3743 papers
This paper presents the first systematic evaluation of self-supervised learning for label-efficient sleep staging using wearable EEG, demonstrating that a specialized SSL pipeline significantly outperforms supervised baselines and general-purpose foundation models by achieving clinical-grade accuracy with only 5–10% of labeled data.
The paper proposes HyWA, a novel Personalized Voice Activity Detection (PVAD) approach that utilizes a hypernetwork to generate personalized weights for selected layers of a standard VAD model, demonstrating consistent performance improvements and enhanced deployment flexibility compared to existing speaker-conditioning methods.
This paper introduces "Reveal-to-Revise," an explainable, bias-aware generative framework that unifies cross-modal attention, Grad-CAM++ attribution, and iterative feedback to achieve state-of-the-art performance and fairness in multimodal image generation and text classification tasks.
The paper introduces MVCustom, a novel diffusion-based framework that unifies multi-view camera pose control and prompt-based customization by leveraging a feature-field representation for training and employing depth-aware rendering with consistent latent completion during inference to ensure both geometric consistency and subject fidelity.
This paper introduces the Hermite eigenstructure ansatz (HEA), a theoretical framework that accurately predicts kernel regression learning curves on real datasets using only the empirical data covariance and target function decomposition, by approximating kernel eigenstructures as Hermite polynomials and demonstrating that MLPs in the feature-learning regime follow similar learning patterns.
KVTC is a lightweight, model-agnostic transform coder that achieves up to 20 (or higher) compression of Key-Value caches for large language models by combining PCA-based decorrelation, adaptive quantization, and entropy coding, thereby enabling memory-efficient serving with reusable caches while maintaining high reasoning and long-context accuracy.
This study evaluates the ability of six LLM-based systems to answer expert-level questions about high-temperature superconductivity using a curated database of 1,726 papers, finding that retrieval-augmented generation (RAG) systems outperform closed models in providing comprehensive, well-supported answers while highlighting both the potential and current limitations of LLMs in specialized scientific domains.
This paper introduces DeepEyesV2, an agentic multimodal model that employs a two-stage training pipeline combining cold-start data curation and reinforcement learning to effectively integrate external tools like code execution and web search for complex real-world reasoning tasks.
The paper introduces "What We Don't C," a novel latent flow matching approach that disentangles latent subspaces by explicitly removing information from conditional guidance to generate meaningful residual representations, thereby enabling the discovery and analysis of factors of variation not captured in the conditioning variables.
The paper proposes D-GAP, a dataset-agnostic and gradient-guided augmentation method that adaptively blends frequency amplitudes and pixel values to reduce domain-specific learning biases and restore spatial details, thereby significantly improving out-of-domain robustness in computer vision models.
This paper introduces STREAM-VAE, a dual-path variational autoencoder that separates slow drifts and fast spikes in vehicle telemetry data to overcome the limitations of standard reconstruction-based methods and achieve robust anomaly detection across diverse operating modes.
This paper introduces REMSA, a constraint-aware agent built upon the newly constructed RSFM Database (RS-FMD) that automates the selection of suitable remote sensing foundation models from natural language queries by integrating structured metadata retrieval with task-driven decision workflows, achieving superior performance over baselines in a novel expert-verified benchmark.
This paper proposes a hierarchical dual-strategy framework that achieves precise selective unlearning of privacy-sensitive medical knowledge in large language models while preserving fundamental competencies, demonstrated by high forgetting and preservation rates on clinical datasets with minimal parameter modification.
This paper introduces CostNav, the first physics-grounded navigation benchmark that evaluates autonomous agents using real-world economic data to reveal that current methods, despite varying in hardware and architecture, all fail to achieve economic viability due to negative contribution margins.
This paper introduces IndiMathBench, a human-verified benchmark of 312 formal Lean 4 theorems derived from Indian Mathematics Olympiads, which utilizes an AI-powered human-assisted pipeline to address the scarcity of high-quality training data and reveals significant challenges in current autoformalization and theorem proving capabilities.
This paper proposes C3, a novel uncertainty quantification method that trains controllable video models to generate high-resolution, calibrated confidence heatmaps at the subpatch level by estimating uncertainty in latent space and using strictly proper scoring rules, thereby enabling reliable hallucination detection and out-of-distribution identification for robotics applications.
The paper introduces Trio, a closed-loop molecular generation framework that integrates fragment-based language modeling, reinforcement learning, and Monte Carlo tree search to produce chemically valid, diverse, and pharmacologically optimized ligands with significantly improved binding affinity, drug-likeness, and synthetic accessibility compared to state-of-the-art methods.
This paper introduces computationally efficient and statistically consistent Random Forest variants that minimize the maximum risk across diverse environments to improve out-of-distribution generalization, offering novel guarantees for mean squared error, negative reward, and regret-based risks.
GTR-Turbo is a highly efficient training method for multi-modal agents that eliminates the need for costly external teacher models by using merged checkpoints from ongoing reinforcement learning as a "free" teacher, thereby improving accuracy by 10–30% while reducing training time and compute costs by 50% and 60%, respectively.
This paper introduces the Pretrained Battery Transformer (PBT), a foundation model that leverages battery-knowledge-encoded mixture-of-experts layers to overcome data scarcity and heterogeneity, achieving state-of-the-art universal battery life prediction across diverse chemistries and conditions.