11526 papers
This paper introduces Akkumula, a scalable and transparent framework that utilizes Spiking Neural Networks to model realistic driver behavior through evidence accumulation, effectively reproducing braking, accelerating, and steering actions while overcoming the limitations of existing hand-crafted approaches.
This paper introduces PubHealthBench, a new benchmark comprising over 8,000 questions derived from UK government guidance, to evaluate LLMs on public health knowledge and finds that while state-of-the-art models excel in multiple-choice tasks, their performance on free-form responses remains limited, highlighting the need for additional safeguards in real-world applications.
This paper establishes the theoretical foundations and single-agent architecture of the Emotion-Gradient Metacognitive RSI (EG-MRSI) framework, a novel system that integrates introspective metacognition and emotion-driven intrinsic motivation to enable provably safe, recursive self-improvement through differentiable reward signals and quantifiable semantic learning metrics.
This paper proposes a novel unsupervised framework for Invariant Risk Minimization that redefines invariance through feature distribution alignment, introducing the linear PICA and deep generative VIAE methods to learn robust, environment-invariant representations from unlabeled data.
TSPulse is a family of ultra-lightweight, pre-trained time-series models that utilize a novel disentanglement framework to learn complementary temporal, spectral, and semantic views, achieving state-of-the-art zero-shot and fine-tuned performance across diverse diagnostic tasks while outperforming models 10–100 times larger.
This paper establishes a tighter information-theoretic lower bound and proposes a modified Track-and-Stop algorithm with a tie-aware stopping rule that achieves asymptotic instance-optimality for best-arm identification in stochastic multi-armed bandits when the number of optimal arms is known.
This paper proposes "Feature Mixing," an extremely simple and fast modality-agnostic method for multimodal outlier synthesis that achieves state-of-the-art performance in out-of-distribution detection and segmentation while offering significant speedups, alongside the introduction of a new multimodal dataset called CARLA-OOD.
This paper analyzes the convergence and escape dynamics of Stochastic Gradient Descent in one-dimensional landscapes, establishing that while SGD reliably converges to local minima, it may linger near local maxima depending on noise variance and geometry, with specific results provided for the probability of escaping sharp maxima to neighboring minima.
This paper introduces the BAH dataset, a multimodal collection of 1,427 videos from 300 participants annotated for ambivalence and hesitancy recognition, alongside baseline benchmarking results that highlight the need for advanced models to support personalized digital health interventions.
SafeDPO is a lightweight, theory-driven method that achieves provably optimal safety alignment in Large Language Models by deriving a closed-form solution for safety-constrained objectives, thereby eliminating the need for complex reward models or multi-stage pipelines while maintaining competitive helpfulness.
This paper introduces TADA, a targeted diffusion-based augmentation framework that selectively generates synthetic images for hard-to-learn examples to improve classifier generalization with significantly reduced computational overhead compared to full-dataset augmentation.
This paper proposes a computationally efficient supervised filter based on a Gumbel-copula implied upper-tail concordance score to identify features that are simultaneously extreme with the positive class, demonstrating its effectiveness in ranking clinically relevant predictors for diabetes risk across large-scale and clinical datasets while outperforming standard filters and matching strong baselines.
This paper introduces Supervised Calibration (SC), a loss-minimization framework that enhances In-Context Learning in Large Language Models by learning optimal per-class affine transformations to correct systematic biases and alter decision boundary orientations, thereby achieving state-of-the-art performance across multiple models and datasets.
This paper reviews the historical disconnect between approximation theory and machine learning practice, discusses emerging trends like deep networks and transformers, and introduces novel research enabling function approximation on unknown manifolds without requiring explicit manifold feature learning.
This paper presents a low-power, cost-effective diffractive optical system that integrates a passive diffractive layer with a shallow neural network to remotely and accurately reconstruct 3D structural vibration spectra, overcoming the scalability and complexity limitations of traditional Structural Health Monitoring solutions.
The paper presents AutoQD, a theoretically grounded method that automatically discovers diverse, high-performing policies in continuous control tasks by generating behavioral descriptors through random Fourier feature embeddings of policy occupancy measures, thereby eliminating the need for hand-crafted descriptors in Quality-Diversity optimization.
This paper introduces Conflict-Aware Evidential Deep Learning (C-EDL), a lightweight post-hoc method that enhances the robustness of uncertainty quantification against adversarial and out-of-distribution inputs by leveraging diverse task-preserving transformations to detect representational conflict and calibrate predictions without retraining.
This paper challenges the default use of honest estimation in causal forests, demonstrating through extensive benchmarking that while it prevents overfitting, it often increases underfitting and reduces the accuracy of individual-level treatment effect estimates, suggesting its application should be guided by specific goals and empirical evaluation rather than reflexive adoption.
This paper proposes a novel Bayesian framework that generalizes federated ADMM by leveraging variational inference duality, yielding both a theoretical unification of ADMM with Gaussian assumptions and practical, high-performance variants like Newton-like and Adam-like updates for diverse distribution families.
This paper presents a theoretical framework demonstrating that standard sparse autoencoders generally fail to recover ground truth monosemantic features from superposed polysemantic ones, and proposes a reweighted variant (WSAE) with a derived selection principle that significantly improves feature recovery and interpretability.