4/7/2026
Improving RCT-Based CATE Estimation Under Covariate Mismatch via Calibrated Alignment
CALM: method for heterogeneous treatment effect estimation combining RCT and observational study data with covariate mismatch.
4/7/2026
How Out-of-Equilibrium Phase Transitions can Seed Pattern Formation in Trained Diffusion Models
Theoretical analysis of pattern formation in diffusion models explained via out-of-equilibrium phase transitions.
4/7/2026
Learning Sampled-data Control for Swarms via MeanFlow
MeanFlow-based learning approach for controlling large-scale swarms with limited sampled-data updates.
4/7/2026
LLM-ODE: Data-driven Discovery of Dynamical Systems with Large Language Models
LLM-ODE: uses LLMs to discover governing equations of dynamical systems from data, improving on genetic programming approaches.
4/7/2026
ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization
ALMAB-DC: sequential experimental design framework combining active learning, multi-armed bandits and distributed computing for black-box optimization.
4/7/2026
Posterior-Calibrated Causal Circuits in Variational Autoencoders: Why Image-Domain Interpretability Fails on Tabular Data
Investigates interpretability of VAEs across modalities, showing image-domain causal circuits fail to generalize to tabular data.
4/7/2026
Uncertainty Quantification for Distribution-to-Distribution Flow Matching in Scientific Imaging
Uncertainty quantification methods for distribution-to-distribution flow matching models in scientific imaging applications.
4/7/2026
CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning
CellFluxRL: reinforcement learning post-training approach for virtual cell models with biologically-constrained generative models.
4/7/2026
Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions
Causal discovery method for chain-reaction systems using interventional data on cascade-structured dynamical systems.
4/7/2026
Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions
Federated learning approach combining Byzantine robustness and differential privacy for distributed training.
4/7/2026
Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning
Framework using LLMs to automatically design auxiliary reward programs for cooperative multi-agent reinforcement learning systems.
4/7/2026
Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch
Compares LLM agents against classical hyperparameter optimization algorithms using autoresearch testbed for tuning small language models.
4/7/2026
Optimal High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback
Theoretical analysis of online convex optimization with two-point bandit feedback achieving tight regret bounds.
4/7/2026
ATLAS-RTC: Closing the Loop on LLM Agent Output with Token-Level Runtime Control
ATLAS-RTC: runtime control system for LLM agents enforcing structured output via token-level monitoring, biasing, masking and rollback.
4/7/2026
HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention
HISA: hierarchical indexing system for efficient sparse attention in LLMs, reducing indexer bottleneck in token-level sparse mechanisms.
4/7/2026
Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework
Develops interpretable ML framework for detecting low left ventricular ejection fraction from ECG data.
4/7/2026
See it to Place it: Evolving Macro Placements with Vision-Language Models
Applies Vision-Language Models to chip floorplanning macro placement optimization tasks.
4/7/2026
Rethinking Language Model Scaling under Transferable Hypersphere Optimization
Introduces HyperP, hypersphere parameterization for LLM scaling with improved stability and hyperparameter transfer.
4/7/2026
Beta-Scheduling: Momentum from Critical Damping as a Diagnostic and Correction Tool for Neural Network Training
Proposes time-varying momentum schedule derived from critically damped harmonic oscillator for neural network training optimization.
4/7/2026
ReproMIA: A Comprehensive Analysis of Model Reprogramming for Proactive Membership Inference Attacks
Research on membership inference attacks against deep learning models using model reprogramming to reduce computational costs of privacy auditing.
4/7/2026
On the Mirage of Long-Range Dependency, with an Application to Integer Multiplication
arXiv paper analyzing integer multiplication as hard problem for neural networks. Theoretical analysis challenging assumptions about long-range dependencies in neural computation.
4/7/2026
Realistic Market Impact Modeling for Reinforcement Learning Trading Environments
arXiv paper on realistic market impact modeling for RL trading agents. Gymnasium-compatible environments with nonlinear transaction costs for algorithmic trading research.
4/7/2026
Task-Centric Personalized Federated Fine-Tuning of Language Models
arXiv paper on personalized federated fine-tuning of language models. Federated learning approach for task-centric LLM adaptation on private distributed data.
4/7/2026
Convergence of Byzantine-Resilient Gradient Tracking via Probabilistic Edge Dropout
arXiv paper on Byzantine-resilient distributed optimization with probabilistic edge dropout. Convergence analysis for distributed learning with adversarial agents.
4/7/2026
Spectral Compact Training: Pre-Training Large Language Models via Permanent Truncated SVD and Stiefel QR Retraction
arXiv paper on memory-efficient LLM pre-training via truncated SVD factorization. Method reduces memory footprint for training large language models on consumer hardware.
4/7/2026
Using predefined vector systems to speed up neural network multimillion class classification
arXiv paper on O(1) complexity label prediction for neural networks with millions of classes. Optimization technique for efficient classification in high-dimensional spaces.
4/7/2026
Fatigue-Aware Learning to Defer via Constrained Optimisation
arXiv paper on human-AI cooperation via fatigue-aware deferral systems. ML method modeling human fatigue to optimize when AI should defer to humans.
4/7/2026
Screening Is Enough
arXiv paper introducing Multiscreen attention mechanism for language models. Alternative to softmax attention enabling absolute relevance scoring in transformers.
4/7/2026
Apriel-1.5-OpenReasoner: RL Post-Training for General-Purpose and Efficient Reasoning
arXiv paper on reinforcement learning post-training for reasoning models. Open-weight model training using verifiable rewards across diverse reasoning domains.
4/7/2026
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
arXiv paper on opponent modeling in game-theoretic reinforcement learning using tree-search and generative models. Research on scalable multi-agent RL methods.
4/7/2026
Accelerated Gradient Methods for Nonconvex Optimization: Escape Trajectories From Strict Saddle Points and Convergence to Local Minima
Theoretical analysis of accelerated gradient methods for nonconvex optimization and convergence to local minima.
4/7/2026
A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis
Multi-agent reinforcement learning framework for HIV prevention policy optimization across U.S. regions.
4/7/2026
Floralens: a Deep Learning Model for the Portuguese Native Flora
Deep CNN model trained on Portuguese native flora dataset for species identification in citizen science.
4/7/2026
Robust Adaptation of Foundation Models with Black-Box Visual Prompting
Black-box visual prompting method for parameter-efficient transfer learning of foundation models without full parameter access.
4/7/2026
SPRIG: Improving Large Language Model Performance by System Prompt Optimization
SPRIG: Genetic algorithm for optimizing system prompts in LLMs to improve task performance.
4/7/2026
MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Data
MissNODAG: Framework for learning cyclic causal graphs from incomplete data using differentiable methods.
4/7/2026
Sparse Max-Affine Regression
Sparse Gradient Descent algorithm for variable selection in convex piecewise linear regression models.
4/7/2026
Score-matching-based Structure Learning for Temporal Data on Networks
Score-matching causal discovery algorithm extended for temporal data on networks.
4/7/2026
From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detection
XAI-based method combining explainability with concept drift detection for monitoring model performance degradation.
4/7/2026
Post-detection inference for sequential changepoint localization
Framework for constructing confidence sets for changepoints in sequential analysis using data-dependent stopping times.
4/7/2026
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning
World models using disentangled representations to transfer semantic knowledge from distracting videos for RL agents.
4/7/2026
Towards Build Optimization Using Digital Twins
Digital twins framework for optimizing CI/CD build processes to reduce duration, failures, and flakiness.
4/7/2026
Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks
Online test-time adaptation for spiking neural networks on neuromorphic chips to handle distribution shifts.
4/7/2026
From Seeing to Doing: Bridging Reasoning and Decision for Robotic Manipulation
FSD framework combining vision-language models with robotic action models for zero-shot manipulation in novel scenarios.
4/7/2026
Informatics for Food Processing
Review of ML/AI applications in food processing, classification systems, and food informatics.
4/7/2026
Operator Learning for Schr\"{o}dinger Equation: Unitarity, Error Bounds, and Time Generalization
Neural network surrogate for learning evolution operators in time-dependent Schrödinger equations with unitarity constraints.
4/7/2026
Gaussian mixture models as a proxy for interacting language models
Gaussian mixture models as computationally efficient proxy for LLM+RAG systems combining multiple models.
4/7/2026
Common Inpainted Objects In-N-Out of Context
COinCO dataset with 97,722 images created via diffusion-based inpainting for training context-aware vision models.
4/7/2026
Learning thermodynamic master equations for open quantum systems
Machine learning methods for learning Hamiltonian components of open quantum systems.
4/7/2026
Accelerating Constrained Sampling: A Large Deviations Approach
Large deviations approach to accelerate constrained sampling algorithms for probability distributions.