Data-driven discovery of governing equations from data remains a fundamental challenge in nonlinear dynamics. Although sparse regression techniques have advanced system identification, they struggle with rational functions and noise sensitivity in complex mechanical systems. The Lagrangian formalism offers a promising alternative, as it typically avoids rational expressions and provides a more concise representation of system dynamics. However, existing Lagrangian identification methods are significantly affected by measurement noise and limited data availability. This paper presents a novel differentiable sparse identification framework that addresses these limitations through three key contributions: (1) the first integration of cubic B-Spline approximation into Lagrangian system identification, enabling accurate representation of complex nonlinearities, (2) a robust equation discovery mechanism that effectively utilizes measurements while incorporating known physical constraints, (3) a recursive derivative computation scheme based on B-spline basis functions, effectively constraining higher-order derivatives and reducing noise sensitivity on second-order dynamical systems. The proposed method demonstrates superior performance and enables more accurate and reliable extraction of physical laws from noisy data, particularly in complex mechanical systems compared to baseline methods.
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FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Models
PDF ↗Private data holds promise for improving LLMs due to its high quality, but its scattered distribution across data silos and the high computational demands of LLMs limit their deployment in federated environments. To address this, the transformer-based federated split models are proposed, which offload most model parameters to the server (or distributed clients) while retaining only a small portion on the client to ensure data privacy. Despite this design, they still face three challenges: 1) Peer-to-peer key encryption struggles to secure transmitted vectors effectively; 2) The auto-regressive nature of LLMs means that federated split learning can only train and infer sequentially, causing high communication overhead; 3) Fixed partition points lack adaptability to downstream tasks. In this paper, we introduce FedSEA-LLaMA, a Secure, Efficient, and Adaptive Federated splitting framework based on LLaMA2. First, we inject Gaussian noise into forward-pass hidden states to enable secure end-to-end vector transmission. Second, we employ attention-mask compression and KV cache collaboration to reduce communication costs, accelerating training and inference. Third, we allow users to dynamically adjust the partition points for input/output blocks based on specific task requirements. Experiments on natural language understanding, summarization, and conversational QA tasks show that FedSEA-LLaMA maintains performance comparable to centralized LLaMA2 and achieves up to 8× speedups in training and inference. Further analysis of privacy attacks and different partition points also demonstrates the effectiveness of FedSEA-LLaMA in security and adaptability.
Understanding enzyme thermal properties is essential for biotechnology and protein engineering, yet experimental measurements of attributes such as temperature optimum, stability, and range remain labor-intensive and costly. Prior studies have shown that specific regions within enzyme sequences disproportionately influence thermal behavior—an aspect often overlooked by existing deep learning models. In this work, we introduce PatchET, a biologically inspired deep learning model that predicts enzyme thermal properties directly from amino acid sequences. PatchET employs a dual-stage, patch-based architecture that captures both intra-patch local features and inter-patch global dependencies, reflecting the hierarchical nature of protein thermal adaptation. Alongside the model, we curate a comprehensive benchmark, including a refined dataset for temperature optimum and the first publicly available dataset for temperature range prediction. PatchET achieves state-of-the-art performance across three key tasks—temperature optimum, stability, and range—and serves as the first dedicated model for temperature range prediction. Extensive ablation studies further validate the effectiveness of our architectural design. Together, PatchET and the accompanying benchmark provide a unified and generalizable framework for modeling enzyme thermal properties, offering new tools for the rational design of thermostable enzymes.
Ordinal classification has been widely applied in many high-stakes applications, e.g., medical imaging and diagnosis, where reliable uncertainty quantification (UQ) is essential for decision making. Conformal prediction (CP) is a general UQ framework that provides statistically valid guarantees, which is especially useful in practice. However, prior ordinal CP methods mainly focus on heuristic algorithms or restrictively require the underlying model to predict a unimodal distribution over ordinal labels. Consequently, they provide limited insight into coverage–efficiency trade-offs, or a model-agnostic and distribution-free nature favored by CP methods. To this end, we fill this gap by propose an ordinal-CP method that is model-agnostic and provides instance-level optimal prediction intervals. Specifically, we formulate conformal ordinal classification as a minimum-length covering problem at the instance level. To solve this problem, we develop a sliding-window algorithm that is optimal on each calibration data, with only a linear time complexity in K, the # of label candidates. The local optimality per instance further also improves predictive efficiency in expectation. Moreover, we propose a length-regularized variant that shrinks prediction set size while preserving coverage. Experiments on four benchmark datasets from diverse domains are conducted to demonstrate the significantly improved predictive efficiency of the proposed methods over baselines (by 15%↓ on average over four datasets).
As machine learning (ML) applications grow increasingly complex in recent years, modern ML frameworks often need to address multiple potentially conflicting objectives with coupled decision variables across different layers. This creates a compelling need for multi-objective bilevel learning (MOBL). So far, however, the field of MOBL remains in its infancy and many important problems remain under-explored. This motivates us to fill this gap and systematically investigate the theoretical and algorithmic foundation of MOBL. Specifically, we consider MOBL problems with multiple conflicting objectives guided by preferences at the upper-level subproblem, where part of the inputs depend on the optimal solution of the lower-level subproblem. Our goal is to develop efficient MOBL optimization algorithms to (1) identify a preference-guided Pareto-stationary solution with low oracle complexity; and (2) enable systematic Pareto front exploration. To this end, we propose a unifying algorithmic framework called weighted-Chebyshev multi-hyper-gradient-descent (WC-MHGD) for both deterministic and stochastic settings with finite-time Pareto-stationarity convergence rate guarantees, which not only implies low oracle complexity but also induces systematic Pareto front exploration. We further conduct extensive experiments to confirm our theoretical results.
ADAPT: Adaptive Decentralized Architecture with Perception-Aligned Training for Structural Generalization in Multi-Agent RL
PDF ↗Multi-agent reinforcement learning (MARL) excels in cooperative and competitive tasks, but most architectures are tied to fixed input-output sizes and require retraining when the number of perceptible or controllable objects changes. While structural generalization techniques mitigate this, they rely on centralized training, raising concerns about scalability and privacy. We propose ADAPT, the first framework to support structural generalization under a decentralized training and decentralized execution (DTDE) paradigm. Every agent adopts an object-centric view, encoding each observed object into a feature vector and aggregating them into a variable-length set representation. To enable each agent to infer task-level contexts from this dynamic input independently, we propose a dynamic-consistency loss that enforces spatio-temporal alignment between context representations and observed environmental dynamics. Agents then condition their policies on the inferred contexts to make locally aligned decisions. For zero-shot transfer, we propose FINE (Foresight INdex for multi-agEnt), a metric that considers Q-value overestimation and enables cross-policy comparison of long-term impact, facilitating effective policy transfer. Experiments show that ADAPT surpasses existing DTDE methods and outperforms CTDE baselines in zero-shot generalization.
Multivariate time series forecasting is crucial across a wide range of domains. While presenting notable progress for the Transformer architecture, iTransformer still lags behind the latest MLP-based models. We attribute this performance gap to unstable inter-channel relationships. To bridge this gap, we propose EMAformer, a simple yet effective model that enhances the Transformer with an auxiliary embedding suite, akin to armor that reinforces its ability. By introducing three key inductive biases, i.e., global stability, phase sensitivity, and cross-axis specificity, EMAformer unlocks the further potential of the Transformer architecture, achieving state-of-the-art performance on 12 real-world benchmarks and reducing forecasting errors by an average of 2.73% in MSE and 5.15% in MAE. This significantly advances the practical applicability of Transformer-based approaches for multivariate time series forecasting.
Length-Adaptive Interest Network for Balancing Long and Short Sequence Modeling in CTR Prediction
PDF ↗User behavior sequences in modern recommendation systems exhibit significant length heterogeneity, ranging from sparse short-term interactions to rich long-term histories. While longer sequences provide more context, we observe that increasing the maximum input sequence length in existing CTR models paradoxically degrades performance for short-sequence users due to attention polarization and length imbalance in training data. To address this, we propose LAIN (Length-Adaptive Interest Network), a plug-and-play framework that explicitly incorporates sequence length as a conditioning signal to balance long- and short-sequence modeling. LAIN consists of three lightweight components: a Spectral Length Encoder that maps length into continuous representations, Length-Conditioned Prompting that injects global contextual cues into both long- and short-term behavior branches, and Length-Modulated Attention that adaptively adjusts attention sharpness based on sequence length. Extensive experiments on three real-world benchmarks across five strong CTR backbones show that LAIN consistently improves overall performance, achieving up to 1.15% AUC gain and 2.25% log loss reduction. Notably, our method significantly improves accuracy for short-sequence users without sacrificing long-sequence effectiveness. Our work offers a general, efficient, and deployable solution to mitigate length-induced bias in sequential recommendation.
The Contrastive Language-Image Pre-Training (CLIP) model excels in few-shot learning by aligning visual and textual representations. Our study shows that template-sample similarity (TSS), defined as the resemblance between a text template and an image sample, introduces bias. This bias leads the model to rely on template proximity rather than true sample-to-category alignment, reducing both accuracy and robustness in classification. We present a framework that uses empty prompts, textual inputs that convey the idea of “emptiness” without category information. These prompts capture unbiased template features and offset TSS bias. The framework employs two stages. During pre-training, empty prompts reveal and reduce template-induced bias within the CLIP encoder. During few-shot fine-tuning, a bias calibration loss enforces correct alignment between images and their categories, ensuring the model focuses on relevant visual cues. Experiments across multiple benchmarks demonstrate that our template correction method significantly reduces performance fluctuations caused by TSS, yielding higher classification accuracy and stronger robustness.
We study the problem of efficiently estimating policies that simultaneously optimize multiple objectives in reinforcement learning (RL). Given n objectives (or tasks), we seek the optimal partition of these objectives into k groups, which is much smaller than n, where each group comprises related objectives that can be trained together. This problem arises in applications such as robotics, control, and preference optimization in language models, where learning a single policy for all n objectives is suboptimal as n grows. We introduce a two-stage procedure — meta-training followed by fine-tuning — to address this problem. We first learn a meta-policy for all objectives using multitask learning. Then, we adapt the meta-policy to multiple randomly sampled subsets of objectives. The adaptation step leverages a first-order approximation property of well-trained policy networks, which is empirically verified to be accurate within a 2% error margin across various RL environments. The resulting algorithm, PolicyGradEx, efficiently estimates an aggregate task-affinity score matrix given a policy evaluation algorithm. Based on the estimated affinity score matrix, we cluster the n objectives into k groups by maximizing the intra-cluster affinity scores. Experiments on three robotic control and the Meta-World benchmarks demonstrate that our approach outperforms state-of-the-art baselines by 16% on average, while delivering up to 26 times faster speedup relative to performing full training to obtain the clusters. Ablation studies validate each component of our approach. For instance, compared with random grouping and gradient-similarity-based grouping, our loss-based clustering yields an improvement of 19%. Finally, we analyze the generalization error of policy networks by measuring the Hessian trace of the loss surface, which gives non-vacuous measures relative to the observed generalization errors.
Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss
PDF ↗The prevalence of real-world multi-view data makes incomplete multi-view clustering (IMVC) a crucial research. The rapid development of Graph Neural Networks (GNNs) has established them as one of the mainstream approaches for multi-view clustering. Despite significant progress in GNNs-based IMVC, some challenges remain: (1) Most methods rely on the K-Nearest Neighbors (KNN) algorithm to construct static graphs from raw data, which introduces noise and diminishes the robustness of the graph topology. (2) Existing methods typically utilize the Mean Squared Error (MSE) loss between the reconstructed graph and the sparse adjacency graph directly as the graph reconstruction loss, leading to substantial gradient noise during optimization. To address these issues, we propose a novel Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss (DGIMVCM). Firstly, we construct a missing-robust global graph from the raw data. A graph convolutional embedding layer is then designed to extract primary features and refined dynamic view-specific graph structures, leveraging the global graph for imputation of missing views. This process is complemented by graph structure contrastive learning, which identifies consistency among view-specific graph structures. Secondly, a graph self-attention encoder is introduced to extract high-level representations based on the imputed primary features and view-specific graphs, and is optimized with a masked graph reconstruction loss to mitigate gradient noise during optimization. Finally, a clustering module is constructed and optimized through a pseudo-label self-supervised training mechanism. Extensive experiments on multiple datasets validate the effectiveness and superiority of DGIMVCM.
Given a non-negative integer \ell, the k-median with outliers problem extends the standard k-median problem by allowing the removal of up to \ell points and minimizing the clustering cost over the remaining ones. Algorithmic development in this setting remains an active area of research due to its relevance in processing noisy data. In this paper, we present a sampling-based reduction from the k-median with outliers problem to its outlier-free counterpart. The reduction incurs a multiplicative overhead of (kℓ⁻¹ + ε⁻¹)^O(ℓ) in the running time: it yields (kℓ⁻¹ + ε⁻¹)^O(ℓ) outlier-free instances, a solution to one of which can be directly transformed into a solution to the original instance with an arbitrarily small loss in the approximation ratio. This improves upon the previously known reduction with an overhead of ((k + ℓ)ε⁻¹)^O(ℓ). As applications, we obtain faster fixed-parameter tractable (FPT) algorithms with tight approximation guarantees for the k-median with outliers problem under various metric spaces. Furthermore, our approach naturally generalizes to constrained variants of the problem where additional constraints are imposed on the cluster sizes, and yields similar improvements in their FPT approximations.
Active domain adaptation (ADA) aims to select a small set of target samples for annotation and use them for training to maximally boost the adaptation performance. However, most existing ADA methods only rely on the original output of the model, without considering the relationship between the source and target domain features, which may lead to selecting uninformative samples. In this paper, we propose an effective ADA framework: Prototype-Driven Active Domain Adaptation with density consideration (PDADA). It selects the most valuable target samples in the presence of domain shift through two criteria: Density-Conscious Domainness (DCD) and Prototype-Driven Informativeness (PDI). Furthermore, considering the class imbalance and cluster looseness issues in sample selection and domain adaptation, we develop a Class Balanced Expansion (CBE) algorithm and the Adversarial Active Domain Adaptation via Protecting Structured Information (AADA-PSI). Extensive experiments demonstrate that under the cooperation of the above components, PDADA outperforms previous methods on several challenging benchmarks and can be generalized to multi-source active domain adaptation setting.
The success of large language models (LLMs) in cognitive tasks prompts the question of whether their next-token prediction (NTP) paradigm can be adapted to model physiological signals from wearable devices. A key target for this adaptation is photoplethysmography (PPG), the most prevalent sensing modality in consumer wearables for non-invasive monitoring of diverse physiological conditions. Unlike in NLP, where NTP aligns with generative objectives, physiological signal analysis involves fundamentally different tasks, such as continuous parameter estimation (regression) and discrete state recognition (classification). This disparity creates a semantic mismatch between the pre-training paradigm and the downstream tasks. To bridge this gap, we propose PPGPT, the first foundation model that reformulates NTP into next-feature token prediction (NFTP), learning hierarchical feature transition probabilities to unify pre-training and downstream objectives. PPGPT features a novel dual-stream encoder that generates feature tokens by jointly modeling temporal dynamics and local-global morphological patterns. The model is developed using a two-stage training framework: it is first pre-trained on a large-scale mixed dataset of 1.6 billion data points and then validated on our newly released BioMTL benchmark, which includes data from 172 subjects over 285 days across seven different tasks. Extensive experiments show that PPGPT significantly outperforms competing methods, achieving a 16.5% improvement in F1-score and a 25.9% reduction in Mean Absolute Error (MAE). Furthermore, the model demonstrates robust few-shot learning capabilities.
Embedding-based generalized zero-shot learning (GZSL) models often first forge robust latent semantic correlations between visual and attribute features so that knowledge can generalize to unseen categories. Despite leveraging attributes as priors and learning a shared embedding space, current methods exhibit two critical flaws. First, attributes with heterogeneous granularity are treated uniformly, leading to semantic ambiguity. Second, the source of class-level misclassification seldom aligns with attribute-level errors, preventing models from targeting the specific attributes responsible. To overcome these limitations, we introduce Structured Attribute-Guided Enhancement (SAGE), a unified framework for GZSL. Consensus-aware bidirectional attention first synchronizes visual–semantic focus regions via a mutual-distillation scheme. Next, we partition all attributes into pairwise-disjoint subsets—Global, Context, and Local—and couple them with visual features extracted at matching spatial scales. Finally, we design a cross-sample, subset-aware distillation mechanism—when a sample is misclassified, SAGE identifies the culpable attribute subset, retrieves high-confidence prototypes from a memory bank, and applies a Kullback–Leibler (KL) divergence constraint to the corresponding feature branch. Comprehensive experiments and ablations on the challenging AwA2, CUB, and SUN benchmarks demonstrate the contribution of each component, with SAGE achieving a new state-of-the-art throughout. These findings underscore SAGE’s robustness and versatility, marking a substantial advance in generalized zero-shot learning and paving the way for broader zero-resource recognition.
Pseudo-Spiking Neurons: A Noise-Based Training Framework for Heterogeneous-Latency Spiking Neural Networks
PDF ↗Spiking Neural Networks (SNNs) promise significant energy efficiency by processing information via sparse, event-driven spikes. However, realizing this potential is hindered by the conventional use of a rigid, uniform timestep, T. This constraint imposes a challenging trade-off between accuracy and latency, while also incurring the prohibitive training costs of Backpropagation Through Time (BPTT). To overcome this limitation, we introduce the Pseudo-Spiking Neuron (PseudoSN), a novel training proxy that conceptualizes latency as an intrinsic, learnable parameter for each neuron. Building on the efficiency of rate-based methods, the PseudoSN models temporal dynamics in a single, BPTT-free pass. It employs a learnable probabilistic noise scheme to emulate the discretization effects of spike generation (e.g., clipping and quantization), making the neuron-specific timestep—and thus latency—directly optimizable via backpropagation. Integrated into a hardware-aware objective, our framework trains heterogeneous-latency SNNs that autonomously learn to optimize the trade-offs among accuracy, latency and energy, establishing a new state-of-the-art on major benchmarks.
Knowledge distillation from Artificial Neural Networks (ANNs) to Spiking Neural Networks (SNNs) is a prominent training paradigm. However, its efficacy is fundamentally limited by a spectral mismatch: SNNs, with their intrinsic low-pass filtering characteristics, struggle to learn high-frequency details from their ANN teachers, creating a bottleneck in knowledge transfer at both the feature and logit levels. To address this, we propose Bi-Spectrum Distillation (BSD), a novel framework that mitigates the mismatch from two complementary perspectives. First, at the feature level, our Spectral Residual Distillation (SRD) enhances the student SNN's features with a parameter-efficient, learnable filter that adaptively compensates for high-frequency information loss, which transforms the student's output to better match the teacher's rich spectral target. Second, at the logits level, our Spectral Semantic Distillation (SSD) enhances fine-grained classification by distilling high-frequency components from teacher-ordered logits. Extensive experiments on CIFAR-10/100, ImageNet, and CIFAR10-DVS demonstrate that BSD achieves new state-of-the-art performance across both CNN and Transformer-based SNNs, validating its effectiveness and broad applicability.
Parameter-efficient transfer learning (PETL) has emerged as a pivotal paradigm for adapting pre-trained foundation models to downstream tasks, significantly reducing trainable parameters yet suffering from substantial memory overhead caused by gradient backpropagation during fine-tuning. While memory-efficient transfer learning (METL) circumvents this challenge by bypassing backbone gradient computation via lightweight small side networks, its stringent memory constraint severely limits learning capacity of side networks, thereby significantly compromising performance. To address these limitations, we propose a novel Mixed-Precision Interactive Side Mixture-of-Experts framework (MP-ISMoE). Specifically, we first propose an Gaussian Noise Perturbed Iterative Quantization (GNP-IQ) scheme to quantize weights into lower-bits while effectively decreasing quantization errors. By leveraging memory conserved from GNP-IQ, we subsequently employ Interactive Side Mixture-of-Experts (ISMoE) to scale up side networks without sacrificing overall memory efficiency. Different from conventional mixture-of-experts, ISMoE learns to select optimal experts by interacting with salient features from frozen backbones, thus suppressing knowledge forgetting and boosting performance. Extensive experiments across diverse vision-language and language-only tasks demonstrate that MP-ISMoE remarkably promotes accuracy compared to state-of-the-art METL approaches, while maintaining comparable parameter and memory efficiency.
Model-heterogeneous federated tuning (MHFT) enables the privacy-preserving fine-tuning of foundation models in heterogeneous systems by allowing clients and the server to adopt different model architectures. Depth partial training—where each client updates only a subset of the model's layers—alleviates system heterogeneity but exacerbates client drift, which stems from clients optimizing different objectives and therefore degrades overall performance. Beyond the well-known statistical bias—where non-IID data leads to client drift—we identify a structural bias arising from clients deploying only partial layers of the global model, which serves as an important cause of drift. We further provide a theoretical analysis showing that the possible range of structural bias expands linearly with the number of missing layers. To counter this effect, we introduce FedBRICK (Federated Bias Recovery via Inserted Calibrative Kernels), which inserts tiny BRICKs into each client’s subnetwork. We employ a dual-end layer-wise distillation scheme to train these blocks using both client-side local data and a small public proxy set on the server. This design effectively mitigates the structural bias caused by layer dropping, reduces client drift, and remains practical for storage-constrained devices. Extensive experiments on federated learning benchmarks confirm that FedBRICK delivers up to a 5% average accuracy gain while requiring no more than 1.44% extra storage per client.
Mamba-Driven Multi-View Discriminative Clustering via Global-Local Cross-View Sequence Modeling
PDF ↗Multi-view clustering (MVC) has recently garnered increasing attention for its ability to partition unlabeled samples into distinct clusters by leveraging complementary and consistent information from different views. Existing MVC methods primarily combine deep neural networks with contrastive learning for cross-view representation learning, yet often overlook the inherent global-local structural relationships among samples. While GNN-based methods capture local structures, they struggle to model global dependencies, leading to inferior inter-cluster separability. In contrast, Transformer-based methods excel at global aggregation but suffer from quadratic complexity, and their attention smoothing effect weakens fine-grained local structures, resulting in suboptimal intra-cluster compactness. To address these limitations, we propose a novel end-to-end MVC framework called Mamba-Driven Multi-View Discriminative Clustering via Global-Local Cross-View Sequence Modeling (MGLC). By flexibly constructing multi-view sequences, MGLC fully exploits the efficient sequence modeling capabilities of Mamba to jointly model cross-view dependencies and global-local structural relationships among samples. Furthermore, MGLC introduces a Cross-Mamba Fusion module to dynamically integrate cross-view and global-local structural representations. Additionally, MGLC incorporates a Dual Calibration Contrastive Learning module, guided by high-confidence pseudo-labels, that adaptively refines both feature and semantic representations while mitigating false negatives among semantically similar samples. Extensive comparative experiments and ablation studies demonstrate the effectiveness of MGLC.