6866 papers
This paper introduces a formal framework for "informativeness" and a corresponding hospitality-specific VQA dataset to evaluate Vision-Language Models, revealing that while current models struggle with decision-oriented reasoning, their performance significantly improves with modest domain-specific finetuning.
This paper proposes a Contrastive Tensor Pre-training (CTP) framework that replaces traditional pairwise similarity alignment with a joint tensor-based approach to unify multiple modalities in a single embedding space, thereby enhancing scene understanding and end-to-end performance in autonomous driving.
This paper introduces a particle filtering framework to rigorously analyze the accuracy-cost tradeoffs of parallel inference methods in large language models, establishing theoretical guarantees and identifying fundamental limits while demonstrating that sampling error alone does not fully predict final model accuracy.
This paper introduces VLM-SubtleBench, a comprehensive benchmark spanning ten fine-grained difference types across diverse domains like industrial, medical, and aerial imagery, to evaluate and reveal the significant performance gaps between current vision-language models and humans in subtle comparative reasoning tasks.
This paper presents a decision-theory framework and a blended AI-statistical forecasting system that successfully delivered skillful, tailored monsoon onset predictions to 38 million Indian farmers in 2025, enabling better agricultural decision-making under uncertainty.
This paper introduces SMGI, a structural theory of general artificial intelligence that formalizes learning as the controlled evolution of a typed meta-model, unifying diverse existing approaches under a rigorous framework defined by structural closure, dynamical stability, bounded capacity, and evaluative invariance.
LeJOT-AutoML is an LLM-driven AutoML framework that automates feature engineering for Databricks job execution time prediction by synthesizing runtime-derived features from diverse artifacts, reducing the development cycle from weeks to minutes and achieving 19.01% cost savings through improved orchestration.
This paper introduces EOAL, a unified and detector-free framework for open-set active learning that leverages labeled unknowns through label-guided clustering and a Dirichlet-calibrated auxiliary head to achieve superior accuracy, efficiency, and query precision compared to existing state-of-the-art methods.
This paper proposes a Bayesian Transformer framework that integrates Monte Carlo Dropout, variational feed-forward layers, and stochastic attention into a PatchTST backbone to deliver well-calibrated, state-of-the-art probabilistic load forecasts with robust uncertainty estimates under extreme weather distributional shifts across multiple global power grids.
NaviDriveVLM proposes a decoupled framework that separates high-level reasoning and motion planning using a large-scale Navigator and a lightweight Driver, achieving superior end-to-end performance on the nuScenes benchmark while reducing training costs and enhancing interpretability.
DyQ-VLA is a dynamic quantization framework for Embodied Vision-Language-Action models that leverages real-time kinematic proxies to adaptively switch and allocate bit-widths, significantly reducing memory footprint and improving inference speed while maintaining near-original performance.
This paper introduces Rel-MOSS, a novel relation-centric deep learning framework that addresses the critical issue of class imbalance in relational databases by employing a relation-wise gating controller and a relation-guided minority synthesizer to enhance the representation and over-sampling of minority entities, thereby significantly outperforming existing methods in entity classification tasks.
This paper proposes a robust transfer learning framework that leverages side information to construct estimate-centered uncertainty sets, thereby reducing the conservatism of standard distributionally robust optimization and improving policy performance under environmental shifts with limited target data.
This paper proposes an AI-driven framework that leverages CodeBERT embeddings and XGBoost to perform static, explainable risk scoring of SQL-based metric definitions, enabling healthcare institutions to proactively identify and mitigate privacy risks in aggregated data before deployment while ensuring regulatory compliance and auditability.
This paper introduces ELLMob, a self-aligned Large Language Model framework that leverages Fuzzy-Trace Theory to reconcile habitual patterns with event constraints, addressing the lack of event-annotated datasets and significantly improving the generation of human mobility trajectories during major societal events like typhoons, pandemics, and the Olympics.
This paper introduces a reinforcement learning pipeline that successfully simplifies complex knot diagrams and determines the unknotting number of the composite knot as three, confirming a recently established upper bound.
This paper introduces PSTNet, a lightweight, physics-structured neural network with only 552 parameters that embeds atmospheric turbulence scaling laws directly into its architecture to enable accurate, real-time turbulence estimation on resource-constrained aircraft guidance systems where traditional models fail.
This paper introduces Local Constrained Bayesian Optimization (LCBO), a novel framework that overcomes the curse of dimensionality in high-dimensional constrained problems by alternating between local descent and uncertainty-driven exploration, achieving polynomial convergence rates and outperforming state-of-the-art methods on benchmarks up to 100 dimensions.
This paper introduces Mixture-of-Experts (MoE) and Mixture-of-Linear-Experts (MoLE) architectures for Machine Learning Interatomic Potentials, demonstrating that element-wise routing with shared nonlinear experts achieves state-of-the-art accuracy across multiple benchmarks while revealing chemically interpretable specialization aligned with periodic-table trends.
The paper introduces $OneMillion-Bench, a novel benchmark comprising 400 expert-curated tasks across five professional domains designed to rigorously evaluate the reliability, reasoning depth, and practical readiness of language agents in complex, real-world scenarios that existing benchmarks fail to address.