Goedel-Code-Prover: hierarchical proof search framework for automated code verification in Lean 4 using LLMs to decompose complex verification goals.
Analysis of how AI scaling laws reshape classical Amdahl's Law for modern heterogeneous computer architectures with specialized accelerators and tensor datapaths.
KG-Hopper: reinforcement learning framework enabling compact open-source LLMs to perform knowledge graph reasoning for multi-hop KBQA tasks.
mSFT: iterative algorithm for multi-task supervised fine-tuning that addresses heterogeneous overfitting by dynamically adjusting compute budget across datasets.
KALAVAI: quantitative model predicting when independently trained specialist LLMs can be fused post-hoc with measurable performance gains; includes practical prediction formula.
EVA: reinforcement learning framework for video agents using MLLMs with adaptive reasoning to handle long video sequences and temporal dependencies efficiently.
MDKeyChunker: structure-aware chunking pipeline for Markdown documents with single-call LLM enrichment to improve RAG accuracy and reduce metadata extraction overhead.
Deep learning model for automated sleep staging shows poor generalization to clinical populations with comorbid sleep disorders; proposes iSLEEPS to address limitations.
arXiv paper on SM-Net, machine learning model generating stellar spectra from fundamental stellar parameters using multiple libraries.
arXiv paper analyzing response homogenization in RLHF-aligned LLMs and its effects on uncertainty estimation methods.
arXiv paper introducing scalability coefficients for detecting problematic items in large-scale AI benchmarks using isotonic regression.
arXiv paper on Few TensoRF, a 3D reconstruction framework combining tensor representations with few-shot learning for NeRF.
arXiv paper demonstrating dual-layer side-channel attacks on local Vision-Language Models exploiting dynamic preprocessing vulnerabilities.
arXiv survey on reinforcement learning applications for infectious disease control and epidemic response optimization.
arXiv paper on physics-guided deep learning for groundwater level prediction using spatio-temporal modeling.
MAGNET: decentralized system for autonomous generation and training of domain-expert language models using autoresearch and BitNet ternary quantization.
Theoretical analysis of simplicity bias in neural networks using minimum description length principle and compression framework.
Investigation of whether LLMs perform genuine in-context molecular property prediction or rely on memorization despite potential training data contamination.
Analysis of activation-based probes for detecting misaligned AI systems, showing blind spots in detecting coherent misalignment versus deception.
DRiffusion: parallel sampling framework accelerating diffusion model inference through draft-and-refine process with skip transitions.
Data-driven framework using wavelet analysis on acoustic emission data to model plastic deformation in metals.
Transformer model with factorized attention to predict defensive coverage assignments in NFL football plays.
Bandit algorithm approach for dynamic regret minimization in unconstrained adversarial linear settings.
Deep learning framework using transformers to predict patient outcomes from EEG while preventing data leakage in survival prediction.
Game-based learning system using adaptive mechanisms to personalize mathematical education for children.
Machine learning for satellite network topology configuration under dynamic orbital movement.
EngineAD real-world multivariate anomaly detection dataset from vehicle fleet sensor telemetry with expert annotations for safety-critical domain.
ARTA joint training framework for adversarially robust multivariate time-series anomaly detection using min-max optimization and information retention.
Theoretical analysis of Minkowski weighted k-means revealing objective as power-mean aggregation of within-cluster dispersions controlled by exponent.
Somax composable Optax-native stack for second-order curvature-aware training with modular APIs for operators, estimators, and preconditioners.
QuitoBench open benchmark for time series forecasting covering eight trend-seasonality-forecastability regimes with regime-balanced dataset design.
GLU framework for sparse spatiotemporal reconstruction and forecasting using global-local-uncertainty fusion with unified state representation.
Two methods for identifying causal directionality in bivariate data using anticipated asymmetric geometries and monotonicity measures.
Deep energy method for solid mechanics simulations handling random material parameters without repeated mesh discretization.
H-Node ANC mechanistic framework identifies and defends hallucination representations in transformer LLMs at individual hidden-state dimensions.
Adversarial bandit optimization framework for non-convex non-smooth loss functions with globally bounded perturbations on linear components.
Study of LLMs' theory of mind capabilities using behavior-based testing to assess their ability to self-model and model other agents.
Dynamic Tokenization via Reinforcement Patching learns variable-sized data-driven patches for long-horizon sequence models with zero-shot transfer capability.
Framework assessing robustness of LLM-enhanced Graph Neural Networks against poisoning attacks targeting both graph structure and textual attributes.
Deep learning approach for short-term precipitation forecasting handling massive atmospheric variables and class imbalance in weather data.
DPD-Cancer uses graph-based deep learning for predicting anti-cancer drug activity and molecular structure-cellular interaction modeling.
TinyML pipeline for real-time acoustic anomaly detection on IoT microcontrollers for environmental sound monitoring without cloud processing.
PEANUT proposes perturbation-based adversarial attack method targeting robustness vulnerabilities in Graph Neural Networks through topology modifications.
PruneFuse strategy uses pruned networks for efficient data selection and fuses them with original networks to optimize deep neural network training.
Complexity analysis of optimal graph rewiring to address oversmoothing and oversquashing in deep graph neural networks.
Unified framework for data-centric dynamic training of LLMs with consistent interfaces for data selection and reweighting.
Study of LLM-based AI scientist agents learning from iterative experimental feedback in cell screening with 800 replicated experiments.
Graph neural network framework using Ricci flow for improved geometric representation learning on graphs.
Analysis of optimization trade-offs in asynchronous federated learning addressing gradient staleness and client bias.
Knowledge distillation approach for deploying Transformer-based reinforcement learning on resource-constrained energy management devices.