Sovereign Context Protocol defines open runtime attribution layer for human-generated content used in LLM training and inference.
Systematic evaluation of segmentation and geospatial foundation models for global field boundary segmentation using FTW benchmark.
Bayes-MICE extends multiple imputation for time series missing data using Bayesian inference and MCMC sampling.
Landmark-guided pose scoring system for automated transducer positioning in point-of-care cardiac ultrasound acquisition.
Pan-cancer immune landscape mapping through metagene clustering and predictive modeling to identify immunotherapy response drivers.
Weakly convex ridge regularizer for 3D non-Cartesian MRI reconstruction providing stable variational alternative to deep learning methods.
Conformal Prediction Assessment framework for evaluating conditional coverage validity in distribution-free prediction with finite-sample guarantees.
LightMover framework for controllable light manipulation in images using video diffusion priors for physically plausible illumination editing.
Unsupervised evaluation of nine open-source pre-trained audio models on music structure analysis without requiring annotated training data.
Study evaluating seven pre-trained deep learning models for predicting groove ratings from audio signals versus handcrafted features.
StretchCast global-regional AI weather forecasting framework using variable-resolution cubed-sphere mesh for refined regional predictions.
Comparative analysis of AI datasets, foundation models, and barriers to achieving general-purpose AI in surgical image analysis.
D-SPEAR dual-stream replay mechanism for stable off-policy reinforcement learning in robotic manipulation with contact-rich dynamics.
Rainbow-DemoRL combines multiple demonstration-augmented reinforcement learning strategies to improve sample efficiency using offline data.
Theoretical study of relationships between Bayesian networks and structural causal models for probabilistic graphical modeling.
CarbonEdge framework for carbon-aware deep learning inference at network edge, extending model partitioning to optimize environmental impact alongside latency.
High-performance engine for low-bit matrix-vector multiplication enabling efficient inference in neural networks, vector databases, and LLMs.
Open-source benchmark evaluating four AI-powered people search platforms across 119 queries for recruiting, sales, and expert search use cases.
Introduces Hidden Ads backdoor attack class exploiting Vision-Language Models' recommendation behavior to inject unauthorized advertisements through natural triggers.
Extends Bellman Deviation Detection framework for model-free RL to detect man-in-the-middle attacks in cyber-physical systems with refined MDP attack models.
RTLSeek uses multi-stage reinforcement learning to improve LLM-based RTL/Verilog generation with diverse hardware design implementations.
Composer paradigm for test-time instance-specific parameter composition enabling adaptive generative models.
Neural Gaussian mixture model using energy score guidance for predictive uncertainty quantification in machine learning.
LVRPO framework for language-visual alignment in multimodal foundation models using GRPO for understanding and generation.
KAT-Coder-V2 agentic coding model using five expert domains with specialized fine-tuning and unified distillation for software engineering tasks.
Empirical likelihood framework for nonsmooth functionals in policy evaluation and statistical inference.
Geospatial inference using encrypted packet-level traffic data from distributed sensor nodes without raw sensory access.
GPU-accelerated JAX library for SGP4 orbital propagation of mega-constellations enabling efficient space situational awareness.
ImagenWorld benchmark with 3.6K condition sets for stress-testing image generation models across six core tasks with human evaluation.
Statistical guarantees for distributionally robust optimization using optimal transport divergences for adversarial robustness.
Markov chain-based car-following model for traffic flow using empirical probabilistic paradigm.
Physics-informed neural networks framework (Deflation-PINNs) that identifies multiple distinct solutions to nonlinear PDEs.
Privacy-preserving federated learning framework using flow-matching generation to improve robustness and aggregation in distributed training.
Transformer-based spatial upsampling of head-related impulse responses for binaural audio rendering.
Analysis of prompt injection attacks against LLM agents, tracking attack pipeline stages and defense mechanisms across five frontier models.
Modular transformer approach for efficient domain adaptation in optical character recognition with reduced computational requirements.
Research on how LLMs perform scientific reasoning tasks and how prompting affects their internal reasoning processes.
Training-free spectral modulation of diffusion model cross-attention via Fourier analysis for improved text-to-image control.
3D SONAR sensors for road condition monitoring and damage classification in adverse weather conditions.
Deep reinforcement learning framework using PPO to train virtual agents for guiding fish school collective motion.
Differentiable power-flow optimization using neural networks to replace Newton-Raphson methods for scalable grid simulation.
Domain adaptation method for quantum machine learning using classical shadows to handle distribution shift in quantum data.
Multi-view learning approach with prototypes for thyroid ultrasound classification with improved robustness across devices.
Multi-agent pipeline for literature analysis using Deleuzian ontology to identify non-linear patterns in research landscapes.
Framework for evolutionary GPU kernel optimization using evaluation-driven agent and evolutionary techniques for operator generation.
Ensemble learning system combining deep learning and traditional classifiers for brain tumor MRI classification.
Analysis of prompt framing artifacts in vision-language model evaluation on clinical neuroimaging tasks.
Survey of network performance modeling approaches comparing traditional simulation with deep learning methods.
Novel geometric optimization framework using affine normal descent with invariance under volume-preserving transformations.
Diffusion distillation approach using reinforcement learning to improve student model performance beyond teacher anchoring.