4/7/2026
A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning
Self-evolving Post-Training (SePT) method enabling LLMs to improve reasoning without external rewards through self-generated training data.
4/7/2026
An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning
Information-theoretic analysis of out-of-distribution generalization in meta-reinforcement learning with bounds under distribution shift scenarios.
4/7/2026
SPORE: Skeleton Propagation Over Recalibrating Expansions
Classical clustering algorithm handling arbitrary cluster geometry without global density assumptions using skeleton propagation and recalibrating expansions.
4/7/2026
SynQuE: Estimating Synthetic Dataset Quality Without Annotations
Method for ranking synthetic datasets by real-world performance without annotations, establishing benchmarks for synthetic data quality estimation.
4/7/2026
Controllable protein design with particle-based Feynman-Kac steering
Feynman-Kac framework for guiding diffusion-based generative models toward proteins with specified properties and tailored structures.
4/7/2026
A Unified Stability Analysis of SAM vs SGD: Role of Data Coherence and Emergence of Simplicity Bias
Unified stability analysis comparing SAM and SGD optimization algorithms showing role of data coherence and simplicity bias in generalization.
4/7/2026
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection
Comparative analysis of transformer models (DistilBERT) versus psycholinguistic features for detecting business email compromise attacks.
4/7/2026
Mitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation
Framework addressing structural overfitting in graph neural networks for missing feature imputation using distribution-aware rectification.
4/7/2026
Personalized Federated Distillation Assisted Vehicle Edge Caching Strategy
Federated learning approach for vehicle edge caching using personalized distillation to predict user content preferences while preserving privacy.
4/7/2026
Random-Bridges as Stochastic Transports for Generative Models
Random-bridges framework for generative models using stochastic processes conditioned on target distributions for flexible transport between distributions.
4/7/2026
GRAFT: Grid-Aware Load Forecasting with Multi-Source Textual Alignment and Fusion
Electric load forecasting model integrating multi-source textual data (news, social media, policies) with temporal grid-aware predictions.
4/7/2026
Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics
Mamba-based neural operator framework for accurate chemical kinetics modeling in combustion simulations using efficient temporal modeling.
4/7/2026
SFBD-OMNI: Bridge models for lossy measurement restoration with limited clean samples
Distribution restoration method using noisy samples and optimal transport to recover fully observed data from partial corrupted observations.
4/7/2026
Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models
Method using large language models to measure semantic similarity in categorical data clustering by bridging gap in attribute distance representation.
4/7/2026
ASSS: A Differentiable Adversarial Framework for Task-Aware Data Reduction
Differentiable adversarial framework for task-aware data reduction using learnable selector and minimax optimization to identify informative samples.
4/7/2026
From Bits to Chips: An LLM-based Hardware-Aware Quantization Agent for Streamlined Deployment of LLMs
LLM-based hardware-aware quantization agent automating model quantization for efficient LLM deployment on resource-constrained hardware.
4/7/2026
Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective
Theoretical analysis of implicit bias in stochastic learning using geometric perspective to explain solution selection in overparameterized models.
4/7/2026
HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Training
Split learning optimization method reducing memory overhead for LLM training on edge devices using hybrid-order optimization instead of first-order approaches.
4/7/2026
Auxiliary-predicted Compress Memory Model(ApCM Model): A Neural Memory Storage Model Based on Invertible Compression and Learnable Prediction
Neural memory storage architecture for LLMs with invertible compression and learnable prediction for runtime memory.
4/7/2026
InfoTok: Information-Theoretic Regularization for Capacity-Constrained Shared Visual Tokenization in Unified MLLMs
Information-theoretic approach for designing shared visual tokenizers in unified multimodal LLMs.
4/7/2026
RASA: Routing-Aware Safety Alignment for Mixture-of-Experts Models
Safety alignment framework addressing unique challenges of sparse routing in Mixture-of-Experts language models.
4/7/2026
ModalImmune: Immunity Driven Unlearning via Self Destructive Training
Training framework for multimodal systems to maintain performance when input channels are lost at deployment.
4/7/2026
Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems
Pruning framework for physics-informed neural networks to improve robustness to noise in PDE inverse problems.
4/7/2026
T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
Time series imputation method using channel-head binding for handling diverse missing patterns.
4/7/2026
MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier
Training method for LLMs to directly model generative reasoning process in scientific discovery applications.
4/7/2026
NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning algorithm for general-sum games with convergence guarantees in heterogeneous agent settings.
4/7/2026
OptEMA: Adaptive Exponential Moving Average for Stochastic Optimization with Zero-Noise Optimality
Optimization algorithm extending exponential moving average with adaptive rates and zero-noise optimality guarantees.
4/7/2026
A Grammar of Machine Learning Workflows
Formal grammar framework preventing data leakage in ML workflows through structural constraints and assessment gates.
4/7/2026
Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings
Reinforcement learning method for post-training reasoning models using hindsight feedback in sparse reward environments.
4/7/2026
Security Considerations for Artificial Intelligence Agents
Security considerations and recommendations for AI agents from Perplexity based on operating agentic systems in production environments.
4/7/2026
Brittlebench: Quantifying LLM robustness via prompt sensitivity
Framework for measuring LLM robustness to prompt variations, typos, and alternative phrasings in real-world inputs.
4/7/2026
AI-Driven Predictive Maintenance with Environmental Context Integration for Connected Vehicles: Simulation, Benchmarking, and Field Validation
Predictive maintenance framework for connected vehicles integrating sensor and environmental data with ML models.
4/7/2026
Accelerating Byzantine-Robust Distributed Learning with Compressed Communication via Double Momentum and Variance Reduction
Distributed learning algorithm combining Byzantine robustness with communication compression for collaborative ML systems.
4/7/2026
Path-Constrained Mixture-of-Experts
Research on sparse Mixture-of-Experts architectures proposing expert path perspective to understand token routing patterns across layers.
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
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
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
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.