Temporal Knowledge Graph Reasoning (TKGR) aims at inferring missing (especially future) events from historical data. Current evaluation in TKGR uniformly weights all events, ignoring that most are trivial repetitions, which overestimate the true reasoning ability. Therefore, the rare outstanding events, whose prediction demands deeper reasoning, should be distinguished and emphasized. To this end, we propose a strikingness-aware evaluation framework, which introduces a rule-based strikingness measuring framework (RSMF) to quantify event strikingness by comparing its expected occurrence with peer events derived from temporal rules. Strikingness is then integrated as a weighting factor into metrics like weighted MRR and Hits@k. Experiments on four TKG benchmarks reveal: 1) All representative models perform worse as event strikingness increases, 2) Path-based methods excel on low-strikingness events and representation-based ones on high-strikingness events, 3) We design an ensemble method whose gains stem from fitting trivial events rather than reasoning improvement. Our framework provides a more rigorous evaluation, refocusing the field on predicting outstanding events.
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Existing multi-modal automatic modulation recognition (AMR) methods primarily focus on exploiting multi-view representations of raw signal data to improve performance, but still struggle to effectively model and exploit high-level human prior knowledge. Although recent studies attempt to introduce large language models (LLMs) to integrate the textual modality, most of them merely treat LLMs as feature extractors, without further exploiting the LLM's potential. To this end, we propose a knowledge-enhanced multi-modal automatic modulation recognition framework based on large language model (AMR-LLM). Specifically, we first specially design an AMR instruction construction mechanism based on knowledge to activate the LLM's potential for signal perception, which designates the LLM as a signal domain expert, introduces human prior knowledge as a supplement, and exploits the unique physical information of each data sample as connection guidance. Then, to enable the LLM to directly perceive digital signals, we introduce a progressive multi-modal fusion strategy mapping signal features into the space of LLMs. Experimental results on multiple benchmark datasets demonstrate that AMR-LLM achieves approximately 5% higher accuracy than current state-of-the-art methods. Moreover, it achieves the current domain performance with only 30% of the data.
Referring Multi-Object Tracking (RMOT) faces a fundamental structural contradiction between the high-discriminability demand and the sparse semantic supervision. This mismatch is particularly acute in highly homogeneous scenarios that require fine-grained discrimination over complex compositional semantics. However, under sparse supervision, models overfit to salient yet insufficient cues, leading to shortcut learning and degraded compositional discrimination. To resolve this, we propose COAL (Counterfactual and Observation-enhanced Alignment Learning), a framework that advances RMOT beyond isolated structural optimization through knowledge regularization. First, we introduce Explicit Semantic Injection (ESI) via a VLM to densify the observation space and enhance instance discriminability. Second, leveraging LLM reasoning, we propose Counterfactual Learning (CFL) to augment supervision, enforcing strict attribute verification for robust compositional recognition. These strategies are unified within a Hierarchical Multi-Stream Integration (HMSI) architecture, which distills external knowledge into domain-specific discriminative representations. Experiments on Refer-KITTI and Refer-KITTI-V2 benchmarks validate COAL's efficacy. Notably, it surpasses the state-of-the-art by 7.28% HOTA on the highly challenging Refer-KITTI-V2. These results demonstrate the effectiveness of knowledge regularization for resolving the sparsity–discriminability paradox in RMOT.
Inductive Logic Programming (ILP) learns interpretable logical rules from data. Existing methods are transductive: their learned parameters are bound to specific predicates and require retraining for each new task. We introduce Neural Rule Inducer (NRI), a pretrained model for zero-shot rule induction. Rather than encoding literal identities, NRI represents literals using domain-agnostic statistical properties such as class-conditional rates, entropy, and co-occurrence, which generalize across variable identities and counts without retraining. The model consists of a statistical encoder and a parallel slot-based decoder. Parallel decoding preserves the permutation invariance of logical disjunction; an autoregressive decoder would instead impose an arbitrary clause order. Product T-norm relaxation makes rule execution differentiable, allowing end-to-end training on prediction accuracy alone. We evaluate NRI on rule recovery, robustness to label noise and spurious correlations, and zero-shot transfer to real-world benchmarks, and we believe this work opens up the possibility of foundation models for symbolic reasoning. Code and the reference checkpoint are available at https://github.com/phuayj/neural-rule-inducer. An extended version with full appendices is available at https://arxiv.org/abs/2605.04916.
The primary challenge in incremental learning for deep neural networks (DNNs) lies in balancing memory stability with learning plasticity as they incrementally acquire new knowledge. Existing incremental learning methods typically require storing portions of old datasets or relying on specialized network architectures to achieve learning without forgetting. Recent neuroscience findings reveal that hippocampal sharp-wave ripples play critical roles in consolidating neocortex memory by internally triggering the reactivation of spontaneous neural circuits without external stimulus. Inspired by this biological mechanism, we propose an incremental learning method termed Sharp-Wave Ripples Learning (SWRL), a novel incremental learning framework composed of two functionally complementary modules: a neocortex-like network for long-term memory and a hippocampus-like model for rapid new-knowledge encoding. SWRL continuously integrates newly acquired knowledge from the hippocampus-like module into the neocortex-like network via a meta-weighted memory integration mechanism guided by internally generated SWR-like signals. Our framework is task-agnostic, architecture-agnostic, and exemplar-free. Experimental results on multiple public benchmarks demonstrate that SWRL achieves superior incremental learning performance under various scenarios across diverse DNN architectures, requiring no access to previous data or modifications to the base architecture. The code and appendix are both released at https://github.com/SYVAE/SWRL.
Many practical decision-making problems involve tasks whose success depends on the entire system history, rather than on achieving a state with desired properties. Markovian Reinforcement Learning (RL) can be inadequate for such tasks, while modeling them as non-Markovian reward decision processes (NMRDPs) enables agents to handle temporal dependencies. Existing approaches offer limited formal guarantees on both (near-)optimality and sample efficiency. We address both issues with QR-max, a novel model-based algorithm for NMRDPs with discrete actions that factorizes Markovian transition learning from non-Markovian reward handling via reward machines. To our knowledge, this is the first model-based RL algorithm for discrete-action NMRDPs that leverages this factorization to obtain PAC convergence to ε-optimal policies with polynomial sample complexity. We then extend QR-max to continuous state spaces with Bucket-QR-max, a SimHash-based discretizer that preserves the same factorized structure and achieves fast and stable learning without manual gridding or function approximation. We experimentally compare our method with state-of-the-art model-based RL approaches on environments of increasing complexity, showing substantially improved sample efficiency and greater robustness in finding optimal policies.
The huge parameters and extensive training corpora of Large Language Models (LLMs) empower them to tackle complex tasks. Yet, their massive size incurs substantial inference costs, hindering deployment on resource‑constrained edge devices. Low‑bit quantization provides a way to compress models and improve their practicality for edge-side deployment. Nonetheless, many edge applications do not require the full generalization abilities of LLMs and instead only depend on their knowledge in specific domains. This paper introduces Gradient Enhancement Task Aware Post-training Quantization, i.e., GTAQ, to address the generalization issue. Concretely, GTAQ locates task‑critical weights via gradient‑based validation and retrieval, and then amplifies these salient weights. For that purpose, GTAQ preserves and strengthens weights related to the target tasks, enabling task‑aware uniform‑bit quantization. We extensively evaluate the LLaMA family of language models on WikiText, C4, and MMLU. Our experiments show that GTAQ consistently delivers notable performance improvements across tasks, surpassing both general‑purpose and task‑aware quantization baselines. At the same time, GTAQ yields more than 3.5× speedup in model inference and substantially cuts storage requirements. https://github.com/YihuaJerry/GTAQ.git.
Answer Set Programming (ASP) is a popular knowledge representation and reasoning framework with countless applications for modeling and solving combinatorial problems. With increasingly large applications, identifying crucial details and collapsing irrelevant or indistinguishable information becomes crucial for a human in the loop. This idea led to the investigation of different notions of abstraction, which relate to forgetting and projection. Very recently, clustering formalisms have been designed that also preserve program dependencies. However, crucial computational properties, such as finding abstractions, remained entirely unexplored to date. In this work, we examine the computational complexity of existing abstraction techniques based on clustering (faithful and uniform abstractions). Besides checking, we tackle the crucial question of constructing abstractions, by also proposing a novel syntactic operator to achieve uniform abstractions, when possible. Moreover, we investigate whether symmetry detection can be used to determine abstractable parts in a program, and give insights on the difference of interchangeability and indistinguishability, by introducing a relaxation of the uniform abstraction condition to capture semantic indistinguishability. We observe that this notion enables us to obtain intuitive faithful abstractions, while symmetry does not. We explore properties needed to reach abstractions from syntactic symmetry.
Existing multimodal recommendation models using complex fusion mechanisms (e.g., attention) or multi-stage processes (e.g., early or late fusion) integrate different modalities. However, attention-based adaptive fusion is prone to shortcut learning, where dominant collaborative signals (ID) can overshadow other modalities. This dominance affects the entire fusion process: early fusion often amplifies biases driven by identity, while late fusion struggles to extract preference-relevant signals from misaligned modalities, even with alignment regularization. To address these issues, we propose Multi-Teacher Single-Student Online Distillation for Multimodal Recommendation (MTS2-4MM), which reframes the multimodal recommendation task from direct fusion to controllable knowledge transfer. Specifically, we construct multiple teachers to specialize in complementary perspectives, and a unified student distills their guidance via objectives at both the ranking and representation levels. This design explicitly controls modality contributions, improves robustness to modality noise and misalignment. Furthermore, we design a Modality-specific Preference Extractor to explicitly extract user preferences across different modalities equally. Extensive experiments across five real-world datasets demonstrate that MTS2-4MM consistently outperforms state-of-the-art baselines, achieving improvements of up to 7.22%.
Cognitive diagnosis (CD) aims to infer students' mastery of knowledge concepts from their response behaviors and constitutes a core component of intelligent education and personalized learning. However, existing graph-based CD models struggle to handle the pronounced long-tail distributions in educational data, where most students and concepts interact with only a limited number of exercises, resulting in suboptimal representation learning and poor generalization to low-frequency instances. To address this challenge, we propose HiCD (Hyperbolic insight for Cognitive Diagnosis), a novel hyperbolic model that embeds students, exercises, and concepts into non-Euclidean space. By exploiting the exponential representational capacity of hyperbolic geometry, HiCD naturally captures hierarchical and sparse structures, effectively alleviating long-tail bias while enhancing embedding expressiveness. A key contribution of HiCD is a hyperbolic diagnostic function that operates directly on the manifold, avoiding Euclidean approximations and preserving geometric consistency. Moreover, HiCD decomposes the educational graph into three semantically distinct subgraphs and assigns each a dedicated curvature, enabling adaptive geometric modeling of heterogeneous relations. Extensive experiments on multiple benchmark datasets demonstrate that HiCD consistently improves diagnostic accuracy and robustness, particularly under severe long-tail scenarios. The source code is available at: https://github.com/CyberXie/HiCD.
In simulation-in-the-loop decision-making systems, reinforcement learning (RL) inference is often constrained by simulator-side execution overhead, where workloads are highly dynamic and sensitive to runtime thread configurations. Existing multithreaded strategies struggle to match thread resources before or during execution, causing resource contention, scheduling overhead, and reduced throughput. Through empirical analysis, we identify the ratio of task execution time to scheduling time as the key factor determining the optimal thread count. Building on this insight, we propose AutoThread, a hybrid adaptive thread-tuning method for mitigating simulation bottlenecks in RL inference. AutoThread employs a Physics-Informed Neural Operator (PINO) as a thread-count predictor and incorporates a finite-source M/M/1 queueing model to constrain and guide prediction, enabling fast and accurate estimation under dynamic workloads. It further performs load-aware online fine-tuning to compensate for prediction errors and refine resource allocation. Experiments show that AutoThread improves average speedup by 18.4% over static strategies, achieves average throughput of 1.7× and 1.8× that of XGBoost and Reinforcer, respectively, and reduces execution time by up to 83.8% compared with state-of-the-art methods. Our code and dataset are publicly available at https://github.com/suchenjm/AutoThread.
Federated Learning (FL) faces two major challenges when deploying large vision-language models such as contrastive language-image pretraining (CLIP) model, namely high computation/communication overhead and performance degradation due to data heterogeneity. To address these, we propose FedENC, a novel federated adaptive CLIP framework. FedENC keeps the CLIP encoder frozen and introduces a lightweight communicable global expert adaptation module for learning general knowledge across clients and two local experts for client-specific adaptation to reduce computation and communication overhead. To mitigate data heterogeneity, we introduce neural collapse (NC) alignment to encourage the model to learn unified and balanced image feature representations. To enrich the performance, we propose a cosine and Kullback–Leibler (KL) divergence based alignment function to improve the prediction consistency between the global and local experts. Finally, we ensemble their outputs for robust inference. Extensive experiments on six datasets (4 natural + 2 medical) in challenging settings (e.g., domain shift) show that FedENC improves the test accuracy (e.g., by +5.37% on DomainNet compared to LoRA) with reasonable communication load (e.g., 15.2x lower than LoRA, with 3.3GB GPU memory usage). The code is available at https://github.com/AIPMLab/FedENC.
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.
Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute–object compositions by leveraging shared knowledge from seen primitives. While recent works have begun to incorporate test-time adaptation to mitigate performance degradation caused by label-space distribution shifts, they suffer from primitive-level knowledge underutilization and cross-modal semantic asymmetry, hindering robust understanding of novel compositions. To address these issues, we introduce MLDA, a test-time adaptation framework for CZSL that jointly derives multi-level prototypes and performs dynamic vision–language alignment. Specifically, we construct and progressively update composition- and primitive-level prototype sets to accumulate structured semantic knowledge. By augmenting test inputs with diverse visual perspectives and enriched compositional descriptions, MLDA leverages optimal transport to achieve consistent matching of semantically salient information across modalities. Furthermore, composition activation scores are dynamically updated over the test stream to emphasize informative compositions and suppress irrelevant ones. Extensive experiments are conducted to verify the effectiveness of the proposed method. The code is available at https://github.com/keepgoingjkg/MLDA.
Federated optimization under data heterogeneity presents a significant challenge, often leading to suboptimal model performance. While numerous methods aim to replicate the ideal performance of centralized training, they frequently fall short in highly heterogeneous settings. In this paper, we introduce HaFedHo, an adaptive objective rectification method that harmonizes local training with the ideal data-centralized objective, requiring minimal modifications to the standard federated learning framework. HaFedHo operates by first decoupling the centralized objective and then employing a dynamic Taylor series expansion to accurately estimate the global objective for each client. Our theoretical analysis shows that the estimation error provably converges to zero as training progresses. Furthermore, extensive experiments on real-world datasets demonstrate that HaFedHo surpasses state-of-the-art methods, including SCAFFOLD, MimeLite, and FedDyn, in both test accuracy and communication efficiency. Notably, HaFedHo maintains its superior performance even with a client participation rate as low as $0.2\%$ in severely heterogeneous environments.
Knowledge distillation has become a prevalent technique for deploying efficient recommender systems, enabling lightweight student models to approximate the performance of larger teachers. However, we identify a critical issue: distillation systematically amplifies popularity bias, as student models inherit and intensify the popularity-driven shortcuts encoded in teachers trained on interaction data dominated by popular items. To address this limitation, we propose GUIDE (Geodesic aware Unbiased Instructive Distillation with Experts), a collaborative distillation framework that incorporates domain-specific debiasing experts alongside the global teacher. GUIDE tackles two key challenges in this paradigm. First, for expert routing, we introduce Spherical Expert Alignment, which conducts expert-student matching on the spherical manifold with geodesic distance optimization, eliminating magnitude-induced bias and ensuring stable gradient flow. Second, for context fusion, we design a Meta-Debiasing Gate that dynamically arbitrates teacher-expert influence based on real-time user-item context through end-to-end meta-learning. Extensive experiments on multiple real-world datasets demonstrate that GUIDE significantly mitigates popularity bias while preserving recommendation accuracy, with state-of-the-art trade-offs among efficiency, accuracy, and fairness. The code and data are available at:https://github.29com/zx19971219/GUIDE.
Hand Object Interaction (HOI) generation provides an efficient solution for virtual reality simulation and embodied AI deployment.Recent studies have explored instruction-driven HOI synthesis, yet they overlooked the fine-grained interactive contact and struggled with robust generalization in data-scarce scenarios. In addition, existing datasets are frequently constrained with limited object categories and monotonous motion patterns. To address these limitations, we first integrate a large-scale HOI dataset, namely HOI-X, which contains diverse objects, rich motion patterns, and part-level contact annotations. Along with HOI-X, we propose A3fford-HOI, a fine-grained and generalizable Hand Object Interaction framework with Anatomy-Aligned Affordance Disentanglement. Specifically, we define a Disentangle-2-Interact paradigm in A3fford-HOI. Considering the various interactions of distinct hand anatomical structures, in the Disentangle stage, we devise an Anatomy-Aligned Affordance Predictor, which leverages the external plausible physical priors in 3D multimodal large language models for affordance disentanglement prediction. Then, the disentangled affordance is employed into the Interact stage, i.e. Affordance Driven Interactor. The interactor integrates textual instructions, object geometric features, and the external knowledge enhanced (MLLMs) affordance via a Diffusion Transformer (DiT) for generalizable HOI generation. Extensive experiments demonstrate that A3fford-HOI maintains superior generation quality and contact plausibility for fine-grained and generalizable hand object interaction.
Recommendation system technology faces the fundamental challenge of data sparsity. Although incorporating auxiliary textual data works to enrich interactions, lengthy and unstructured item descriptions often fail to depict user interests, as they primarily describe item attributes. Furthermore, the sparse interactions provide incomplete interest patterns, leading to biased recommendations. To address these issues, this work proposes an LLM-summarized interest distillation (SID) model to enrich interactions by interest summarization and retrieval augmentation. Interest summarization leverages large language model to improve user interest descriptions and filters out interest-irrelevant details, generating compact, user-centric interest texts. Retrieval augmentation utilizes semantic neighbors as multiple teachers to adaptively transfer their multimodal collaborative knowledge to augment behaviorally sparse student users, thereby enriching interest learning for recommendation. It enhances interactions both qualitatively, through improved interest descriptions, and quantitatively, through expanded semantic neighbors. Extensive experiments show the superior performance of SID over the state-of-the-art models, demonstrating its effectiveness of interest summarization and retrieval augmentation to improve recommendations.
Time series forecasting is vital in diverse sectors such as energy and transportation, where non-stationary dynamics are deeply intertwined with external events in other modalities such as texts. However, incorporating natural language-based external events to improve non-stationary forecasting remains largely unexplored, as most approaches still rely on a single modality, resulting in limited contextual knowledge and model underperformance. Enabling fine-grained multimodal interactions between temporal and textual data is challenged by two fundamental issues: (1) the gap in modeling interactions among discrete external events and continuous time series in a unified framework; (2) classical uniform diffusion timestep ignores event-induced non-stationary variability, leading to imbalanced denoising difficulty across diffusion stages. In this work, we propose event-aware non-stationary time series forecasting EventTSF, an autoregressive diffusion framework that integrates historical time series and textual events via step-wise diffusion. To mitigate the imbalanced denoising difficulty of uniform timestep sampling, EventTSF uses an event-aware flow-matching timestep conditioned on event semantics. Extensive experiments on 7 synthetic and real-world datasets show that EventTSF outperforms 12 non-stationary time series forecasting baselines, achieving average gains of 41.3% in probabilistic forecasting and 27.5% in deterministic forecasting across all evaluation metrics.
Multimodal reasoning requires a path that retains integrity over a wide range of constraints, from visual grounding to logic consistency. However, the current Process Reward Models (PRMs) focus on heuristically defined rewards that equally weigh these factors, which may lead to the concealment of individual dimension failures (such as visual hallucinations) by the dominating factors, without guaranteeing the validity of the reasoning process in general. Therefore, to overcome the limitation, the paper proposes the concept of Multimodal Multi-Dimensional Scalarization Process Reward Modeling (MMS-PRM), a paradigm specifically developed to enforce the worst dimension’s robustness in multimodal reasoning. Specifically, a hierarchical fine-grained reward space is developed to represent the multimodal risks in the reasoning tasks, and a Chebyshev-based Monte Carlo Tree Search (MCTS) algorithm is introduced, in which the primary focus during the path searching is given to the worst-performing dimension. Moreover, a curriculum-based Direct Preference Optimization (DPO) approach is developed to gradually learn the balanced reasoning skills in the policy. The experimental results show that, without the dimension collapse issue, the MMS-PRM approach significantly improves the reliability of the multimodal reasoning performance and reaches competitive results in various challenging tasks. The code is available at https://github.com/leibniz-Man/MMS-PRM