3/31/2026
Rainbow-DemoRL: Combining Improvements in Demonstration-Augmented Reinforcement Learning
Rainbow-DemoRL combines multiple demonstration-augmented reinforcement learning strategies to improve sample efficiency using offline data.
3/31/2026
CarbonEdge: Carbon-Aware Deep Learning Inference Framework for Sustainable Edge Computing
CarbonEdge framework for carbon-aware deep learning inference at network edge, extending model partitioning to optimize environmental impact alongside latency.
3/31/2026
RSR-core: A High-Performance Engine for Low-Bit Matrix-Vector Multiplication
High-performance engine for low-bit matrix-vector multiplication enabling efficient inference in neural networks, vector databases, and LLMs.
3/31/2026
PeopleSearchBench: A Multi-Dimensional Benchmark for Evaluating AI-Powered People Search Platforms
Open-source benchmark evaluating four AI-powered people search platforms across 119 queries for recruiting, sales, and expert search use cases.
3/31/2026
Hidden Ads: Behavior Triggered Semantic Backdoors for Advertisement Injection in Vision Language Models
Introduces Hidden Ads backdoor attack class exploiting Vision-Language Models' recommendation behavior to inject unauthorized advertisements through natural triggers.
3/31/2026
Secure Reinforcement Learning: On Model-Free Detection of Man in the Middle Attacks
Extends Bellman Deviation Detection framework for model-free RL to detect man-in-the-middle attacks in cyber-physical systems with refined MDP attack models.
3/31/2026
RTLSeek: Boosting the LLM-Based RTL Generation with Multi-Stage Diversity-Oriented Reinforcement Learning
RTLSeek uses multi-stage reinforcement learning to improve LLM-based RTL/Verilog generation with diverse hardware design implementations.
3/31/2026
Test-Time Instance-Specific Parameter Composition: A New Paradigm for Adaptive Generative Modeling
Composer paradigm for test-time instance-specific parameter composition enabling adaptive generative models.
3/31/2026
Energy Score-Guided Neural Gaussian Mixture Model for Predictive Uncertainty Quantification
Neural Gaussian mixture model using energy score guidance for predictive uncertainty quantification in machine learning.
3/31/2026
LVRPO: Language-Visual Alignment with GRPO for Multimodal Understanding and Generation
LVRPO framework for language-visual alignment in multimodal foundation models using GRPO for understanding and generation.
3/31/2026
KAT-Coder-V2 Technical Report
KAT-Coder-V2 agentic coding model using five expert domains with specialized fine-tuning and unified distillation for software engineering tasks.
3/31/2026
jaxsgp4: GPU-accelerated mega-constellation propagation with batch parallelism
GPU-accelerated JAX library for SGP4 orbital propagation of mega-constellations enabling efficient space situational awareness.
3/31/2026
ImagenWorld: Stress-Testing Image Generation Models with Explainable Human Evaluation on Open-ended Real-World Tasks
ImagenWorld benchmark with 3.6K condition sets for stress-testing image generation models across six core tasks with human evaluation.
3/31/2026
Deflation-PINNs: Learning Multiple Solutions for PDEs and Landau-de Gennes
Physics-informed neural networks framework (Deflation-PINNs) that identifies multiple distinct solutions to nonlinear PDEs.
3/31/2026
FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
Privacy-preserving federated learning framework using flow-matching generation to improve robustness and aggregation in distributed training.
3/31/2026
Kill-Chain Canaries: Stage-Level Tracking of Prompt Injection Across Attack Surfaces and Model Safety Tiers
Analysis of prompt injection attacks against LLM agents, tracking attack pipeline stages and defense mechanisms across five frontier models.
3/31/2026
Efficient Domain Adaptation for Text Line Recognition via Decoupled Language Models
Modular transformer approach for efficient domain adaptation in optical character recognition with reduced computational requirements.
3/31/2026
Beyond the Answer: Decoding the Behavior of LLMs as Scientific Reasoners
Research on how LLMs perform scientific reasoning tasks and how prompting affects their internal reasoning processes.
3/31/2026
A Deep Reinforcement Learning Framework for Closed-loop Guidance of Fish Schools via Virtual Agents
Deep reinforcement learning framework using PPO to train virtual agents for guiding fish school collective motion.
3/31/2026
A Multi-Agent Rhizomatic Pipeline for Non-Linear Literature Analysis
Multi-agent pipeline for literature analysis using Deleuzian ontology to identify non-linear patterns in research landscapes.
3/31/2026
Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization
Framework for evolutionary GPU kernel optimization using evaluation-driven agent and evolutionary techniques for operator generation.
3/31/2026
The Scaffold Effect: How Prompt Framing Drives Apparent Multimodal Gains in Clinical VLM Evaluation
Analysis of prompt framing artifacts in vision-language model evaluation on clinical neuroimaging tasks.
3/31/2026
From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches
Survey of network performance modeling approaches comparing traditional simulation with deep learning methods.
3/31/2026
$R_{dm}$: Re-conceptualizing Distribution Matching as a Reward for Diffusion Distillation
Diffusion distillation approach using reinforcement learning to improve student model performance beyond teacher anchoring.
3/31/2026
Towards a Medical AI Scientist
Medical AI Scientist system that autonomously generates hypotheses, conducts experiments, and writes manuscripts in clinical medicine.
3/31/2026
Constructing Composite Features for Interpretable Music-Tagging
Genetic programming pipeline for automatically evolving interpretable composite features for music tagging tasks.
3/31/2026
On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers
Method for improving diversity in text-to-image diffusion transformers through contextual space repulsion to address typicality bias.
3/31/2026
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets
Study on few-shot learning and RNNs applying asymptotic equipartition property from information theory to machine learning.
3/31/2026
Convergence of the Inexact Langevin Algorithm in KL Divergence with Application to Score-based Generative Models
Theoretical analysis of inexact Langevin algorithm convergence for score-based generative models with KL divergence guarantees.
3/31/2026
Continual Graph Learning: A Survey
Survey on continual graph learning covering incremental learning from streaming graph data with experience and generative replay approaches.
3/31/2026
Correcting Auto-Differentiation in Neural-ODE Training
Mathematical analysis of auto-differentiation reliability in neural-ODE training with high-order numerical methods.
3/31/2026
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
Novel prior learning method for neural networks using structured posteriors to improve generalization and uncertainty estimation.
3/31/2026
Unichain and Aperiodicity are Sufficient for Asymptotic Optimality of Average-Reward Restless Bandits
Proves asymptotic optimality of new restless bandit policies with O(1/√N) gap under unichain and aperiodicity conditions.
3/31/2026
Learning the Model While Learning Q: Finite-Time Sample Complexity of Online SyncMBQ
Theoretical analysis of sample complexity for model-based Q-learning, establishing finite-time convergence bounds for model-learning algorithms.
3/31/2026
Remedying uncertainty representations in visual inference through Explaining-Away Variational Autoencoders
Paper proposing Explaining-Away Variational Autoencoders to improve uncertainty representations in deep generative models for visual inference tasks.
3/31/2026
Recent Advances of Multimodal Continual Learning: A Comprehensive Survey
Survey of multimodal continual learning methods that enable models to learn from new data across multiple modalities while retaining previous knowledge without catastrophic forgetting.
3/31/2026
Transformers learn variable-order Markov chains in-context
Transformers learn variable-order Markov chains in-context with finite-sample accuracy analysis using context-tree weighting.
3/31/2026
Gradient Compression Beyond Low-Rank: Wavelet Subspaces Compact Optimizer States
Wavelet subspace compression for optimizer states reduces memory during LLM training, improving upon low-rank approaches.
3/31/2026
Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs
Steering vectors applied to LLM activations for bias mitigation across social dimensions like age, gender, and race.
3/31/2026
Large Language Models for Computer-Aided Design: A Survey
Survey of LLM integration with Computer-Aided Design tools, covering applications in 3D modeling and design workflows.
3/31/2026
Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning
Foundation models for time-series prediction often use simple parroting strategies rather than learning physics, revealing shared failure modes.
3/31/2026
Structured Agent Distillation for Large Language Model
Structured Agent Distillation compresses large LLM-based ReAct agents into smaller models while preserving reasoning and action consistency.
3/31/2026
CoDec: Prefix-Shared Decoding Kernel for LLMs
CoDec kernel optimizes LLM decoding by sharing prefix computation across multiple prompts to reduce memory-intensive KV cache access.
3/31/2026
MicroMix: Efficient Mixed-Precision Quantization with Microscaling Formats for Large Language Models
MicroMix: mixed-precision quantization method using microscaling formats for efficient LLM inference on NVIDIA Blackwell hardware.
3/31/2026
Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning
Novel fine-tuning mechanism for LLMs that addresses data quality/volume issues through controlled forgetting to improve domain adaptation.
3/31/2026
PENGUIN: Enhancing Transformer with Periodic-Nested Group Attention for Long-term Time Series Forecasting
PENGUIN: Transformer variant with periodic-nested group attention mechanism for improved long-term time series forecasting.
3/31/2026
Initialization Schemes for Kolmogorov-Arnold Networks: An Empirical Study
Empirical study of initialization schemes for Kolmogorov-Arnold Networks, proposing theory-driven approaches to improve training of spline-based KANs.
3/31/2026
No Need for Learning to Defer? A Training Free Deferral Framework to Multiple Experts through Conformal Prediction
Training-free framework for deferring predictions to multiple experts using conformal prediction without retraining.
3/31/2026
ReTrack: Data Unlearning in Diffusion Models through Redirecting the Denoising Trajectory
ReTrack enables data unlearning in diffusion models via importance sampling to remove memorized training data influence.
3/31/2026
Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning
Algorithms for distributed RL with policy gradients under asynchronous parallel computation and communication.