2/20/2026
Transforming Behavioral Neuroscience Discovery with In-Context Learning and AI-Enhanced Tensor Methods
Using in-context learning and AI-enhanced tensor methods to automate behavioral neuroscience discovery pipelines.
2/20/2026
Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
Uncertainty-aware time-series ensemble method for proactive anomaly prediction providing early warning signals before anomalies occur.
2/20/2026
Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders
Hash-based indexing mechanism for embedding tables in recommendation systems reducing collisions and improving model freshness.
2/20/2026
Spatio-temporal dual-stage hypergraph MARL for human-centric multimodal corridor traffic signal control
Multi-agent reinforcement learning framework using spatio-temporal hypergraphs for human-centric multimodal traffic signal control.
2/20/2026
AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation
Graph neural network architecture with adversarial synthesis and contrastive learning for resilient node classification under structural noise.
2/20/2026
Adam Improves Muon: Adaptive Moment Estimation with Orthogonalized Momentum
NAMO optimizer combining Adam's adaptive moments with Muon's orthogonalized momentum for efficient large language model training.
2/20/2026
MeGU: Machine-Guided Unlearning with Target Feature Disentanglement
Machine unlearning approach using target feature disentanglement to balance privacy and model utility for right to be forgotten compliance.
2/20/2026
A Locality Radius Framework for Understanding Relational Inductive Bias in Database Learning
Formal framework defining locality radius to determine when multi-hop reasoning is necessary for foreign key discovery in relational databases.
2/20/2026
FLoRG: Federated Fine-tuning with Low-rank Gram Matrices and Procrustes Alignment
Federated fine-tuning approach for LLMs using low-rank Gram matrices and Procrustes alignment to enable collaborative adaptation without data sharing.
2/20/2026
Operationalization of Machine Learning with Serverless Architecture: An Industrial Operationalization of Machine Learning with Serverless Architecture: An Industrial Implementation for Harmonized System Code Prediction
Serverless MLOps framework orchestrating complete ML lifecycle with event-driven pipelines for model-agnostic inference and rapid deployment.
2/20/2026
Online Learning with Improving Agents: Multiclass, Budgeted Agents and Bandit Learners
Online learning framework for multiclass settings where agents can improve features to achieve better labels with budgeted costs.
2/20/2026
i-PhysGaussian: Implicit Physical Simulation for 3D Gaussian Splatting
3D Gaussian splatting with implicit physical simulation for accurate prediction of object deformation and movement.
2/20/2026
TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
Time-invariant frequency operator for handling distribution shift in nonstationary time series forecasting.
2/20/2026
VP-VAE: Rethinking Vector Quantization via Adaptive Vector Perturbation
Vector Perturbation VAE decouples representation learning from discretization by eliminating explicit codebook dependency in generative modeling.
2/20/2026
When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer
Analysis of underfitting challenges in multi-expert learning to defer systems where classifiers abstain and defer to experts.
2/20/2026
TimeOmni-VL: Unified Models for Time Series Understanding and Generation
Unified multimodal model for time series understanding and generation combining numerical forecasting with semantic interpretation.
2/20/2026
Powering Up Zeroth-Order Training via Subspace Gradient Orthogonalization
Gradient-free zeroth-order optimization method for memory-efficient fine-tuning of large-scale models via subspace gradient orthogonalization.
2/20/2026
In-Context Learning in Linear vs. Quadratic Attention Models: An Empirical Study on Regression Tasks
Empirical study comparing how transformers and linear attention models perform in-context learning on regression tasks, analyzing MSE, convergence, and generalization.
2/20/2026
Continual uncertainty learning
Deep reinforcement learning with domain randomization for robust control of mechanical systems with multiple uncertainties and nonlinear dynamics.
2/20/2026
SoftDTW-CUDA-Torch: Memory-Efficient GPU-Accelerated Soft Dynamic Time Warping for PyTorch
Open-source PyTorch library implementing GPU-accelerated Soft Dynamic Time Warping with improved memory efficiency and numerical stability.
2/20/2026
CounterFlowNet: From Minimal Changes to Meaningful Counterfactual Explanations
ArXiv paper on CounterFlowNet for generating multiple minimal counterfactual explanations for tabular data with heterogeneous features.
2/20/2026
Structured Prototype-Guided Adaptation for EEG Foundation Models
ArXiv research on adapting EEG foundation models with limited supervision using prototype-guided fine-tuning for clinical settings.
2/20/2026
Learning a Latent Pulse Shape Interface for Photoinjector Laser Systems
ArXiv paper applying Wasserstein Autoencoders to control laser pulse shapes in Free-Electron Lasers via differentiable latent interface.
2/20/2026
Unified Latents (UL): How to train your latents
ArXiv research on Unified Latents framework combining diffusion priors with diffusion decoders for efficient latent representation learning.
2/20/2026
RLGT: A reinforcement learning framework for extremal graph theory
ArXiv paper proposing RL framework for extremal graph theory problems, extending Deep Cross-Entropy methods to combinatorial optimization.
2/20/2026
LexiSafe: Offline Safe Reinforcement Learning with Lexicographic Safety-Reward Hierarchy
LexiSafe: Offline safe reinforcement learning method using lexicographic hierarchy to prevent safety violations in cyber-physical systems.
2/20/2026
Flickering Multi-Armed Bandits
Research paper introducing Flickering Multi-Armed Bandits framework where available actions change dynamically, modeled via random graph processes.
2/20/2026
The Sound of Death: Deep Learning Reveals Vascular Damage from Carotid Ultrasound
Machine learning framework using deep learning on carotid ultrasound videos to detect vascular damage for cardiovascular disease risk assessment.
2/20/2026
SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework
Subquadratic structure inference pipeline for immune repertoire analysis combining retrieval, affinity kernels, and multimodal fusion.
2/20/2026
From Subtle to Significant: Prompt-Driven Self-Improving Optimization in Test-Time Graph OOD Detection
Test-time training method using prompts for Graph Neural Network out-of-distribution detection without accessing training data.
2/20/2026
Shortcut learning in geometric knot classification
Study of shortcut learning in neural networks applied to knot topology classification with applications to protein folding and polymer physics.
2/20/2026
A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data
ML framework for biomedical data emphasizing feature stability and interpretability under incomplete/missing data for trustworthy clinical decision-making.
2/20/2026
MDP Planning as Policy Inference
Formulates MDP planning as Bayesian policy inference with return-based optimality prior for discrete action domains.
2/20/2026
Linear Convergence in Games with Delayed Feedback via Extra Prediction
Analyzes convergence of weighted optimistic gradient descent-ascent in bilinear games with feedback delays via prediction.
2/20/2026
Learning with Boolean threshold functions
Develops training method for neural networks with strictly ±1 node values using Boolean threshold functions and constraint formulation.
2/20/2026
Retrospective In-Context Learning for Temporal Credit Assignment with Large Language Models
Uses LLMs for temporal credit assignment in self-evolving agents via retrospective in-context learning from sparse feedback.
2/20/2026
LORA-CRAFT: Cross-layer Rank Adaptation via Frozen Tucker Decomposition of Pre-trained Attention Weights
Introduces CRAFT, parameter-efficient fine-tuning method using Tucker decomposition on frozen pre-trained transformer attention weights.
2/20/2026
Variational inference via radial transport
Proposes radVI algorithm improving variational inference by optimizing radial profiles beyond standard Gaussian approximations.
2/20/2026
The Anxiety of Influence: Bloom Filters in Transformer Attention Heads
Identifies transformer attention heads functioning as membership testers/bloom filters across GPT-2 and Pythia models.
2/20/2026
Position: Evaluation of ECG Representations Must Be Fixed
Position paper arguing for standardized evaluation benchmarks in 12-lead ECG representation learning beyond current arrhythmia-focused datasets.
2/20/2026
MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM Reasoning
Proposes MASPO algorithm improving gradient utilization and sample efficiency in RL for LLM reasoning tasks beyond GRPO limitations.
2/20/2026
A Theoretical Framework for Modular Learning of Robust Generative Models
Theoretical framework for training modular LLMs by combining domain-specific expert models robustly without heuristic dataset weighting.
2/20/2026
Revisiting Weight Regularization for Low-Rank Continual Learning
Investigates weight regularization techniques in parameter-efficient continual learning with low-rank adapters for pre-trained models.
2/20/2026
Be Wary of Your Time Series Preprocessing
Theoretical analysis of normalization strategies and their impact on expressivity of Transformer-based time series models.
2/20/2026
Canonicalizing Multimodal Contrastive Representation Learning
Studies canonicalization of representation spaces across independently trained multimodal contrastive models for consistency.
2/20/2026
Asymptotic Smoothing of the Lipschitz Loss Landscape in Overparameterized One-Hidden-Layer ReLU Networks
Theoretical analysis of loss landscape topology in overparameterized one-hidden-layer ReLU networks with Lipschitz losses.
2/20/2026
Towards Anytime-Valid Statistical Watermarking
Develops anytime-valid statistical watermarking method to distinguish machine-generated text from human content in LLMs.
2/20/2026
Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning
Proposes privacy-preserving federated split learning with intermediate representation protection for distributed ML training.
2/20/2026
Stable Asynchrony: Variance-Controlled Off-Policy RL for LLMs
Addresses variance issues in asynchronous RL training for LLMs using policy-gradient methods like REINFORCE and GRPO on reasoning tasks.
2/20/2026
Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning
arXiv paper on federated learning with incremental data under limited communication. Addresses catastrophic forgetting in privacy-preserving distributed learning scenarios.