3/31/2026
Heddle: A Distributed Orchestration System for Agentic RL Rollout
Heddle is a distributed system for orchestrating agentic RL rollouts with LLMs to address trajectory generation bottlenecks.
3/31/2026
Lipschitz verification of neural networks through training
Training methodology to verify and bound Lipschitz constants of neural networks for adversarial robustness and generalization.
3/31/2026
Graph Vector Field: A Unified Framework for Multimodal Health Risk Assessment from Heterogeneous Wearable and Environmental Data Streams
GVF framework models health risk as vector fields on simplicial complexes from multimodal wearable and environmental data.
3/31/2026
Neural Federated Learning for Livestock Growth Prediction
Federated learning approach for livestock growth prediction addressing privacy concerns and limited data availability.
3/31/2026
ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment
ORACAL is a multimodal GNN framework for smart contract vulnerability detection with causal graphs and explainability.
3/31/2026
Automating Early Disease Prediction Via Structured and Unstructured Clinical Data
Automated methodology for early disease prediction from structured and unstructured clinical data using NLP.
3/31/2026
Skillful Kilometer-Scale Regional Weather Forecasting via Global and Regional Coupling
Global-regional coupling framework using Transformers for kilometer-scale regional weather forecasting.
3/31/2026
Policy-Controlled Generalized Share: A General Framework with a Transformer Instantiation for Strictly Online Switching-Oracle Tracking
PCGS framework for strictly online prediction under non-stationarity with Transformer instantiation for expert switching.
3/31/2026
A Perturbation Approach to Unconstrained Linear Bandits
Perturbation-based approach for unconstrained bandit linear optimization with improved regret guarantees.
3/31/2026
ERPO: Token-Level Entropy-Regulated Policy Optimization for Large Reasoning Models
ERPO uses token-level entropy-regulated policy optimization to improve credit assignment in reinforcement learning for language models.
3/31/2026
Variational Neurons in Transformers for Language Modeling
Variational neurons in Transformer feed-forward layers to incorporate uncertainty into internal computation for language modeling.
3/31/2026
Detecting the Unexpected: AI-Driven Anomaly Detection in Smart Bridge Monitoring
AI-driven anomaly detection system for smart bridge monitoring using infrastructure data.
3/31/2026
MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations
MR-CDM framework for multi-resolution time series generation using hierarchical decomposition and diffusion models.
3/31/2026
Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback
Study on robustness against data corruption in offline multi-agent reinforcement learning from human feedback.
3/31/2026
Pre-Deployment Complexity Estimation for Federated Perception Systems
Framework for estimating learning complexity and communication costs in federated learning systems before deployment.
3/31/2026
FI-KAN: Fractal Interpolation Kolmogorov-Arnold Networks
FI-KAN introduces fractal interpolation function bases into Kolmogorov-Arnold Networks for improved multi-scale function approximation.
3/31/2026
OptINC: Optical In-Network-Computing for Scalable Distributed Learning
arXiv paper proposing optical in-network computing to reduce communication overhead in distributed machine learning systems.
3/31/2026
LIBERO-Para: A Diagnostic Benchmark and Metrics for Paraphrase Robustness in VLA Models
arXiv paper introducing LIBERO-Para benchmark to evaluate robustness of Vision-Language-Action models to paraphrased instructions in robotic tasks.
3/31/2026
Taming the Instability: A Robust Second-Order Optimizer for Federated Learning over Non-IID Data
arXiv paper proposing FedRCO, a second-order optimization framework for federated learning with improved stability under non-IID data.
3/31/2026
FairGC: Fairness-aware Graph Condensation
arXiv paper addressing fairness issues in graph condensation, preventing amplification of demographic biases during dataset compression.
3/31/2026
Physics-Informed Neural Networks for Predicting Hydrogen Sorption in Geological Formations: Thermodynamically Constrained Deep Learning Integrating Classical Adsorption Theory
arXiv paper using physics-informed neural networks to predict hydrogen sorption in geological formations with thermodynamic constraints.
3/31/2026
Machine Learning-Assisted High-Dimensional Matrix Estimation
arXiv paper integrating learning-based optimization with classical statistical methods for efficient high-dimensional matrix estimation.
3/31/2026
Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids
arXiv paper applying deep reinforcement learning to maritime coverage path planning on irregular hexagonal grids.
3/31/2026
Label-efficient Training Updates for Malware Detection over Time
arXiv paper addressing label-efficient retraining of malware detection models under distribution drift in real-world settings.
3/31/2026
Mixture-Model Preference Learning for Many-Objective Bayesian Optimization
arXiv paper on Bayesian framework for preference learning in many-objective optimization using mixture models of latent preference archetypes.
3/31/2026
Evolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models
arXiv paper presenting evolutionary framework using LLMs to discover novel reinforcement learning algorithms by searching over executable update rules.
3/31/2026
KGroups: A Versatile Univariate Max-Relevance Min-Redundancy Feature Selection Algorithm for High-dimensional Biological Data
arXiv paper introducing KGroups, a feature selection algorithm for high-dimensional biological data using max-relevance min-redundancy criteria.
3/31/2026
IsoQuant: Hardware-Aligned SO(4) Isoclinic Rotations for LLM KV Cache Compression
IsoQuant uses quaternion algebra and isoclinic rotations for efficient LLM KV cache compression with hardware-aligned blockwise operations.
3/31/2026
FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation
FeDMRA addresses federated class-incremental learning with dynamic memory replay for non-IID distributed healthcare data.
3/31/2026
HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention
HISA improves efficiency of token-level sparse attention mechanisms through hierarchical indexing, reducing O(L²) bottleneck.
3/31/2026
The Unreasonable Effectiveness of Scaling Laws in AI
Analysis of scaling laws in AI across model families, explaining their predictive power and universal effectiveness in training loss reduction.
3/31/2026
Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework
Interpretable machine learning framework for detecting low left ventricular ejection fraction from ECG data using predictor-driven approach.
3/31/2026
CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments
CirrusBench evaluates LLM-based agents in real-world cloud service environments beyond correctness, measuring robustness and efficiency.
3/31/2026
Unrestrained Simplex Denoising for Discrete Data. A Non-Markovian Approach Applied to Graph Generation
Simplex denoising framework for discrete generative modeling using non-Markovian noising scheme, applied to graph generation.
3/31/2026
Learning Partial Action Replacement in Offline MARL
Offline multi-agent reinforcement learning approach using Partial Action Replacement to handle exponential joint action space growth.
3/31/2026
ChemCLIP: Bridging Organic and Inorganic Anticancer Compounds Through Contrastive Learning
ChemCLIP uses contrastive learning to bridge organic and inorganic anticancer compound discovery by enabling knowledge transfer across chemical domains.
3/31/2026
Physics-Informed Framework for Impact Identification in Aerospace Composites
Physics-informed impact identification framework combining observational and inductive biases for aerospace composite damage detection.
3/31/2026
Position: Explainable AI is Causality in Disguise
Position paper arguing explainable AI fundamentally relies on causal inference rather than diverse disconnected XAI methods.
3/31/2026
LACE: Loss-Adaptive Capacity Expansion for Continual Learning
LACE mechanism for continual learning that adaptively expands model capacity during training based on loss signal monitoring.
3/31/2026
Information-Theoretic Limits of Safety Verification for Self-Improving Systems
Information-theoretic analysis of safety verification impossibility for self-improving systems balancing bounded risk with unbounded utility.
3/31/2026
Mitigating Backdoor Attacks in Federated Learning Using PPA and MiniMax Game Theory
Federated learning defense using PPA and game theory minimax approaches against backdoor attacks from malicious clients.
3/31/2026
AMIGO: Agentic Multi-Image Grounding Oracle Benchmark
AMIGO benchmark for evaluating agentic vision-language models on long-horizon multi-image grounding tasks through sequential attribute-focused queries.
3/31/2026
FL-PBM: Pre-Training Backdoor Mitigation for Federated Learning
FL-PBM method for detecting and mitigating backdoor attacks in federated learning during pre-training phases.
3/31/2026
Subspace Optimization for Backpropagation-Free Continual Test-Time Adaptation
PACE system for backpropagation-free test-time adaptation by optimizing normalization layer parameters using covariance matrix approaches.
3/31/2026
GPU-Accelerated Optimization of Transformer-Based Neural Networks for Real-Time Inference
GPU-accelerated TensorRT inference pipeline for BERT and GPT-2 with mixed-precision optimization achieving 64.4x CPU speedup.
3/31/2026
Stepwise Credit Assignment for GRPO on Flow-Matching Models
Stepwise credit assignment method for GRPO on flow models that differentiates early composition steps from late detail-refinement steps.
3/31/2026
See it to Place it: Evolving Macro Placements with Vision-Language Models
VeoPlace uses vision-language models for chip floorplanning macro placement by leveraging VLM spatial reasoning abilities to complement learning-based approaches.
3/31/2026
Expectation Error Bounds for Transfer Learning in Linear Regression and Linear Neural Networks
Theoretical analysis of transfer learning in linear regression and neural networks with closed-form generalization error bounds.
3/31/2026
Rethinking Language Model Scaling under Transferable Hypersphere Optimization
HyperP introduces hypersphere parameterization for language model scaling with improved training stability compared to first-order optimizer approaches.
3/31/2026
Stop Probing, Start Coding: Why Linear Probes and Sparse Autoencoders Fail at Compositional Generalisation
Analysis of why linear probes and sparse autoencoders fail at compositional generalization under superposition, proposing iterative coding alternatives.