4/6/2026
Accelerated Learning with Linear Temporal Logic using Differentiable Simulation
Integration of linear temporal logic specifications into RL using differentiable simulation for safe, correct-by-construction controller synthesis.
4/6/2026
Beyond Noisy-TVs: Noise-Robust Exploration Via Learning Progress Monitoring
Noise-robust exploration method for RL using learning progress monitoring to escape unlearnable noise sources with improved sample efficiency.
4/6/2026
ARMOR: High-Performance Semi-Structured Pruning via Adaptive Matrix Factorization
ARMOR: one-shot post-training pruning algorithm for LLMs achieving 2:4 semi-structured sparsity with minimal performance degradation for efficient deployment.
4/6/2026
High-probability Convergence Guarantees of Decentralized SGD
Theoretical analysis of convergence guarantees for decentralized stochastic gradient descent with high-probability bounds and reduced assumptions.
4/6/2026
Local Reinforcement Learning with Action-Conditioned Root Mean Squared Q-Functions
Extension of Forward-Forward algorithm to reinforcement learning with action-conditioned Q-functions, replacing backpropagation with local learning.
4/6/2026
f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness
f-INE framework for stable influence estimation under training randomness, addressing instability in sample-level impact estimation for data curation.
4/6/2026
Diffusion Models as Dataset Distillation Priors
Dataset distillation method leveraging diffusion models as priors to synthesize compact, representative datasets with improved diversity and generalization.
4/6/2026
Towards best practices in low-dimensional semi-supervised latent Bayesian optimization for the design of antimicrobial peptides
Deep learning approach for antimicrobial peptide design using semi-supervised latent Bayesian optimization with improved interpretability.
4/6/2026
Steering Autoregressive Music Generation with Recursive Feature Machines
MusicRFM framework enabling fine-grained control over pre-trained music generation models by steering internal activations via Recursive Feature Machines.
4/6/2026
Fast and Robust Simulation-Based Inference With Optimization Monte Carlo
Bayesian parameter inference method for complex stochastic simulators using differentiable approaches to reduce simulation costs in high-dimensional spaces.
4/6/2026
Goal-Driven Reward by Video Diffusion Models for Reinforcement Learning
Goal-driven reward signals from pretrained video diffusion models for reinforcement learning agent training.
4/6/2026
Pushing the Limits of Distillation-Based Continual Learning via Classifier-Proximal Lightweight Plugins
Distillation-based continual learning with classifier-proximal plugins addressing stability-plasticity tradeoff.
4/6/2026
Resting Neurons, Active Insights: Robustify Activation Sparsity for Large Language Models
Method to robustify activation sparsity in LLMs by addressing representational instability during inference acceleration.
4/6/2026
Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings
Machine learning approach for early chronic kidney disease screening in low-resource settings using explainable models.
4/6/2026
Forget Many, Forget Right: Scalable and Precise Concept Unlearning in Diffusion Models
Scalable multi-concept unlearning in text-to-image diffusion models addressing weight conflicts and collateral damage.
4/6/2026
On the Extreme Variance of Certified Local Robustness Across Model Seeds
Analysis of extreme variance in certified robustness verification across neural network model seeds.
4/6/2026
Textual Equilibrium Propagation for Deep Compound AI Systems
Textual Equilibrium Propagation for optimizing compound AI systems with multiple modules in long-horizon agentic workflows.
4/6/2026
Early Classification of Time Series in Non-Stationary Cost Regimes
Early classification of time series in non-stationary environments with uncertain and time-varying decision costs.
4/6/2026
ChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphs
ChronoSpike: Adaptive spiking graph neural network for dynamic graph representation learning with event-driven efficiency.
4/6/2026
When RL Meets Adaptive Speculative Training: A Unified Training-Serving System
Unified training-serving system combining RL with adaptive speculative decoding for accelerated LLM inference.
4/6/2026
Infusion: Shaping Model Behavior by Editing Training Data via Influence Functions
Infusion: Framework using influence functions to craft training data perturbations that induce targeted model behavior changes.
4/6/2026
Equivariant Evidential Deep Learning for Interatomic Potentials
Uncertainty quantification for machine learning interatomic potentials using evidential deep learning.
4/6/2026
Low-Dimensional and Transversely Curved Optimization Dynamics in Grokking
Geometric analysis of optimization dynamics in transformers trained on modular arithmetic revealing low-dimensional subspaces.
4/6/2026
Early-Warning Signals of Grokking via Loss-Landscape Geometry
Study on early-warning signals of grokking via loss-landscape geometry on SCAN and Dyck-1 benchmarks.
4/6/2026
CeRA: Overcoming the Linear Ceiling of Low-Rank Adaptation via Capacity Expansion
CeRA: Parameter-efficient fine-tuning method extending LoRA with non-linear capacity expansion via gating and dropout.
4/6/2026
Learning Physical Operators using Neural Operators
Physics-informed neural operators for solving PDEs with improved generalization beyond training distributions.
4/6/2026
SafeSci: Safety Evaluation of Large Language Models in Science Domains and Beyond
SafeSci: Framework for evaluating safety of large language models in scientific domains with comprehensive benchmarks.
4/6/2026
CRISP: Compressed Reasoning via Iterative Self-Policy Distillation
CRISP: Method for teaching LLMs to reason more concisely via self-distillation with 'be concise' conditioning.
4/6/2026
Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis
Stock market prediction using Node Transformer and BERT sentiment analysis for financial forecasting.
4/6/2026
Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
WinDiNet uses pretrained video diffusion model as differentiable physics simulator for urban wind flow prediction, replacing expensive CFD simulations.
4/6/2026
$\lambda$-GELU: Learning Gating Hardness for Controlled ReLU-ization in Deep Networks
λ-GELU parameterized gating function enabling controlled ReLU conversion while maintaining smooth activation properties for deployment.
4/6/2026
ERPO: Token-Level Entropy-Regulated Policy Optimization for Large Reasoning Models
ERPO method for token-level credit assignment in LLM reasoning models, addressing entropy collapse in GRPO through information heterogeneity.
4/6/2026
Temporal Credit Is Free
Recurrent network training without Jacobian propagation using hidden state temporal credit. Studies gradient normalization and online adaptation.
4/6/2026
The Spectral Edge Thesis: A Mathematical Framework for Intra-Signal Phase Transitions in Neural Network Training
Mathematical framework explaining phase transitions in neural network training via spectral gap of parameter update Gram matrices. Grokking and capability gains analysis.
4/6/2026
Transfer learning for nonparametric Bayesian networks
Transfer learning for nonparametric Bayesian networks under scarce data. Proposes PC-stable-transfer and hill climbing transfer learning methods.
4/6/2026
Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Tutorial on Bayesian Optimization for automating scientific discovery using surrogate models and probability-driven frameworks.
4/6/2026
annbatch unlocks terabyte-scale training of biological data in anndata
annbatch: mini-batch loader for terabyte-scale biological data in AnnData format, addressing memory bottlenecks in ML training on large datasets.
4/6/2026
Central Limit Theorems for Stochastic Gradient Descent Quantile Estimators
arXiv paper developing asymptotic theory for quantile estimation via stochastic gradient descent with constant learning rate.
4/6/2026
Learn then Decide: A Learning Approach for Designing Data Marketplaces
arXiv paper introducing MAPP mechanism for efficient data marketplace pricing using learned value distributions.
4/6/2026
gen2seg: Generative Models Enable Generalizable Instance Segmentation
arXiv paper on gen2seg: using generative models (Stable Diffusion, MAE) for category-agnostic instance segmentation.
4/6/2026
LMask: Learn to Solve Constrained Routing Problems with Lazy Masking
arXiv paper proposing LMask, a learning framework using dynamic masking for constrained routing problems optimization.
4/6/2026
Are Statistical Methods Obsolete in the Era of Deep Learning? A Study of ODE Inverse Problems
arXiv paper comparing deep learning neural networks against statistical methods for solving ODE inverse problems.
4/6/2026
Decoding RWA Tokenized U.S. Treasuries: Functional Dissection and Address Role Inference
arXiv paper analyzing tokenized U.S. Treasuries transactions on blockchain infrastructure.
4/6/2026
Constrained free energy minimization for the design of thermal states and stabilizer thermodynamic systems
arXiv paper on constrained free energy minimization for quantum thermodynamic system design.
4/6/2026
LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade
arXiv paper analyzing 150+ years of German parliamentary migration debates using LLMs, revealing shift from post-war solidarity to anti-solidarity.
4/6/2026
ROPA: Synthetic Robot Pose Generation for RGB-D Bimanual Data Augmentation
arXiv paper on ROPA: synthetic robot pose generation for RGB-D bimanual data augmentation to improve imitation learning policies.
4/6/2026
Adaptive randomized pivoting and volume sampling
Algorithm for column subset selection using adaptive randomized pivoting with connections to volume sampling.
4/6/2026
Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference
Forecasting data movement patterns in MoE LLM inference to reduce bottlenecks in multi-unit serving systems.
4/6/2026
Fast Best-in-Class Regret for Contextual Bandits
Fast regret bounds for contextual bandits without realizability assumptions using pessimistic policy updates.
4/6/2026
Seer: Online Context Learning for Fast Synchronous LLM Reinforcement Learning
Seer: context learning RL system for fast synchronous LLM training, addressing rollout latency and resource utilization.