Null-space-based methods have garnered considerable attention in model editing by constraining updates to the null space of the pre-existing knowledge representation, thereby preserving the model's original behavior. However, in practice these methods rely on an approximate null space—leading to knowledge leakage—and further suffer from severe performance degradation during sequential editing. Recent work shows that history-aware editing strategies can empirically mitigate this decline, yet the underlying reason remains unclear. In this paper, we first expose the knowledge leakage inherent in existing null-space approaches and then analyze why history-aware updates effectively preserve both editing performance and general capabilities during long-horizon editing. Building on these insights, we propose BetaEdit, a refined framework that effectively controls the knowledge leakage and integrates history-aware updates into the null-space paradigm. Extensive experiments on three large language models across two standard benchmarks show that BetaEdit consistently outperforms prior methods in the challenging regime of massive-scale sequential editing. Code is available at: https://github.com/lbq8942/BetaEdit.
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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.
It has been shown that a neural network's Lipschitz constant can be leveraged to derive robustness guarantees, to improve generalizability via regularization or even to construct invertible networks. Therefore, a number of methods varying in the tightness of their bounds and their computational cost have been developed to approximate the Lipschitz constant for different classes of networks. However, comparatively little research exists on methods for exact computation, which has been shown to be NP-hard. Nonetheless, there are applications where one might readily accept the computational cost of an exact method. These applications could include the benchmarking of new methods or the computation of robustness guarantees for small models on sensitive data. Unfortunately, existing exact algorithms restrict themselves to only ReLU-activated networks, which are known to come with severe downsides in the context of Lipschitz-constrained networks. We therefore propose a generalization of the LipBaB algorithm to compute exact Lipschitz constants for arbitrary continuous piecewise linear neural networks and p-norms. With our method, networks may contain traditional activations like ReLU or LeakyReLU, activations like GroupSort or the related MaxMin and FullSort, which have been of increasing interest in the context of Lipschitz-constrained networks, or other piecewise linear functions like MaxPool.
Zero-shot transfer for offline reinforcement learning involves generalizing to a wide range of tasks that are often risk-sensitive, without new interaction. We show here that although Successor Features (SFs) provide a principled framework for transfer through abstracting dynamics from rewards, their typical form is restricted to expected cumulative returns and is thus indifferent to variance in returns. We contend that such a mean-centric approach introduces bias by conflating risky and safe trajectories with identical expected outcomes, as it ignores outcome dispersion critical for risk-aware evaluation. Additionally, we note that distributional methods can be employed to model outcome distributions, but frequently do so at a high computational cost due to the need for discretization or elaborate sampling. To overcome this problem, we introduce a form of successor representation that incorporates an uncertainty envelope over states: Gaussian Distributional Successor Features (GDSF). Rather than inferring an implicit generative process, GDSF learns to approximate the distribution of cumulative outcomes with a Gaussian through recursive moment-matching. We present this framework for efficient representation of aleatoric uncertainty, without resorting to computationally expensive existing distributional methods. We also introduce Latent Outcome Optimization, an inference procedure which finds high-reward outcomes in the data support and drives a goal-conditioned policy. This dual-mode design allows the agent to transfer to non-linear objectives in a zero-shot way while preserving closed-form efficiency for linear tasks. Experiments on the D4RL benchmark show that GDSF yields better performance in risk-sensitive tasks while achieving at least competitive results in standard linear regimes.
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.
Cross-view Referring Expression Comprehension (REC) requires a model to accurately localize objects in a spatial representation of one perspective based on descriptions generated from a different perspective. Essentially, it demands maintaining referential consistency under perspective shifts. This problem is prevalent in engineering application scenarios (e.g., Geographic Information Systems (GIS), urban planning, etc), where descriptions are typically generated from a top-down or planning view, while targets must be identified in a spatial representation distinct from the source. However, existing methods often assume shared perspectives or rely on an explicit observer to interpret observer-centric spatial relations (e.g., "left/right" of the observer). Consequently, they struggle to handle cross-view grounding cases commonly arising in engineering application scenarios, where descriptions are defined by the source-view representation itself and do not involve any observing subject. To this end, we propose a novel Subject-Agnostic Cross-View REC task and construct a controlled simulation environment to systematically study referential consistency under perspective changes. Methodologically, we propose a Reward-Driven Consistency Learning framework. By imposing mutually constraining reward signals on the grounding and localization results between the source and target views, we guide the model to maintain referential consistency across different view representations. Experimental results show that our method significantly outperforms baselines in multiple cross-view settings, exhibiting superior generalization and robustness under perspective shifts.
Retrieval-Augmented Generation (RAG) is sensitive to the vast hyperparameters of the retriever and generator, yet optimizing them using given queries remains a challenging task due to the complex hyperparameter interactions and expensive evaluation costs. Existing algorithms are ineffective and slow in convergence, since they often treat RAG as a monolithic black box or only optimize partial hyperparameters. In this paper, we propose CDS4RAG, a framework that optimizes the full hyperparameters of RAG using given queries via a new formulation of cyclic dual-sequential problem. CDS4RAG is special in the sense that it distinguishes the hyperparameters of the retriever and generator, optimizing them in turn and in a cyclic manner. Such a paradigm allows us to design fine-grained within-cycle budget provision and expedite the optimization via cross-cycle seeding when optimizing the generator. Importantly, CDS4RAG is an algorithm-agnostic framework that can be paired with diverse general algorithms. Through experiments on four common benchmarks and two backbone LLMs, we reveal that CDS4RAG considerably boosts the vanilla algorithms in 21/24 cases while significantly outperforming state-of-the-art algorithms in all cases with up to 1.54x improvements of generation quality and better speedup.
In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks with considerable performance gains, yet its effectiveness is highly sensitive to the choice of demonstrations. Most existing selection methods rely on heuristic or proxy signals, such as similarity, diversity, or uncertainty, and select demonstrations independently, which may misalign with downstream performance and overlook set-level composition effects. Therefore, we propose Performance-Driven Demonstration Selection (PDDS), which directly aligns demonstration selection with ICL performance. PDDS formulates selection as predicting the target LLM’s downstream task performance for a given query–in-context pair, replacing proxy heuristics with a performance-aware objective. Unlike prior approaches that rank demonstrations independently before composing the prompt, PDDS evaluates the entire in-context set, capturing inter-demonstration interactions and composition effects. PDDS trains an end-to-end scorer using supervision from the target LLM’s actual task outcomes. At inference time, it selects high-scoring in-context sets without additional target LLM calls or validation-time feedback. Across 6 NLP tasks, 8 datasets, and 8 target LLMs ranging from 1B to 70B parameters, PDDS achieves state-of-the-art results and generalizes well across LLMs and datasets. PDDS remains effective with as few as 30 training instances and can be integrated as a plug-and-play component to enhance existing ICL methods.
Sleep staging is pivotal for assessing sleep quality and clinical diagnostics. While contactless radio-frequency (RF) sensing offers an unobtrusive alternative to Polysomnography (PSG), existing methods often struggle with generalization across diverse devices and environments due to the scarcity of annotated RF data. To overcome this limitation, we propose XSensorSleep, a cross-sensor domain generalization framework. Uniquely, XSensorSleep is trained exclusively on large-scale respiratory datasets from chest and abdominal belts and is directly transferable to RF-derived respiratory signals. To bridge the domain gap, we partition the training data into multiple pseudo-domains based on data sources and sensor types, forcing the model to extract domain-invariant features from heterogeneous respiratory patterns. Furthermore, we introduce a hierarchical alignment strategy: Epoch-Level Feature Alignment (ELFA) suppresses sensor-specific morphological artifacts, while Spectral Temporal Alignment (STA) captures invariant global sleep architectures. Specifically, STA leverages the rotation-invariance of eigenvalue spectra to align whole-night transition dynamics, ensuring the model prioritizes universal physiological rhythms over device-dependent signal fluctuations. Extensive evaluations across various home and clinical RF datasets demonstrate that XSensorSleep achieves superior generalization, enabling practical, automated, and label-free non-contact sleep staging.
This paper addresses the problem of learning to sparsify stochastic linear bandits, where a decision-maker sequentially selects actions from a high-dimensional space subject to a sparsity constraint on the number of nonzero elements in the action vector. The key challenge lies in minimizing cumulative regret while tackling the potential NP-hardness of finding optimal sparse actions due to the inherent combinatorial structure of the problem. We propose an adaptively phased exploration and exploitation algorithmic framework, utilizing ordinary least squares for parameter learning and specialized subroutines for sparse action selection. When the action set is a Euclidean ball, optimal sparse actions can be efficiently computed, enabling us to establish a Õ(d √T) regret, where d is the dimension of the action vector and T is the time horizon length. For general convex and compact action sets where finding optimal sparse actions is intractable, we employ a greedy subroutine. For general strongly convex action sets, we derive a Õ(d √T) α-regret; for general compact sets lacking strong convexity, we establish a Õ(d T^(2/3)) α-regret, where α pertains to the approximation ratio of the greedy algorithm. Finally, we validate the performance of our algorithms using extensive experiments.
Single-cell perturbation screens enable systematic discovery of gene regulatory mechanisms, yet the exponential expansion of perturbation space makes comprehensive experimentation impractical. Although in silico predictors have been increasingly proposed to address this challenge, most existing methods either assume static regulatory priors or learn unconstrained data-driven representations. As a result, they often suffer from poor adaptation to state-dependent shifts and weak out-of-distribution generalization. In response, we propose URFPert, an unrolled regulatory flow network tailored for single-cell perturbation prediction. Specifically, URFPert is composed of three fundamental modules: (i) Regulatory Structure Unrolling module, which employs an iterative unrolling optimization algorithm to infer state-dependent gene regulatory networks (GRNs); (ii) Heterogeneous Graph Flow Network, which incorporates the inferred GRNs into a conditional velocity field to yield state-consistent and perturbation-specific trajectories; (iii)Discriminative Optimization Strategy, which combines Mean Squared Error~(MSE) with Maximal Coding Rate Reduction~(MCR²) to promote discriminative perturbation responses and mitigate training confounders. Comprehensive evaluations on five single-cell perturbation datasets demonstrate that URFPert outperforms state-of-the-art methods in unseen perturbation regimes, providing a powerful tool for interpreting regulatory mechanisms. The project code is public via https://github.com/Jiniretttt/URFPert.
Masked autoencoder have demonstrated excellent performance in point cloud representation learning and received widespread attention. However, due to the unstructured nature of point clouds, existing methods struggle to effectively model both local geometric information and global topological features, while generally neglecting positional information, resulting in insufficient model representational capacity. To address these challenges, we propose a Position-Assigned Graph Masked Autoencoder (PA-GMAE) framework for point cloud representation learning. Specifically, we first explicitly transform the unstructured point clouds into a graph structure and assigns it positional information, leveraging the structured properties of graph to mine the intrinsic correlations among points. During the encoding stage, global contextual are dynamically injected into node features, endowing the model with capabilities of global awareness. In the decoding stage, collaborative work across multiple decoding tasks fully captures the global topology and positional dependencies of the point cloud, enhancing the quality of learned representations. Extensive experiments are conducted on three authoritative benchmark datasets, namely ScanObjectNN, ModelNet40, and ShapeNetPart, across typical downstream tasks including point cloud classification and part segmentation, the results demonstrate the effectiveness and superiority of the proposed framework.
Inductive knowledge graph reasoning (IKGR) requires generalization to entities and relation types not seen during training. We introduce Knowledge Graph Wave Networks (KGWN), an IKGR approach that learns adaptive per-relation wave operators to control relation-dependent propagation, along with velocity normalization ensuring stability at any propagation depth. Experiments demonstrate state-of-the-art results on inductive benchmarks and competitive transductive performance.
Recent personalized federated learning research focuses on heterogeneous models across clients. However, existing methods often rely on external data, model decoupling, and partial learning, which makes them sensitive to settings. In contrast, we revisit hypernetworks and leverage their strong generalization ability to propose the first practical method for personalized federated learning. We first propose a model heterogeneous personalized federated learning framework based on hypernetworks, MH-pFedHN, which quantifies clients with different architectures using customized embedding vectors and then generates client-specific model parameters through a server-side hypernetwork. Besides the shared feature extractor, our hypernetwork consists of multiple heads, where clients with similar numbers of parameters are assigned the same number of customized embedding vectors and consequently share the same head. This design enables knowledge sharing across different architectures and reduces the computation of parameter generation. To further enhance the hypernetwork’s learning and generalization, we propose MH-pFedHNGD, which introduces a lightweight yet effective plug-in global model. Our framework requires no external data and does not disclose client model architectures, thereby effectively ensuring security and demonstrating great potential. Experiments across various models and tasks demonstrate that our approach outperforms standard baselines and exhibits strong generalization performance. Our code is available at https://github.com/DangDang1895/MH-pFL
Medication recommendation plays a critical role in clinical decision-making by supporting personalized and safe treatment planning. Existing methods rely heavily on historical co-occurrence patterns and primarily optimize discrete prescription prediction objectives, limiting generalization in rare or emerging disease settings. We propose MGRec, a framework that shifts learning from discrete prescription prediction to condition-aware modeling in a continuous molecular representation space. MGRec treats molecular structure as the primary modeling target and employs a Therapeutic--Safety Factorized Conditional Variational Autoencoder to disentangle therapeutic and safety-related factors in a condition-aware molecular latent space. The model infers treatment-relevant molecular representations conditioned on current patient-specific clinical context, which are mapped to clinically approved medications for final recommendation. To improve clinical safety, we further introduce a DDI (drug-drug interaction)-guided latent regularization to integrate drug interaction knowledge at the representation level. Experiments on two real-world benchmarks demonstrate that MGRec achieves state-of-the-art accuracy and reduced interaction risk, particularly in data-sparse scenarios.
Fairness k-submodular maximization has attracted increasing interest due to its broad relevance in artificial intelligence and machine learning. However, most existing works are limited to monotone objectives or simple size constraints, while non-monotone settings with richer constraints remain largely unexplored. In this paper, we first introduce a constant-factor approximation algorithm for the problem with a general non-monotone objective function under a matroid constraint. Our approach is built upon a two-stage algorithmic framework. Specifically, we first develop an algorithm that guarantees feasibility with respect to upper fairness bounds only. We then show how this algorithm can be systematically extended to simultaneously enforce fairness bounds, while preserving provable approximation guarantees. Comprehensive experiments on standard benchmark datasets demonstrate that our algorithm achieves competitive objective values while maintaining a favorable balance between fairness guarantees and query complexity efficiency compared to existing state-of-the-art methods.
As graph repositories grow in scale and diversity, training Graph Neural Networks (GNNs) becomes computationally demanding. However, existing graph condensation methods often fail to retain the intrinsic structural patterns of the original graphs, which are essential in graph-based learning. Therefore, these methods suffer from limited performance and poor generalization in downstream tasks due to the loss of structural information. To address this, we propose Similarity-guided Structural Matching Learning for Graph Dataset Condensation (SSGDC), which efficiently reduces repository size while maintaining both task performance and structural information. Our approach introduces a similarity-based graph selector to identify high-quality subsets for condensation. The condensed graphs are optimized using a dual-objective loss that combines gradient-matching for task alignment with a metric-learning loss for structural preservation within the selected subset. This ensures that the condensed dataset retains both task-relevant information and the essential relational topology that supports GNN training and enhances generalization. Experiments demonstrate that our method achieves higher accuracy and better structure retention across varying condensation ratios, highlighting the critical role of structural preservation in graph dataset condensation.
Supervised infrared-visible image fusion (IVIF) often overfits limited training distributions, creating a critical generalization gap under open-world degradations (rain, haze, low light, noise, blur). To address this issue, we propose AIR-Fusion, a parameter-efficient adaptation of a frozen, restoration-capable latent diffusion backbone for degraded IVIF without full fine-tuning, transferring restoration priors for robust fusion. A Cross-Modal Bridging Adapter (CMBA) aligns infrared cues and textual instructions with the frozen diffusion conditioning space and injects them into multi-scale denoising features to steer instruction-guided restoration-aware fusion. In addition, a Trajectory-Constrained Rectifier (TCR) regularizes stochastic sampling via a pixel-latent closed loop, rectifying intermediate predictions with source-referenced structures and re-encoding them to stabilize the denoising trajectory and recover fine details suppressed by latent compression. Experiments across multiple datasets and degradation settings show consistent improvements in restoration quality and fusion fidelity, with strong generalization under complex and compounded degradations.
Accurate estimation of time-varying treatment effects is crucial for optimizing interventions in personalized medicine. However, observational data often contains complex confounding bias and temporal complexities, making counterfactual estimation challenging. We propose Counterfactual Estimation via Temporal-Aware Intervention Networks (TAIN), a novel model that introduces an Intervention-aware Functional Convolution kernel to emphasize the role of treatments and capture complex temporal treatment interactions. TAIN addresses confounding bias from a domain generalization perspective, approximating the unknown target domain using adversarial examples and incorporating Sharpness-Aware Minimization to derive a generalization bound. This approach is more suitable for longitudinal settings compared to existing methods inspired by domain adaptation techniques due to inherent differences between static and longitudinal contexts. Experiments on simulated datasets demonstrate TAIN's superior performance compared to state-of-the-art models for counterfactual estimation over time.
We introduce QUINTIC, a general algorithm for actively learning quantitative automata from preferences. Quantitative automata evaluate input sequences by applying a valuation function---such as sum, product, or average---to the output labels of the states visited. Such models naturally arise in use cases ranging from probabilistic verification, sequence classification, and sequential decision making. However, existing learning approaches, such as variants of L*, weighted‐automata learning algorithms, and active learning preference‐driven methods, either assume finite output alphabets or restrict the valuation function to particular forms. QUINTIC utilizes a symbolic observation table and applies deductive reasoning with the assistance of an SMT solver to identify the correct minimal state, transition, and state label combination of the quantitative automaton. The deductive reasoning relies on the minimal combination of theories determined by the valuation function and output alphabet. Consequently, QUINTIC has completeness, minimalism, and query complexity guarantees, and learns quantitative automata across finite, integer, and rational outputs. Our extensive experiments show how QUINTIC scales under weak or strong feedback, and alternative MaxSMT objectives.