7603 papers
This paper introduces Universal Watermark Presence Detection (UWPD), a novel task for identifying invisible watermarks without prior algorithm knowledge, supported by the UniFreq-100K dataset and the Frequency Shield Network (FSNet) model that achieves superior zero-shot detection by dynamically amplifying high-frequency watermark signals while suppressing semantic content.
This paper proposes a bi-directional feedback fusion framework that integrates human activity embeddings with dual-timescale temporal modules to significantly improve the accuracy and interpretability of indoor CO2 and PM2.5 forecasting compared to traditional data-driven models.
This paper introduces FutureBoosting, a hybrid-AI framework that enhances electricity price forecasting by integrating forecasted features from a frozen time series foundation model into a regression model, thereby achieving significant accuracy improvements over state-of-the-art baselines while maintaining interpretability.
The paper proposes Safe Transformer, a modular approach that inserts an explicit, interpretable safety bit into pre-trained language models to achieve controllable alignment and near-zero attack success rates through lightweight fine-tuning, addressing the opacity of traditional implicit safety methods.
This paper proposes a reinforcement learning approach for dense crowd navigation that achieves zero-shot generalization to higher crowd densities by combining density-invariant observation encoding, density-randomized training, and physics-informed proxemic reward shaping, thereby significantly outperforming existing learning-based and analytical methods in success rate and collision avoidance without freezing.
This paper introduces Calibrated Credit Intelligence (CCI), a deployment-oriented framework that integrates Bayesian uncertainty quantification, fairness-constrained gradient boosting, and shift-aware fusion to deliver accurate, reliable, and equitable credit risk scores that remain robust under temporal distribution shifts.
This paper presents "Agent Hunt," a decentralized autoformalization system for algebraic topology that utilizes a simulated bounty-based marketplace to coordinate multiple LLM agents in dynamically proposing, competing to prove, and iteratively refining formal statements within an Interactive Theorem Proving environment.
This paper proposes Rank-factorized Implicit Neural Bias (RIB), a novel positional bias mechanism that enables the use of hardware-efficient FlashAttention in Super-Resolution Transformers, allowing for significantly larger window sizes and training patches that achieve state-of-the-art performance (35.63 dB PSNR) while reducing training and inference times by 2.1 and 2.9, respectively.
The paper introduces ResearchEnvBench, a new benchmark designed to evaluate autonomous agents' ability to synthesize complex execution environments for research code, revealing significant current limitations in dependency resolution and version management.
The paper introduces ViroGym, a comprehensive benchmark comprising extensive deep mutational scanning data and real-world tasks to evaluate protein language models for predicting viral variant effects and guiding rational antigen selection for vaccine development.
This paper introduces an efficient framework for heterogeneous decentralized diffusion models that enables experts to train with mixed objectives (DDPM and Flow Matching) and reduced resource requirements, achieving a 16x decrease in compute and 14x reduction in data compared to prior approaches while improving image quality and diversity.
This paper proposes a framework that fine-tunes pretrained generative models to directly sample within complex, structured feasible regions, achieving a novel balance between strict constraint satisfaction and high-quality sample realism for safety-critical applications like robotics and autonomous driving.
This paper identifies that applying Group Relative Policy Optimization (GRPO) to diffusion language models causes reward collapse due to noisy importance ratio estimates and formulation mismatches, and proposes StableDRL, a reformulated algorithm featuring unconditional clipping and self-normalization to stabilize training and prevent policy drift.
The paper introduces DIRECTER, a novel activation steering method that dynamically modulates steering strength through a plausibility-guided decoding loop and layer sensitivity analysis to enhance LLM instruction-following accuracy while preventing the oversteering that typically degrades text quality.
ButterflyViT introduces a geometric parameterization method that treats Mixture of Experts as rotations of a shared quantized substrate, achieving a 354 memory reduction for Vision Transformers on edge devices while maintaining accuracy through spatial smoothness regularization.
This paper introduces ProtAlign, a multi-objective preference alignment framework that fine-tunes pretrained inverse folding models to simultaneously optimize diverse developability properties like solubility and thermostability while preserving structural designability, resulting in the enhanced MoMPNN model for practical protein sequence design.
This paper surveys the landscape of robotic foundation models, identifies eleven key industrial implications to establish a 149-criteria assessment framework, and evaluates 324 models to reveal that current industrial readiness is limited and uneven, necessitating a shift from isolated benchmark successes to systematic integration of safety, real-time performance, and robust system deployment.
This paper introduces XMACNet, an explainable, lightweight CNN that combines self-attention mechanisms with multi-modal fusion of RGB images and vegetation indices to achieve high-accuracy chili disease classification suitable for edge deployment.
This paper proposes ICD3, an interpretable and robust approach for detecting concept drift in imbalanced streaming data by employing multi-distribution-granular search to identify small concepts and training independent One-Cluster Classifiers for each, thereby overcoming the masking effect of dominant large clusters.
This paper proposes and validates a multi-level explainability framework demonstrating that SHAP explanations for Alzheimer's disease diagnostic and prognostic models are robust, stable, and consistent across different disease stages and prediction tasks, thereby enhancing their reliability for clinical adoption.