11899 papers
The paper introduces BumpNet, a sparse multilayer perceptron framework that utilizes trainable sigmoid-based basis functions to efficiently solve partial differential equations and learn operators through universal approximation capabilities, while integrating with architectures like PINNs, EDNNs, and DeepONets.
This paper proposes a fully online distributionally robust reinforcement learning algorithm with general function approximation that learns robust policies through interaction alone, achieving sublinear regret bounds independent of state and action space sizes by leveraging the robust Bellman-Eluder dimension.
This paper proposes Objective-Aware Surrogate Initialization (OASI), a method that seeds multi-objective Bayesian optimization with Pareto-biased solutions to efficiently discover memory-feasible, high-accuracy keyword spotting models for resource-constrained TinyML hardware.
This paper demonstrates that Reinforcement Learning with Verifiable Rewards (RLVR) significantly enhances the generalization of causal reasoning in large language models compared to supervised fine-tuning, but only when the models possess sufficient initial reasoning competence and are trained on specific combinations of model scale and query complexity.
This paper presents the first deterministic iterative algorithm for constructing an -coreset that guarantees subspace embedding for any , achieving an optimal size of by removing previously necessary logarithmic factors and enabling deterministic solutions for regression.
This paper introduces DevRev-Search, an automated benchmark for technical support retrieval, and proposes an Index-Preserving Adaptation strategy that fine-tunes only the query encoder to achieve scalable, high-performance multi-tenant search without the prohibitive cost of re-indexing.
This paper proposes the Measurement-Consistent Langevin Corrector (MCLC), a theoretically grounded plug-and-play module that stabilizes latent diffusion inverse problem solvers by bridging the gap between solver dynamics and stable reverse diffusion processes through measurement-consistent Langevin updates.
This paper presents an automated, multi-stage pipeline that identifies cervical spine fractures by fusing orthogonal 2D segmentations to estimate 3D volumes of interest, which are then analyzed using a 2.5D CNN-Transformer ensemble to achieve diagnostic performance comparable to expert radiologists while reducing computational dimensionality.
This paper establishes a unified statistical framework demonstrating that synthetic augmentation in imbalanced learning is not universally beneficial, revealing that its efficacy and optimal quantity depend on local data symmetry and generator alignment, and proposing a Validation-Tuned Synthetic Size (VTSS) strategy to empirically determine the best augmentation level.
SpecBridge is a novel framework that improves small-molecule identification from mass spectrometry by fine-tuning a spectral encoder to align with a frozen molecular foundation model's latent space, achieving significant accuracy gains over existing baselines while maintaining parameter efficiency.
This paper presents LeanTutor, a proof-of-concept AI system that combines Large Language Models with the Lean theorem prover to provide verified mathematical proof tutoring, evaluated using the newly introduced PeanoBench dataset.
This paper introduces NRR-Phi, a formal framework that maps ambiguous text to a non-collapsing state space using a hybrid extraction pipeline, thereby preserving multiple interpretations and preventing premature semantic commitment in large language model inference.
This paper proposes a new deployment paradigm for language models called "Least-Privilege Language Models," which introduces a mechanism to dynamically restrict a model's internal computational capabilities during inference—rather than just filtering outputs—thereby enabling fine-grained, policy-driven control over specific functionalities without the need for retraining.
NeuroPareto is a novel framework that integrates rank-centric filtering, uncertainty disentanglement via Deep Gaussian Processes, and a history-conditioned acquisition network to efficiently navigate costly many-objective optimization problems in vast parameter spaces while outperforming existing baselines in convergence and diversity.
This paper proposes a consensus-based Bayesian framework that models enterprise directory access as a multi-level interaction graph to detect malicious activity by identifying logical perturbations in strongly connected components through opinion dynamics and time-evolving anomaly scoring.
The paper proposes HealthMamba, an uncertainty-aware spatiotemporal graph state space model that integrates a unified context encoder, a novel GraphMamba architecture, and comprehensive uncertainty quantification to significantly improve the accuracy and reliability of healthcare facility visit predictions across four large-scale real-world datasets.
This paper proposes "Rewards as Labels" (REAL), a novel framework that reformulates Reinforcement Learning with Verifiable Rewards as a classification problem to address gradient misassignment and domination issues in methods like GRPO, thereby achieving superior training stability and performance on mathematical reasoning benchmarks compared to state-of-the-art baselines.
The paper introduces SureLock, a method for Masked Diffusion-LMs that dynamically locks converged token positions to skip redundant computations while caching their keys and values, thereby reducing per-iteration complexity from to $O(MNd)$ and achieving 30–50% FLOP savings without compromising generation quality.
This paper extends the CNF framework to solve coupled and non-linear Partial Differential Equations for forward, inverse, and discovery tasks, while providing a comprehensive survey, implementation, and evaluation of various neural PDE solvers to address challenges like the curse of dimensionality and high computational costs.
This paper establishes a homology theory for ample groupoids using compactly supported Moore complexes, proving functoriality and Kakutani invariance, deriving Mayer-Vietoris sequences, and demonstrating that a universal coefficient theorem holds for discrete coefficients while identifying specific obstructions for non-discrete ones.