2315 papers
This paper proposes a world model framework for lithium-ion battery degradation prognosis that encodes cycle data into latent states and propagates them forward using learned dynamics, demonstrating that iterative rollout significantly reduces trajectory forecast error compared to direct regression while a Single Particle Model constraint specifically enhances prediction accuracy at the degradation knee.
This paper presents a Multi-Agent Reinforcement Learning framework using Proximal Policy Optimization to coordinate UAV fleets for time-critical medical supply delivery, demonstrating that classical PPO outperforms asynchronous and sequential strategies in dynamically prioritizing tasks and reallocating resources under uncertain conditions using real-world geographic data.
This paper introduces Group Relative Reward Rescaling (GR), a novel reinforcement learning method that effectively mitigates length inflation in large language models by reframing length control as a multiplicative rescaling paradigm, thereby achieving lossless optimization and superior performance compared to existing baselines without compromising downstream capabilities.
The paper introduces SCORE, a lightweight deep learning architecture that replaces traditional layer stacking with a weight-shared, ODE-inspired contractive recurrent update mechanism to improve training stability, accelerate convergence, and reduce parameter counts across various model types.
This paper proposes a reinforcement learning approach that automatically tunes cluster scheduler scoring weights using percentage improvement rewards, frame-stacking, and limited domain information to significantly improve end-to-end job performance across diverse workloads and cluster setups.
This paper introduces a machine learning framework that utilizes a directed bipartite graph to model time-ordered predictive linkages between U.S. and Chinese equity markets, revealing a significant directional asymmetry where U.S. returns strongly predict Chinese intraday performance while the reverse effect is limited.
This paper establishes theoretical conditions under which Monotone Operator Equilibrium Networks maintain convergence and bounded error under weight quantization by analyzing spectral perturbations, and validates these findings through experiments showing that quantization-aware training can recover provable convergence at four-bit precision.
This paper introduces the Riemannian geometry-preserving variational autoencoder (RGP-VAE), a generative model that produces high-fidelity, symmetric positive-definite synthetic EEG covariance matrices to effectively augment data and enhance performance in motor imagery brain-computer interface applications.
This paper demonstrates that Transformers performing in-context learning on binary hypothesis testing tasks effectively approximate Bayes-optimal statistical estimators by dynamically adapting their internal decision mechanisms—ranging from voting-style ensembles for linear tasks to deeper sequential computations for nonlinear ones—rather than relying on simple similarity matching or fixed heuristics.
The paper introduces HAPEns, a novel post-hoc ensembling method for tabular data that constructs diverse ensembles along the Pareto front of predictive performance and hardware efficiency, significantly outperforming existing baselines across 83 datasets by explicitly balancing accuracy with resource constraints.
This paper empirically demonstrates that contrary to the hypothesis that moral reasoning alignment requires diversity-seeking algorithms, standard reward-maximizing RLVR methods are equally or more effective because high-reward moral responses exhibit a concentrated distribution in semantic space similar to logical reasoning tasks.
This paper establishes a mathematical framework called Gradient Flow Drifting that proves the equivalence between the recently proposed Drifting Model and the Wasserstein gradient flow of the forward KL divergence under KDE approximation, while extending the approach to a mixed-divergence strategy on Riemannian manifolds to simultaneously mitigate mode collapse and blurring.
This technical note presents a JAX-compatible implementation of the Self-Scaled Broyden family of quasi-Newton methods, including BFGS, DFP, and Broyden variants with Zoom line search, built upon the Optimistix library to facilitate their adoption within the JAX community.
This paper introduces Geo-ATBench, a new benchmark and the Geo-AT task that leverage geospatial semantic context to resolve acoustic ambiguities in multi-label audio tagging, demonstrating through the GeoFusion-AT framework that incorporating location-based priors significantly improves recognition performance and aligns with human judgment.
This paper proposes Conditional Expectation Reward (CER), a novel reinforcement learning method that utilizes the large language model itself as an implicit verifier to provide soft, graded reward signals, thereby overcoming the limitations of rule-based verification and enabling effective reasoning training across both mathematical and general free-form answer domains.
This paper proposes a novel, explainable approach to detect and eliminate neural network backdoors by analyzing active paths within the model, demonstrating its effectiveness through experiments on intrusion detection systems.
The paper introduces FAME, a novel abductive explanation method for large neural networks that utilizes dedicated perturbation domains and LiRPA-based bounds to efficiently generate formal abstract minimal explanations, demonstrating superior performance in explanation size and runtime compared to VERIX+.
This paper proposes a Spatio-Temporal Attention Graph Neural Network (STA-GNN) that integrates conformal prediction to provide unsupervised, explainable, and drift-aware anomaly detection for Industrial Control Systems by modeling dynamic inter-dependencies across physical and network entities.
This paper demonstrates that a data-driven framework combining Singular Value Decomposition with Shallow Recurrent Decoder (SHRED) networks can accurately and efficiently reconstruct full spatio-temporal magnetohydrodynamic states from sparse temperature sensor measurements, offering a robust surrogate model for real-time monitoring and control in nuclear fusion applications.
This paper introduces Contract And Conquer (CAC), a black-box adversarial attack method that combines knowledge distillation on an expanding dataset with precise search space contraction to provably compute adversarial examples within a fixed number of iterations, outperforming existing state-of-the-art approaches on ImageNet.