3/31/2026
GIFT: Bootstrapping Image-to-CAD Program Synthesis via Geometric Feedback
Image-to-CAD program synthesis using geometric feedback for bootstrapping alignment between visual and symbolic representations.
3/31/2026
FlowRL: A Taxonomy and Modular Framework for Reinforcement Learning with Diffusion Policies
Modular framework and taxonomy for reinforcement learning with diffusion and flow models as policy representations.
3/31/2026
TurboAngle: Near-Lossless KV Cache Compression via Uniform Angle Quantization
KV cache compression via uniform angle quantization in Fast Walsh-Hadamard domain with per-layer precision allocation.
3/31/2026
KV Cache Quantization for Self-Forcing Video Generation: A 33-Method Empirical Study
Empirical study of 33 KV cache quantization methods for self-forcing video generation with memory optimization.
3/31/2026
On Token's Dilemma: Dynamic MoE with Drift-Aware Token Assignment for Continual Learning of Large Vision Language Models
Mixture of Experts with drift-aware token routing for continual instruction tuning of large vision language models.
3/31/2026
Match or Replay: Self Imitating Proximal Policy Optimization
Self-imitating proximal policy optimization algorithm improving exploration efficiency in sparse reward reinforcement learning.
3/31/2026
BLOSSOM: Block-wise Federated Learning Over Shared and Sparse Observed Modalities
Federated learning framework for multimodal data with heterogeneous clients and missing modalities using block-wise approach.
3/31/2026
On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry
Theoretical analysis of self-supervised pre-training using two-stage M-estimation and representation symmetry to improve bounds.
3/31/2026
Prototype-Aligned Federated Soft-Prompts for Continual Web Personalization
Federated soft-prompts framework for continual web personalization with privacy preservation and stability-plasticity control.
3/31/2026
Optimizing Coverage and Difficulty in Reinforcement Learning for Quiz Composition
Reinforcement learning agents (DQN, SARSA, A2C/A3C) for automated quiz composition with topic coverage and difficulty optimization.
3/31/2026
Low-Rank Adaptation Reduces Catastrophic Forgetting in Sequential Transformer Encoder Fine-Tuning: Controlled Empirical Evidence and Frozen-Backbone Representation Probes
Empirical study of Low-Rank Adaptation (LoRA) in sequential fine-tuning of transformer encoders, analyzing catastrophic forgetting behavior.
3/31/2026
What-If Explanations Over Time: Counterfactuals for Time Series Classification
Survey of counterfactual explanation algorithms for time series classification, covering instance-based, pattern-driven, and gradient-based methods.
3/31/2026
RG-TTA: Regime-Guided Meta-Control for Test-Time Adaptation in Streaming Time Series
RG-TTA: Meta-controller for test-time adaptation in streaming time series forecasting that modulates adaptation intensity based on regime similarity.
3/31/2026
KVSculpt: KV Cache Compression as Distillation
KVSculpt: KV cache compression for long-context LLM inference treating compression as knowledge distillation, orthogonal to quantization and low-rank methods.
3/31/2026
Spectral Signatures of Data Quality: Eigenvalue Tail Index as a Diagnostic for Label Noise in Neural Networks
Eigenvalue tail index of neural network weight matrices predicts test accuracy under label noise, achieving R^2=0.984 as diagnostic for data quality.
3/31/2026
ATLAS-RTC: Closing the Loop on LLM Agent Output with Token-Level Runtime Control
ATLAS-RTC: Runtime control system for LLM agents that enforces structured output via token-level monitoring, drift detection, and closed-loop interventions during decoding.
3/31/2026
ITQ3_S: High-Fidelity 3-bit LLM Inference via Interleaved Ternary Quantization with Rotation-Domain Smoothing
ITQ3_S: 3-bit weight quantization method for LLM inference using rotation-domain adaptive quantization to reduce precision loss from weight distribution outliers.
3/31/2026
Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute
Proteina-Complexa: fully atomistic protein binder generation method combining conditional generative modeling with structure-based optimization.
3/31/2026
Efficient Inference of Large Vision Language Models
Optimization techniques for efficient inference in large vision-language models addressing computational bottlenecks from high-resolution visual tokens.
3/31/2026
Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guarantees
Distributed stochastic gradient descent with game-theoretic incentives to prevent gradient manipulation by strategic agents while ensuring convergence.
3/31/2026
Principal Prototype Analysis on Manifold for Interpretable Reinforcement Learning
Principal Prototype Analysis on Manifold: interpretability method for reinforcement learning agents using prototype-based explanations.
3/31/2026
From Independent to Correlated Diffusion: Generalized Generative Modeling with Probabilistic Computers
Generalization of diffusion models to correlated stochastic sampling using probabilistic computers beyond standard neural network implementations.
3/31/2026
FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning
FedDES: graph-based dynamic ensemble selection for personalized federated learning that addresses negative transfer through selective peer integration.
3/31/2026
Diffusion Maps is not Dimensionality Reduction
Analysis of diffusion maps showing they provide spectral representation of geometry rather than dimensionality reduction, compared with Isomap and UMAP.
3/31/2026
Reducing Oracle Feedback with Vision-Language Embeddings for Preference-Based RL
ROVED: hybrid reinforcement learning framework combining vision-language embeddings with oracle feedback to reduce annotation costs for reward learning.
3/31/2026
Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection
Koopman-based surrogate models for RL control of fluid dynamics with mitigation of distribution shifts.
3/31/2026
InkDrop: Invisible Backdoor Attacks Against Dataset Condensation
InkDrop demonstrates backdoor attacks against dataset condensation methods through invisible trigger implantation.
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
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
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
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.