate property predictions. Bilayer 2D material stacking is essential for exploring new materials with novel functions and inherent phenomena, enabling the creation of new 2D bilayers for diverse real-world applications. Research on bilayer vdWs materials has made significant progress from experimental and computational perspectives. Various bilayer materials have been successfully synthesized experimentally and the increasing utilization of high-throughput computing technology has constructed several computational two-dimensional materials databases. However, the use of AI to model bilayer stacking and predict new properties remains underexplored, necessitating further research studies. In this work, we propose a novel multimodal learning approach to study the interfaces between dissimilar materials that jointly enable new or multiple functions, and to predict new properties arising from the vertical integration (stacking) of different functional material layers under given configurations. Comprehensive experiments demonstrate the effectiveness and efficiency of our approach compared to baseline methods. Our code is available at https://github.com/AnVuong123/bimat_ml.
Probabilities of causation (PoCs) are fundamental quantities for counterfactual analysis and personalized decision making. However, existing analytical results are largely confined to binary settings. This paper extends PoCs to multi-valued treatments and outcomes by deriving closed form bounds for a representative family of discrete PoCs within Structural Causal Models, using standard experimental and observational distributions. We introduce the notion of equivalence classes of PoCs, which reduces arbitrary discrete PoCs to this family, and establish a replaceability principle that transfers bounds across value permutations. For the resulting bounds, we prove soundness in all dimensions and empirically verify tightness in low dimensional cases via Balke's linear programming method; we further conjecture that this tightness extends to all dimensions. Simulations indicate that our closed form bounds consistently tighten recent recursive bounds while remaining simpler to compute. Finally, we illustrate the practical relevance of our results through toy examples.
Neural Network k-Nearest Neighbor (NN-kNN) was proposed as an interpretable network model that learns feature weights and similarity to retrieve relevant cases for classification. This paper extends it to regression with the goal of generating accurate predictions based on neighboring cases with similar labels. Specifically, we introduce three modular components: an attention mechanism that weights the contribution of retrieved cases, a locality-aware regularizer that favors label-similar neighbors, and an optional case adaptation module that refines the retrieved estimate. Across synthetic and standard tabular regression benchmarks, NN-kNN achieves competitive predictive error against strong baselines (kNN-R, MLKR, and MLPs) while providing cases with similar labels as explanation (later referred as label-similar case). Moreover, NN-kNN supports manual knowledge injection through tuning weights for human-comprehensible features. The result is a simple, general, and interpretable approach to continuous-valued prediction that unifies retrieval, attention, and optional case adaptation within a single neural framework.
Creative story generation has achieved notable progress with large language models. Current methods construct narratives through hierarchical planning or incremental expansion. These approaches produce structurally complete stories but offer limited support for organic narrative development. Many fiction writers offer a complementary view of the creative process. They often describe their work as gradually uncovering a story embedded in the world they have built. This perspective motivates our approach. We propose that a world model can grow to contain latent narratives as it develops. When characters hold incomplete knowledge and situations remain unresolved, the world structure itself accumulates seeds for story development. Based on this insight we present FossilWriter, a framework that learns hypergraph world models for creative story generation. FossilWriter represents the story world as a shared hypergraph and introduces a narrative layer that marks elements with developmental potential. An excavation module detects and scores latent narratives on this structure. A scene controller selects locations and characters to develop promising narrative threads. The hypergraph grows during writing and serves as the single source of truth to ensure long-range consistency. Experiments on three benchmarks show that FossilWriter achieves an 81.4% win rate on plot coherence and 84.7% on world consistency. Human assessment shows a 32.5% reduction in long-range factual conflicts compared with strong baselines.
Synthesising autonomous agents that can navigate uncertain environments while adhering to complex temporal constraints remains a fundamental challenge. While Linear Temporal Logic (LTL) provides a rigorous language for specifying such tasks, the inherent undecidability of qualitatively verifying LTL satisfaction in partially observable Markov decision processes renders quantitative synthesis difficult, especially when designing reliable reward signals for approximate solvers. In this paper, we bridge this gap with a novel, sound reward-shaping mechanism that dynamically generates belief-dependent rewards grounded in certified LTL satisfaction. By integrating this mechanism into an enhanced Monte Carlo Planning framework, we empower agents to navigate the `fog' of partial observability with a search process focused on maximising verifiable success. Our experiments demonstrate that this approach not only thrives in scenarios where existing solvers fail but also maintains effectiveness and scalability across diverse benchmark domains.
We present a unified framework for modeling agents’ epistemic states and counterfactual conditionals. The novelty of our approach lies at the semantic level. We introduce a computationally grounded semantics in which an agent’s doxastic accessibility relation, needed to model the standard notion of deductively closed belief, is derived from the agent’s belief base, and in which the notion of comparative similarity between states, needed to interpret counterfactual conditionals, is computed from both the atomic formulas describing the agents’ belief bases and those describing the environment. We use this semantics to interpret a language of beliefs and counterfactuals. We demonstrate the expressiveness of our language and the flexibility of the semantics by formalizing a wide range of notions: counterfactual dependence, counterfactual notions of reason and knowledge, conditional belief. Furthermore, we show that our semantics illuminates the subtle interplay between belief change and counterfactuals. On the computational side, we provide a succinct formulation of model checking for our language and establish a PSPACE complexity result.
Knowledge Graphs (KGs) are extensively used across different domains and in several applications. Often, these KGs are very large in size. Such KGs become unwieldy for tasks such as question answering and visualization. Summarization of KGs offers a viable alternative in such cases. Furthermore, personalized KG summarization is crucial in the current data-driven world as it captures the specific requirements of users based on their query patterns. Since it only maintains relevant information, the personalized summaries of KG are small, resulting in significantly smaller storage requirements and query runtime. In this work, we adapt the coreset theory to create personalized KG summaries. For a given dataset and a user-specific query workload, we present an approach that samples a relevant subset of triples using sensitivity-based importance sampling. We ensure that the subset approximates the characteristics of the full dataset with bounded approximation error. We define sensitivity scores that measure the importance of a triple with respect to a user’s query workload, which are then used by our coreset construction algorithm. We explicitly focus on personalized knowledge graph summarization by constructing summaries independently for each user based on their query behaviour. Our evaluation on Freebase, WikiData, and DBpedia shows that COREKG delivers higher query-answering accuracy and structural coverage than the state-of-the-art methods, such as GLIMPSE, PPR, PEGASUS and APEX² while requiring only a tiny fraction of the original graph.
Large Language Models (LLMs) have shown potential for solving mathematical tasks. We show that LLMs can be utilized to generate proofs by induction for hardware verification and thereby replace some of the manual work %alleviating some of the manual effort done by Formal Verification engineers and deliver value to industry. We present a neurosymbolic approach that includes two prompting frameworks to generate candidate invariants, which are checked using a formal symbolic tool. Our results indicate that with sufficient reprompting, LLMs are able to generate inductive arguments for mid-size open-source RTL designs. For 90% of our problem set, at least one of the prompt setups succeeded in producing a provably correct inductive argument.
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.
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.
Chain-of-Thought (CoT) reasoning empowers Large Language Models (LLMs) with remarkable capabilities, but typically requires enormous parameter scales. CoT distillation has emerged as a promising paradigm to transfer reasoning prowess into compact student models (SLMs), yet existing approaches often rely on a solitary teacher, capping the student’s potential since individual LLMs often exhibit distinct capability biases and may suffer from catastrophic forgetting. While leveraging diverse teachers seems appealing, effectively fusing their supervisions remains challenging: teacher-student incompatibility risks amplifying hallucinations, and passive supervision fails to ensure genuine logic internalization. To address this, we introduce COMPACT, a framework that adaptively fuses supervisions from different teachers by dynamically weighting teacher gradients based on the student’s real-time compatibility evaluated by a multi-dimensional metric: (1) Graph-based Consensus to filter misleading rationales by identifying mainstream reasoning paths; (2) Mutual-Information-based Adaptability to detect "epiphany moments" for genuinely understanding the reasoning process rather than merely imitating; and (3) Loss-based Difficulty to assess student receptivity to the teacher's guidance and prevent negative transfer. Our experiments and latent space analysis demonstrate that COMPACT effectively integrates diverse reasoning capabilities without damaging the model's original knowledge structure, achieving state-of-the-art performance on various benchmarks while effectively mitigating catastrophic forgetting. Code available: https://github.com/CAG-Research/COMPACT.git.
We introduce missingness-MDPs (miss-MDPs), a novel subclass of partially observable Markov decision processes (POMDPs) that incorporates the theory of missing data. A miss-MDP is a POMDP whose observation function is a missingness function, specifying the probability that individual state features are missing (i.e., unobserved) at a time step. The literature distinguishes three canonical missingness types: (1) missing completely at random (MCAR), (2) missing at random (MAR), and (3) missing not at random (MNAR). The problem is to compute near-optimal policies for a miss-MDP with an unknown missingness function, given a dataset of action-observation histories. Achieving such optimality guarantees for policies requires learning the missingness function from data, which is infeasible for general POMDPs. To overcome this challenge, we exploit the structural properties of different missingness types to derive probably approximately correct (PAC) algorithms for learning the missingness function. These algorithms yield an approximate but fully specified miss-MDP that we solve using off-the-shelf planning methods. We prove that, with high probability, the resulting policies are ε-optimal in the true miss-MDP. Empirical results confirm the theory and demonstrate superior performance of our approach over two model-free methods.
World models (WM) learn predictive representations from high-dimensional sensory input. However, their internal knowledge is implicit and often inadequate for systematic reasoning about object structure. Qualitative Spatial Reasoning (QSR) models provide explicit human-readable object descriptions under transformations. This paper shows that these two forms of modeling can be combined by injecting a QSR model into a WM to improve the WM reasoning abilities. Using a cube rotation setting inspired by the cube comparison test used in mental rotation tasks as a case study, we use a QSR model that captures the geometric relationships and transformation rules describing how features move across faces under discrete rotations. We translate this QSR model into differentiable constraints using Logic Tensor Networks (LTNs) to guide gradient based learning. These constraints are injected into the WM loss function to regularize its latent dynamics to satisfy the spatial constraints of rotation. The results show that under an extreme data scarce regime (10%), baseline model's performance collapses to near zero, whereas our approach achieves above 90% better performance relative to baseline. Our method consistently maintains high scores across all data regimes (~80%), for both in-distribution and out-of-distribution observations. This paper proposes a novel framework to infuse QSR knowledge inside WM to make learned latent dynamics logical. Code is available at: https:// github.com/ajayrao80/LtnDreamer/
With the expanding use of artificial intelligence generated images (AGIs) in scenarios such as gaming, art, and film production, evaluating their quality is essential to ensure their practical utility. To guarantee effective quality measurement, both content and distortion must be considered. However, existing image quality assessment (IQA) methods fail to sufficiently combine the two aspects in their assessments. Meanwhile, the AGIs annotated with credible content and distortion labels are lack, thus, employing cross-domain methods to utilize typically public image datasets is meaningful. Based on the above conclusions, this paper proposed a cross-domain AI-generated IQA via content-distortion awareness (CDAQA). Firstly, the large language model has been utilized to obtain content and distortion expressions of images. Secondly, a dual-stream contrastive language-image pre-training module (DCLIP) has been designed to gain both quality-aware features in images and the corresponding expressions that related to content and distortion, respectively. Thirdly, a graph knowledge transferring module (GKT) that constructs graphs via inter-domain content relevance has been proposed. GKT can update distortion information in the source domain according to the target domain representations. Finally, the cross-domain framework has been designed to update the existing IQA model for AGIs. And, the contrastive loss of DCLIP, feature update loss of GKT, and quality prediction loss are coordinated to jointly update different modules, enabling sufficient fusion of target domain knowledge. Experimental results demonstrate that CDAQA achieves higher accuracy and stability in cross-domain AGIs tasks.
Fair clustering has garnered considerable attention, with various fairness notions proposed to ensure equitable representation across demographic groups. In this paper, we focus on the k-center problem in bounded doubling metrics under two popular fairness requirements: group fairness and data summarization fairness, referred to Group Fair k-Center (Gf-k-Cen) and Data Summarization Fair k-Center (Dsf-k-Cen), respectively. Both fairness notions extend classical clustering formulation by associating each data point with a demographic label. Motivated by recent advances in parameterized approximation results for fair clustering, we investigate whether these problems admit Fixed-Parameter Tractable (FPT) approximation schemes in bounded doubling metrics. The previous algorithms typically neglect the local structural properties induced by fairness constraints itself, which limits their approximation quality. By further leveraging the geometric properties of doubling metrics together with local fairness information, we develop a candidate-based structural method that yields (1+eps)-approximation algorithms with FPT running times for both problems, parameterized by the number of selected centers. To the best of our knowledge, these results constitute the first parameterized approximation schemes for the Gf-k-Cen and Dsf-k-Cen problems in bounded doubling metrics.
Unified segmentation aims to consolidate multiple vision tasks into a single model, yet faces two core challenges: learning robust homogeneous features (e.g., shared low- and mid-level cues) to enable cross-domain knowledge transfer, while disentangling heterogeneous features (e.g., task-specific semantic objectives) to avoid negative transfer and preserve task independence. To address these challenges, we propose the Homo- and Heterogeneous Network (H2Net), a unified framework that jointly models shared homogeneous representations and task-specific heterogeneous features. Specifically, H2Net incorporates a Cross-Modal Structure Enhancement Module (CSEM), which integrates auxiliary depth priors via joint frequency–spatial cross-modal attention to strengthen task-agnostic structural representations. In addition, a Task Adapter Pool (TAP) is introduced to model task-specific heterogeneous features by assigning dedicated adapters to individual tasks, enabling task-aware feature modulation and semantic disentanglement within a shared backbone. Extensive experiments on benchmarks spanning eight tasks demonstrate the effectiveness of the proposed approach and its superior performance. Code and results will be available at https://h2net-ijcai26.github.io.
Emotion and intent joint understanding in multimodal conversations (MC-EIU) aims to infer emotion and intent information by leveraging the semantic dependencies among multimodal data. However, prior works largely ignore redundancy interference and suffer from insufficient interaction and inter-task noise propagation due to their reliance on shallow mechanisms. To overcome these limitations, we propose a novel framework named Disentangled Hierarchical Interaction and Adaptive Adjustment (DELTA) for MC-EIU. We first design a Disentangled Feature Denoising module based on orthogonal decomposition to effectively filter out noise and redundancy from heterogeneous data. Second, we propose a Dual-Level Adaptive Adjustment mechanism to dynamically optimize learning dynamics from both modality and instance perspectives. Furthermore, we introduce a Hierarchical Task-Bottleneck Interaction(HTBI) module, which employs a set of progressively compressed bottleneck tokens as a communication hub. This design can effectively simulating the multi-round iterative interaction between emotion and intent tasks. Experiments on the benchmark MC-EIU bilingual dataset demonstrate that our framework significantly outperforms state-of-the-art baselines in both emotion and intent tasks.
LLM-based role-playing has rapidly improved in fidelity, yet stronger adherence to persona constraints commonly increases vulnerability to jailbreak attacks, especially for risky or negative personas. Most prior work mitigates this issue with training-time solutions (e.g., data curation or alignment-oriented regularization). However, these approaches are costly to maintain as personas and attack strategies evolve, can degrade in-character behavior, and are typically infeasible for frontier closed-weight LLMs. We propose a training-free Dual-Cycle Adversarial Self-Evolution framework with two coupled cycles. A Persona-Targeted Attacker Cycle synthesizes progressively stronger jailbreak prompts, while a Role-Playing Defender Cycle distills observed failures into a hierarchical knowledge base of (i) global safety rules, (ii) persona-grounded constraints, and (iii) safe in-character exemplars. At inference time, the Defender retrieves and composes structured knowledge from this hierarchy to guide generation, producing responses that remain faithful to the target persona while satisfying safety constraints. Extensive experiments across multiple proprietary LLMs show consistent gains over strong baselines on both role fidelity and jailbreak resistance, and robust generalization to unseen personas and attack prompts.