2128 papers
This paper introduces an open-source framework for Graph Neural Network-based Time Series Anomaly Detection to enable reproducible experimentation and critical evaluation, demonstrating that GNNs enhance both detection performance and interpretability while highlighting the need for standardized metrics and thresholding strategies.
The paper introduces EsoLang-Bench, a novel benchmark utilizing esoteric programming languages to expose the limitations of large language models' genuine reasoning capabilities by revealing a dramatic performance gap between their high scores on standard benchmarks and near-zero accuracy on tasks requiring the acquisition of new languages through documentation and experimentation rather than memorization.
This paper empirically demonstrates that catastrophic forgetting in low-rank decomposition-based parameter-efficient fine-tuning is primarily driven by update subspace geometry, revealing that tensor-based and structurally aligned methods outperform traditional shared matrix approaches in sequential learning scenarios.
The paper introduces ActiveUltraFeedback, an efficient active learning pipeline that leverages uncertainty estimates and novel selection strategies like Double Reverse Thompson Sampling to generate high-quality preference data, enabling Large Language Models to achieve superior alignment performance with as little as one-sixth of the annotated data required by static baselines.
This paper introduces PF-PINO, a physics-informed neural operator framework that embeds phase-field governing equation residuals into the training loss to significantly improve the accuracy, generalization, and long-term stability of predictive parametric phase-field modelling compared to conventional methods.
Mousse is a novel optimizer that improves upon the Muon algorithm by integrating Shampoo's Kronecker-factored preconditioning to adaptively handle the heavy-tailed curvature of deep neural networks, thereby achieving faster training convergence with negligible computational overhead.
This paper proposes a Multi-Prototype-Guided Federated Knowledge Distillation (MP-FedKD) approach for AI-RAN enabled Multi-Access Edge Computing systems, which addresses non-IID data challenges and mitigates information loss from single-prototype averaging by integrating self-knowledge distillation, a conditional hierarchical agglomerative clustering strategy, and a novel loss function to outperform state-of-the-art baselines in accuracy and error metrics.
This paper derives upper PAC generalization bounds for neural oscillators based on second-order ODEs and MLPs, demonstrating that their estimation errors grow polynomially with model size and time while showing that constraining MLP Lipschitz constants via regularization enhances generalization performance in modeling nonlinear structural systems.
This paper establishes global universal approximation theorems for path-dependent functionals on spaces of piecewise linear paths using linear functionals of discrete-time signatures, demonstrating their applicability to Brownian motion-driven systems such as random and stochastic ordinary differential equations.
This paper identifies that deep neural networks frequently encode the absence of concepts to drive neuron activation—a phenomenon largely overlooked by standard explainable AI methods—and proposes simple extensions to attribution and feature visualization techniques to effectively reveal and leverage these "missing" concepts for better model interpretation and debiasing.
This paper proposes and validates a hybrid quantum-classical framework that integrates a Long Short-Term Memory (LSTM) network with a Quantum Circuit Born Machine (QCBM) to significantly improve financial volatility forecasting accuracy on high-frequency stock market data compared to traditional classical models.
This paper proposes ACP-SL, an adaptive channel pruning scheme for Split Learning that utilizes a label-aware channel importance scoring module to compress smashed data, thereby significantly reducing communication overhead while improving test accuracy and training efficiency.
This paper introduces -GaBO, a novel family of Bayesian optimization algorithms that leverages information geometry to construct Matérn kernels and geometric optimizers tailored for the probability simplex, demonstrating superior performance over constrained Euclidean approaches in optimizing mixtures and robotic control tasks.
This paper introduces In-Context RLVR, a method that leverages a model's own in-context learning ability to measure "Demonstration Utility" via Evidence Gain, thereby implicitly reweighting rewards to prioritize high-quality reasoning traces over merely correct but flawed solutions during Reinforcement Learning with Verifiable Rewards training.
This paper introduces the smoothing pseudo-projector, a lightweight, multigrid-inspired module that corrects hidden representations in transformer-based models to suppress noise from label-irrelevant inputs, thereby improving training dynamics and robustness without altering the core architecture.
This paper proposes a novel hierarchical multi-task multi-fidelity (H-MT-MF) framework for Gaussian process-based surrogate modeling that unifies inter-task information sharing and fidelity-dependent uncertainty handling to significantly improve prediction accuracy and data efficiency in manufacturing systems with heterogeneous data sources.
This paper introduces HR-GAT, a hierarchical resolution graph attention network that leverages geospatial data to predict spectrum demand with 21% higher accuracy than baseline models, effectively addressing spatial autocorrelation challenges to enable more efficient spectrum sharing and policy-making.
The paper proposes GAST, a novel Parameter-Efficient Fine-Tuning method that unifies data-layer selection and layer-sparse strategies to adaptively match impactful data points with specific model layers, thereby overcoming the limitations of existing single-dimension approaches and achieving superior performance.
The paper introduces CarbonBench, the first standardized benchmark comprising over 1.3 million global observations from 567 sites, designed to rigorously evaluate and compare zero-shot spatial transfer learning methods for upscaling terrestrial carbon fluxes across diverse, unseen ecosystems and climate regimes.
The paper proposes MSSR, a memory-aware adaptive replay framework that estimates sample-level memory strength to dynamically schedule rehearsal intervals, effectively mitigating catastrophic forgetting while maintaining fast adaptation in continual LLM fine-tuning.