3/10/2026
NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning algorithm for general-sum games with heterogeneous agent objectives and convergence guarantees.
3/10/2026
Diffusion Controller: Framework, Algorithms and Parameterization
Control-theoretic framework for controllable diffusion generation using linearly-solvable MDPs and reweighting pretrained models.
3/10/2026
RESCHED: Rethinking Flexible Job Shop Scheduling from a Transformer-based Architecture with Simplified States
Transformer-based approach to flexible job shop scheduling using simplified states instead of handcrafted features.
3/10/2026
Resource-Adaptive Federated Text Generation with Differential Privacy
Federated learning method for generating differentially private synthetic text datasets from LLMs for downstream task reuse.
3/10/2026
Interpretable Maximum Margin Deep Anomaly Detection
Deep SVDD improvement for anomaly detection addressing hypersphere collapse and interpretability via maximum margin approach.
3/10/2026
Entropy-Aware On-Policy Distillation of Language Models
On-policy distillation method using entropy-aware objectives for improved knowledge transfer between language models.
3/10/2026
Dreamer-CDP: Improving Reconstruction-free World Models Via Continuous Deterministic Representation Prediction
Dreamer-CDP improves world models with continuous deterministic representation prediction without reconstruction.
3/10/2026
Countdown-Code: A Testbed for Studying The Emergence and Generalization of Reward Hacking in RLVR
Benchmark environment for studying reward hacking in RL agents through dual-access mathematical reasoning tasks.
3/10/2026
Combining Adam and its Inverse Counterpart to Enhance Generalization of Deep Learning Optimizers
InvAdam optimizer variant that improves generalization by finding flatter minima than standard Adam.
3/10/2026
Agentic Planning with Reasoning for Image Styling via Offline RL
AI agent framework using offline RL for structured planning and reasoning in image editing tasks.
3/10/2026
Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts
LLM-based system for automated CUDA kernel optimization across ML and scientific computing domains.
3/10/2026
Shaping Parameter Contribution Patterns for Out-of-Distribution Detection
Method to improve OOD detection by diversifying parameter contribution patterns in classifiers.
3/10/2026
wDPO: Winsorized Direct Preference Optimization for Robust LLM Alignment
wDPO improves DPO for LLM alignment by using winsorization to handle noisy preference data robustly.
3/10/2026
Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training
Empirical study on knowledge distillation and difficulty-aware training for improving LLM performance in finance domain.
3/10/2026
Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation
PT-RAG uses retrieval-augmented generation to predict cellular responses to gene perturbations with improved generalization.
3/10/2026
Rethinking Deep Research from the Perspective of Web Content Distribution Matching
WeDas framework improves web search agents by matching queries to web content distribution structures for better evidence retrieval.
3/10/2026
Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series
Symbolic machine learning method to convert chaotic time series into interpretable algebraic equations for forecasting.
3/10/2026
AutoResearch-RL: Perpetual Self-Evaluating Reinforcement Learning Agents for Autonomous Neural Architecture Discovery
AutoResearch-RL is an RL agent that autonomously conducts perpetual neural architecture and hyperparameter search via code modification without human supervision.
3/10/2026
Adversarial Latent-State Training for Robust Policies in Partially Observable Domains
Adversarial latent-state training framework for robust policies in partially observable MDPs under latent distribution shift with theoretical guarantees.
3/10/2026
ShakyPrepend: A Multi-Group Learner with Improved Sample Complexity
ShakyPrepend applies differential privacy-inspired tools to multi-group learning for improved sample complexity and adaptation to group structure.
3/10/2026
Norm-Hierarchy Transitions in Representation Learning: When and Why Neural Networks Abandon Shortcuts
Analyzes norm-hierarchy transitions explaining when neural networks transition from spurious shortcuts to structured representations during training.
3/10/2026
Learning Concept Bottleneck Models from Mechanistic Explanations
Learning concept bottleneck models from mechanistic explanations instead of pre-specified or LLM-prompted concepts for improved interpretability and predictive power.
3/10/2026
Learning Clinical Representations Under Systematic Distribution Shift
Addresses representation entanglement between physiologic signal and institutional artifacts in clinical ML under systematic distribution shift from heterogeneous practices.
3/10/2026
Latent Generative Models with Tunable Complexity for Compressed Sensing and other Inverse Problems
Develops tunable-complexity priors for diffusion models and normalizing flows to balance representation error and overfitting in inverse problem solving.
3/10/2026
N-Tree Diffusion for Long-Horizon Wildfire Risk Forecasting
N-Tree Diffusion enables efficient long-horizon wildfire risk forecasting by hierarchically extending diffusion models across multiple prediction steps.
3/10/2026
Scaling Laws in the Tiny Regime: How Small Models Change Their Mistakes
Examines neural scaling laws in sub-20M parameter regime for TinyML/edge AI, showing both ConvNets and MobileNetV2 follow power law error scaling.
3/10/2026
Learning to Reflect: Hierarchical Multi-Agent Reinforcement Learning for CSI-Free mmWave Beam-Focusing
Hierarchical multi-agent RL framework for controlling reconfigurable intelligent surfaces in mmWave systems without channel state information estimation overhead.
3/10/2026
Feed m Birds with One Scone: Accelerating Multi-task Gradient Balancing via Bi-level Optimization
Accelerates multi-task learning gradient balancing through bi-level optimization to improve MGDA-type methods for handling task conflicts.
3/10/2026
Deterministic Fuzzy Triage for Legal Compliance Classification and Evidence Retrieval
Deterministic fuzzy triage system for legal compliance classification using dual encoders and transparent bands, demonstrated on contractual evidence HIPAA/NERC-CIP alignment.
3/10/2026
DualSpec: Accelerating Deep Research Agents via Dual-Process Action Speculation
DualSpec accelerates LLM-based research agents by speculating on actions during reasoning to reduce latency in long-horizon information-seeking tasks with tool use.
3/10/2026
Data Agent: Learning to Select Data via End-to-End Dynamic Optimization
Data Agent uses end-to-end optimization to dynamically select informative samples during training acceleration.
3/10/2026
Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part II
Cost-driven state representation learning for control tasks from high-dimensional partial observations.
3/10/2026
Discrete Tokenization Unlocks Transformers for Calibrated Tabular Forecasting
Tokenization approach enables transformers to outperform gradient boosting on tabular forecasting tasks.
3/10/2026
A Unified Framework for Knowledge Transfer in Bidirectional Model Scaling
Unified framework for knowledge transfer between models of different sizes, enabling bidirectional scaling.
3/10/2026
Online Continual Learning for Anomaly Detection in IoT under Data Distribution Shifts
OCLADS framework for continual learning in IoT anomaly detection under non-stationary data distributions.
3/10/2026
A Unified View of Drifting and Score-Based Models
Theoretical analysis connecting drifting models and score-based generative models through kernel-weighted discrepancy.
3/10/2026
One-for-All Model Initialization with Frequency-Domain Knowledge
Method for transferring knowledge from pre-trained models to different architectural scales using frequency-domain information.
3/10/2026
Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction
Neural dynamics-informed pre-training framework for personalized brain functional network construction addressing heterogeneous neural activity patterns.
3/10/2026
Generative prediction of laser-induced rocket ignition with dynamic latent space representations
Data-driven approach using dynamic latent space representations for generative prediction of laser-induced rocket ignition with uncertainty quantification.
3/10/2026
Obliviator Reveals the Cost of Nonlinear Guardedness in Concept Erasure
Obliviator method revealing vulnerability of concept erasure to nonlinear adversaries, analyzing statistical dependencies in representation unlearning.
3/10/2026
ECG Classification on PTB-XL: A Data-Centric Approach with Simplified CNN-VAE
ECG classification on PTB-XL dataset using simplified CNN-VAE with data-centric approach for cardiovascular disease detection.
3/10/2026
Constraints Matrix Diffusion based Generative Neural Solver for Vehicle Routing Problems
Constraints Matrix Diffusion-based generative neural solver for vehicle routing problems emphasizing local optimization and small-scale generalization.
3/10/2026
TS-MLLM: A Multi-Modal Large Language Model-based Framework for Industrial Time-Series Big Data Analysis
TS-MLLM: multi-modal LLM framework for industrial time-series analysis combining temporal signals, frequency-domain visuals, and textual knowledge for prognostics.
3/10/2026
TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
TT-Sparse: neural building block for learning interpretable sparse rule models using differentiable truth tables balancing performance and human-understandable complexity.
3/10/2026
Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models
Visual representation framework encoding signals as low-rank adaptations to frozen diffusion foundation models for compact storage and reuse.
3/10/2026
Helix: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving
Helix: evolutionary reinforcement learning system combining LLMs with RL for open-ended scientific problem solving with improved exploration and generalization.
3/10/2026
Partial Differential Equations in the Age of Machine Learning: A Critical Synthesis of Classical, Machine Learning, and Hybrid Methods
Critical review synthesizing classical numerical methods and machine learning approaches for solving PDEs, examining six fundamental computational challenges.
3/10/2026
Beyond Surrogates: A Quantitative Analysis for Inter-Metric Relationships
Theoretical analysis of relationships between surrogate losses and evaluation metrics, addressing metric mismatch between offline validation and online performance.
3/10/2026
Global Convergence of Average Reward Constrained MDPs with Neural Critic and General Policy Parameterization
Primal-dual natural actor-critic algorithm for constrained MDPs with neural network critics and general policy parameterization, enabling high-dimensional continuous control.
3/10/2026
Step-Size Decay and Structural Stagnation in Greedy Sparse Learning
Theoretical analysis of greedy sparse learning algorithms examining convergence failure with step-size decay in matching pursuit and boosting methods.