3/9/2026
Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion
RL-based method for retrieving diverse, property-aligned result sets using diffusion models for set-valued retrieval objectives.
3/9/2026
U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach
Dataset and neural network approach for radiomap prediction in 6G wireless systems with extra-large MIMO arrays.
3/9/2026
A Reference Architecture of Reinforcement Learning Frameworks
Reference architecture framework analyzing 18 RL implementations to establish common patterns and standardization for RL frameworks.
3/9/2026
CLoPA: Continual Low Parameter Adaptation of Interactive Segmentation for Medical Image Annotation
Continual learning strategy for online adaptation of interactive segmentation models in medical imaging with low-parameter updates.
3/9/2026
Certified and accurate computation of function space norms of deep neural networks
Method for computing certified bounds on function space norms of deep neural networks applied to PDE solutions.
3/9/2026
Quantum Diffusion Models: Score Reversal Is Not Free in Gaussian Dynamics
Theoretical analysis of quantum diffusion models and score reversal in Gaussian dynamics with complete positivity constraints.
3/9/2026
Semantics-Aware Caching for Concept Learning
Optimization technique using semantic-aware caching to improve instance retrieval efficiency in concept learning on knowledge bases.
3/9/2026
Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education
ML system trained on 45k+ ultrasound images to detect fetal orofacial clefts prenatally, addressing shortage of specialist expertise.
3/9/2026
Boosting deep Reinforcement Learning using pretraining with Logical Options
Two-stage hybrid framework combining logical options with deep reinforcement learning to improve agent alignment and prevent over-exploitation of early reward signals.
3/9/2026
SCOPE: Scene-Contextualized Incremental Few-Shot 3D Segmentation
SCOPE incremental few-shot 3D point cloud segmentation addressing catastrophic forgetting by leveraging unlabeled background scenes in sparse supervision settings.
3/9/2026
BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations
BEVLM distills semantic knowledge from LLMs into bird's-eye view representations for autonomous driving with improved spatial consistency and reduced computation.
3/9/2026
A Cognitive Explainer for Fetal ultrasound images classifier Based on Medical Concepts
Cognitive explainer using medical concepts to interpret deep neural network decisions for fetal ultrasound standard scan plane detection with transparency and interpretability.
3/9/2026
Expert-Aided Causal Discovery of Ancestral Graphs
Expert-aided causal discovery method incorporating background knowledge ex-post to improve reliability of ancestral graph discovery while minimizing expert probing costs.
3/9/2026
A unified framework for learning with nonlinear model classes from arbitrary linear samples
Unified framework for learning with nonlinear model classes from arbitrary linear measurements in Hilbert spaces with novel data-dependent learning guarantees.
3/9/2026
Estimation of Energy-dissipation Lower-bounds for Neuromorphic Learning-in-memory
Energy-dissipation analysis for neuromorphic learning-in-memory optimizers using compute-in-memory paradigm to address memory-wall and energy bottlenecks.
3/9/2026
Predictive Coding Networks and Inference Learning: Tutorial and Survey
Tutorial and survey on predictive coding networks based on neuroscientific framework viewing brain as hierarchical Bayesian inference model minimizing prediction errors.
3/9/2026
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
PACE combines parameter-efficient fine-tuning with consistency regularization to improve generalization by reducing gradient norms during transformer adaptation to downstream tasks.
3/9/2026
L0-Regularized Quadratic Surface Support Vector Machines
L0-regularized kernel-free quadratic surface SVMs addressing overfitting and interpretability by reducing model parameters that scale quadratically with dimensionality.
3/9/2026
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching
FragFM hierarchical framework using fragment-level discrete flow matching and coarse-to-fine autoencoders for efficient scalable molecular graph generation.
3/9/2026
Aligning Compound AI Systems via System-level DPO
System-level DPO method for aligning compound AI systems with multiple interacting components including LLMs, foundation models, and external tools to human preferences.
3/9/2026
CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving
Context-Aware Priority Sampling using VQ-VAEs to improve data efficiency and handle imbalanced datasets in imitation learning for autonomous driving systems.
3/9/2026
From Tokenizer Bias to Backbone Capability: A Controlled Study of LLMs for Time Series Forecasting
Controlled study examining how LLM tokenizer bias and backbone capability affect time series forecasting performance using pre-trained language models as backbone.
3/9/2026
Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence
Federated Learning survey covering privacy-preserving distributed machine learning enabling multiple clients to collaboratively train models without centralizing sensitive data.
3/9/2026
FourierSpecNet: Neural Collision Operator Approximation Inspired by the Fourier Spectral Method for Solving the Boltzmann Equation
FourierSpecNet hybrid framework combining Fourier spectral methods with neural networks to approximate collision operators for solving the Boltzmann equation efficiently.
3/9/2026
Maximizing Asynchronicity in Event-based Neural Networks
Asynchronous-to-synchronous paradigm for event cameras using learned feature encoding to improve expressivity and generalizability for sparse sequential visual data.
3/9/2026
Temporal Misalignment Attacks against Multimodal Perception in Autonomous Driving
DejaVu attack exploiting temporal misalignment vulnerabilities in multimodal fusion systems for autonomous driving by manipulating camera and LiDAR synchronization.
3/9/2026
Merging Memory and Space: A State Space Neural Operator
State Space Neural Operator for learning solution operators of time-dependent PDEs using structured state space models with adaptive damping and learnable frequency modulation.
3/9/2026
TIC-GRPO: Provable and Efficient Optimization for Reinforcement Learning from Human Feedback
arXiv paper providing theoretical analysis of GRPO (Group Relative Policy Optimization) for LLM fine-tuning from human feedback.
3/9/2026
Characterizing Evolution in Expectation-Maximization Estimates for Overspecified Mixed Linear Regression
arXiv paper analyzing EM algorithm behavior under model misspecification in mixture models with excess components.
3/9/2026
A Geometric Perspective on the Difficulties of Learning GNN-based SAT Solvers
arXiv paper analyzing GNN-based SAT solvers through graph Ricci curvature geometric perspective to explain performance degradation.
3/9/2026
One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning
arXiv paper on efficient world models for heterogeneous multi-task planning, addressing gradient conflicts and plasticity loss.
3/9/2026
Performance Assessment Strategies for Language Model Applications in Healthcare
arXiv paper on assessing performance of language model applications in healthcare, addressing evaluation methodology.
3/9/2026
Reasoned Safety Alignment: Ensuring Jailbreak Defense via Answer-Then-Check
arXiv paper introducing Answer-Then-Check safety alignment method to defend LLMs against jailbreak attacks using reasoning.
3/9/2026
C^2Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learning
arXiv paper on prompt-based federated continual learning addressing class-wise and temporal forgetting across distributed clients.
3/9/2026
Auto-Regressive U-Net for Full-Field Prediction of Shrinkage-Induced Damage in Concrete
arXiv paper applying auto-regressive U-Net for predicting time-dependent damage in concrete. Domain-specific, limited AI/ML generality.
3/9/2026
Planner Aware Path Learning in Diffusion Language Models Training
arXiv paper on training diffusion language models with planner-aware path learning to optimize generation strategies.
3/9/2026
Diffusion Alignment as Variational Expectation-Maximization
arXiv paper formulating diffusion model alignment as variational EM to reduce reward over-optimization and mode collapse.
3/9/2026
Online Minimization of Polarization and Disagreement via Low-Rank Matrix Bandits
arXiv paper on opinion dynamics optimization using low-rank matrix bandits in online settings. Social dynamics focus, limited AI/ML relevance.
3/9/2026
Decoding Partial Differential Equations: Cross-Modal Adaptation of Decoder-only Models to PDEs
arXiv paper on adapting decoder-only LLMs to partial differential equations via cross-modal learning for scientific machine learning.
3/9/2026
KLASS: KL-Guided Fast Inference in Masked Diffusion Models
arXiv paper introducing KLASS sampling method for masked diffusion models using token-level KL divergence to accelerate inference.
3/9/2026
Diffusion Fine-Tuning via Reparameterized Policy Gradient of the Soft Q-Function
SQDF applies soft Q-function RL to fine-tune diffusion models with KL regularization, mitigating reward over-optimization.
3/9/2026
Whatever Remains Must Be True: Filtering Drives Reasoning in LLMs, Shaping Diversity
Analyzes diversity loss in RL-trained LLMs caused by mode-seeking reverse KL; proposes forward KL filtering for reasoning tasks.
3/9/2026
A-3PO: Accelerating Asynchronous LLM Training with Staleness-aware Proximal Policy Approximation
A-3PO accelerates asynchronous LLM RL training with staleness-aware proximal policy approximation, improving over decoupled PPO.
3/9/2026
Data-Driven Global Sensitivity Analysis for Engineering Design Based on Individual Conditional Expectations
Data-driven sensitivity analysis using Individual Conditional Expectations for interpreting black-box models in engineering design.
3/9/2026
Understanding and Improving Hyperbolic Deep Reinforcement Learning
Analysis of optimization challenges in hyperbolic deep RL identifying gradient factors affecting training success for hierarchical state embeddings.
3/9/2026
CARE What Fails: Contrastive Anchored-REflection for Verifiable Multimodal
CARE failure-centric post-training framework uses contrastive learning on wrong rollouts to improve multimodal reasoning with verifiable rewards.
3/9/2026
LLMTM: Benchmarking and Optimizing LLMs for Temporal Motif Analysis in Dynamic Graphs
LLMTM benchmarks LLMs on temporal motif analysis in dynamic graphs for anomaly detection and structural understanding.
3/9/2026
Beyond Mapping : Domain-Invariant Representations via Spectral Embedding of Optimal Transport Plans
Spectral embedding approach for domain-invariant representations via optimal transport plans, addressing distributional shift.
3/9/2026
Neural Signals Generate Clinical Notes in the Wild
CELM foundation model generates clinical notes from long-duration EEG recordings using multimodal learning.
3/9/2026
EDIS: Diagnosing LLM Reasoning via Entropy Dynamics
EDIS analyzes token-level entropy trajectories during LLM generation to diagnose reasoning quality beyond aggregate confidence statistics.