Olmo Hybrid: From Theory to Practice and Back
OLMo Hybrid: theoretical and empirical analysis of hybrid models combining linear RNNs and attention as alternatives to pure transformers with scaling benefits.
OLMo Hybrid: theoretical and empirical analysis of hybrid models combining linear RNNs and attention as alternatives to pure transformers with scaling benefits.
Neural operator methods for multi-task optimal control problems, mapping task descriptions to control policies using permutation-invariant architectures.
Benchmark of Earth embedding models (AlphaEarth, Prithvi, Clay) for neighborhood-scale urban monitoring from satellite imagery.
Analysis of trajectory prediction models revealing that surrounding agents often degrade accuracy due to learned confounders, using Shapley attribution.
Study of data intervention techniques for improving fairness across demographic subgroups in ICU prediction models using real healthcare data.
Data-driven approach using trained autoencoders as fast projectors to enforce complex nonconvex operational constraints in learning and control systems.
Theoretical analysis of adversarial online learning for smooth real-valued functions on ℝ with cumulative p-loss bounds.
Empirical survey comparing regularization frameworks (Ridge, Lasso, ElasticNet, Post-Lasso) across 134,400 simulations with historical development context.
Method for evaluating bagged neural network predictions using kernel density estimation to select representative predictions in nonlinear regression.
BlazeFL: lightweight federated learning simulation framework enabling fast, deterministic training of hundreds or thousands virtual clients on single node.
Neural approach for black-box global optimization from noisy samples using iterative refinement to avoid local minima in multi-modal functions.
Reinforcement learning approach for handling delayed feedback by replacing state augmentation with homomorphic methods to reduce sample complexity.
Mechanistic interpretability method for discovering repeated attention patterns in large language models at scale without resource-intensive controlled settings.
CountsDiff: diffusion model framework for generating and imputing count-based discrete ordinal data using survival probability schedules.
Framework for automated mathematical conjecture resolution combining LLMs with formal verification to improve reliability of research-level mathematical problem solving.
Research on representational collapse in multi-agent LLM committees using majority voting, measuring agent diversity via cosine similarity and effective rank on mathematical reasoning tasks.
k-Maximum inner product attention for graph transformers addressing quadratic complexity while maintaining expressive power of GraphGPS.
DDCL-Attention: Prototype-based readout layer for transformer encoders using soft probabilistic token matching for compact summaries.
Empirical comparison of Poisson log-normal models vs penalized Poisson regression for microbiome count data prediction.
Bayesian information-theoretic approach to training data attribution for tracing model predictions to influential training examples.
Method for input-dependent layer selection in steering vectors to improve LLM alignment at inference time, adapting intervention layer per input.
SODA: Semi on-policy knowledge distillation method for LLMs balancing off-policy simplicity with on-policy effectiveness without adversarial training instability.
Spatiotemporal interpolation method for NASA GEDI satellite biomass data with uncertainty quantification for deforestation monitoring.
Ride-hailing demand forecasting using regime-calibrated priors and demand segmentation for fleet dispatch optimization.
Theoretical research on multi-task representation learning for reinforcement learning with shared representations across related RL tasks with different rewards.
ML research on adapting KGE metric for geoscientific systems with temporal non-stationarity in water management and climate variability modeling.
Framework combining structure pretraining with diffusion models for generating molecular dynamics trajectories with limited MD data.
ACES: method for selecting LLM-generated code using LLM-generated tests via leave-one-out AUC consistency without determining test correctness.
Title mismatch: discusses ride-hailing demand forecasting with regime-calibrated similarity ensemble, not dimensionality reduction on CNN features.
Low-bit mixed-precision attention kernel using MXFP for efficient transformer inference with reduced memory bandwidth.
BWTA: binarized transformer quantization scheme with ternary activations and algorithm-hardware co-design for efficient inference.
Multirate Stein variational gradient descent optimizing different step sizes for attraction and repulsion in Bayesian sampling.
Autoencoder method for parameter estimation of superposed damped sinusoidal signals in physical systems.
Analysis of LLM reasoning models under noisy labels in reinforcement learning with verifiable rewards, identifying label noise vulnerabilities.
ArrowFlow: novel ML architecture operating in permutation space using ranking filters and permutation-matrix updates without gradients.
Generalization analysis of stochastic bilevel optimization with applications to hyperparameter optimization, meta-learning, and RL.
Spectral Path Regression using directional Chebyshev harmonics for interpretable learning on tabular data without exponential scaling.
Index policy for restless multi-armed bandits under individual penalty constraints for wireless resource allocation.
Neural network surrogates for geophysics recover physical sensitivity kernels through gradient analysis on surface-wave dispersion.
Analysis of geometric alignment cost in scientific foundation models for biology/physics, showing discrete tokenization degrades continuous geometry preservation.
Framework for uncertainty-aware foundation models on clinical data, addressing incomplete and irregular measurements in healthcare.
Copula-based method for generating synthetic educational data that preserves marginal distributions while protecting student privacy.
ClawArena benchmark for evaluating AI agents in dynamic environments with evolving information, contradictions, and implicit user feedback.
Graph-assisted retrieval framework for reasoning about defects in laser powder bed fusion manufacturing using structured scientific knowledge.
Framework using Temporal Behavior Trees to repair suboptimal trajectories before using them for robot control policy learning.
Analysis of token routing in Mixture-of-Experts models reveals three-phase training trajectory for load balance evolution.
Method for constrained model steering of LLMs addressing safety/privacy requirements via spectral subspace optimization.
Calibration audit of multimodal cancer survival models fusing histopathology images with genomic data.
Two-stage ML framework predicting E. coli presence in household drinking water for microbial contamination screening.
Risk scoring system optimizing net benefit using sparse integer linear programming for high-stakes decision-making.