3/26/2026
HDPO: Hybrid Distillation Policy Optimization via Privileged Self-Distillation
Training method for LLMs on mathematical reasoning combining RL with privileged self-distillation to improve learning on hard problems.
3/26/2026
The Luna Bound Propagator for Formal Analysis of Neural Networks
C++ implementation of neural network verification tool supporting bound propagation methods for DNN formal analysis.
3/26/2026
Off-Policy Safe Reinforcement Learning with Constrained Optimistic Exploration
Safe reinforcement learning method addressing constraint violations in off-policy exploration through constrained optimistic exploration Q-learning.
3/26/2026
GRMLR: Knowledge-Enhanced Small-Data Learning for Deep-Sea Cold Seep Stage Inference
ML method for deep-sea microbial analysis with small datasets using knowledge enhancement techniques.
3/26/2026
Wireless communication empowers online scheduling of partially-observable transportation multi-robot systems in a smart factory
Online multi-robot task assignment and route scheduling in smart factories using wireless communication under partial observability.
3/26/2026
Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score
DIET: structured pruning method for LLMs using dimension-wise global importance scores that adapt to task-specific requirements.
3/26/2026
Can we generate portable representations for clinical time series data using LLMs?
Uses LLMs to generate portable patient embeddings from clinical time series that transfer across hospitals with minimal retraining.
3/26/2026
Understanding the Challenges in Iterative Generative Optimization with LLMs
Analysis of design challenges in iterative generative optimization using LLMs for self-improving agents; identifies hidden choices engineers must make.
3/26/2026
Stochastic Dimension-Free Zeroth-Order Estimator for High-Dimensional and High-Order PINNs
Dimension-free zeroth-order estimator for PINNs addressing spatial derivative complexity and memory overhead in high-dimensional PDEs.
3/26/2026
i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
Iterative unsupervised framework for feature selection and clustering in high-dimensional data by recovering influential features.
3/26/2026
Lagrangian Relaxation Score-based Generation for Mixed Integer linear Programming
Generative framework using Lagrangian relaxation-guided score-based generation to solve mixed-integer linear programming with diverse solutions.
3/26/2026
MoE-Sieve: Routing-Guided LoRA for Efficient MoE Fine-Tuning
MoE-Sieve: routing-guided LoRA fine-tuning framework for MoE models that adapts to skewed expert routing patterns for efficiency.
3/26/2026
The impact of sensor placement on graph-neural-network-based leakage detection
Investigates optimal sensor placement for GNN-based leakage detection in water distribution networks.
3/26/2026
Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization
Dual guidance approach for RL-based LLM training combining external verification and internal experience to improve reasoning task performance.
3/26/2026
KCLNet: Electrically Equivalence-Oriented Graph Representation Learning for Analog Circuits
Graph representation learning for analog circuit electrical equivalence to support electronic design automation tasks.
3/26/2026
Causality-Driven Disentangled Representation Learning in Multiplex Graphs
Causal inference framework for learning disentangled representations from multiplex graphs by separating shared and layer-specific information.
3/26/2026
The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimation
RLHF-aligned LLMs exhibit response homogenization limiting uncertainty estimation; analyzes alignment tax impact across different tasks and sampling methods.
3/26/2026
On Gossip Algorithms for Machine Learning with Pairwise Objectives
Gossip-based distributed machine learning algorithms for IoT networks with privacy constraints and limited computation/communication resources.
3/26/2026
Reservoir-Based Graph Convolutional Networks
Graph convolutional networks using reservoir computing to address challenges with complex and dynamic graph data and long-range dependencies.
3/26/2026
Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models
Bayesian optimization framework for tuning control policies using human preferences and pairwise comparisons instead of quantitative evaluations.
3/26/2026
Linear-Nonlinear Fusion Neural Operator for Partial Differential Equations
Neural operator learning method combining linear and nonlinear effects for efficient PDE solving without repeated solution computation.
3/26/2026
TsetlinWiSARD: On-Chip Training of Weightless Neural Networks using Tsetlin Automata on FPGAs
FPGA-based implementation of weightless neural networks using Tsetlin automata for on-chip training and inference with low latency and complexity.
3/26/2026
A Deep Dive into Scaling RL for Code Generation with Synthetic Data and Curricula
Scalable RL pipeline for improving LLM code generation through synthetic data and curriculum learning, addressing data diversity challenges at scale.
3/26/2026
IPatch: A Multi-Resolution Transformer Architecture for Robust Time-Series Forecasting
Transformer architecture for multivariate time series forecasting using multi-resolution representations to capture short-term and long-range dependencies.
3/26/2026
Uncovering Memorization in Timeseries Imputation models: LBRM Membership Inference and its link to attribute Leakage
Study on privacy vulnerabilities in deep learning time series imputation models, demonstrating membership inference attacks in black-box settings.
3/26/2026
Identification of NMF by choosing maximum-volume basis vectors
Nonnegative matrix factorization approach using maximum-volume basis vectors for identifying NMF solutions in highly mixed data.
3/26/2026
Attack Assessment and Augmented Identity Recognition for Human Skeleton Data
Methods for assessing adversarial attack vulnerability and augmenting identity recognition models trained on small LiDAR skeleton datasets.
3/26/2026
Embracing Heteroscedasticity for Probabilistic Time Series Forecasting
Framework for probabilistic time series forecasting that explicitly models heteroscedasticity and time-varying conditional variances in nonstationary dynamics.
3/26/2026
Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting
ReGuider representation-level supervision method improves time series forecasting by capturing extreme patterns and salient dynamics in temporal representations.
3/26/2026
DeepDTF: Dual-Branch Transformer Fusion for Multi-Omics Anticancer Drug Response Prediction
DeepDTF dual-branch transformer framework predicts cancer drug response from multi-omics data, addressing cross-modal alignment in precision oncology.
3/26/2026
Language-Assisted Image Clustering Guided by Discriminative Relational Signals and Adaptive Semantic Centers
Vision-language model approach for image clustering using LLM-generated text features with adaptive semantic centers to improve inter-class discriminability.
3/26/2026
Cost-Sensitive Neighborhood Aggregation for Heterophilous Graphs: When Does Per-Edge Routing Help?
Cost-Sensitive Neighborhood Aggregation (CSNA) GNN layer uses per-edge routing to handle heterophilous graph structures differently based on adversarial vs informative regimes.
3/26/2026
Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning
Framework uses LLMs to automatically design reward functions for cooperative multi-agent reinforcement learning, synthesizing executable reward programs from environment instrumentation.
3/26/2026
CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control
Multi-agent reinforcement learning approach for decentralized adaptive traffic signal control using learned coordination in partially observable environments.
3/26/2026
MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization
MolEvolve framework uses LLM guidance with evolutionary search for interpretable molecular optimization, addressing activity cliffs and lack of interpretability.
3/26/2026
Learning Response-Statistic Shifts and Parametric Roll Episodes from Wave--Vessel Time Series via LSTM Functional Models
LSTM functional models learn nonlinear mappings from wave-vessel time series to predict parametric roll episodes and statistical shifts in ship responses.
3/26/2026
CUA-Suite: Massive Human-annotated Video Demonstrations for Computer-Use Agents
CUA-Suite dataset provides massive human-annotated continuous video demonstrations for training computer-use agents on desktop automation tasks, addressing data bottleneck.
3/26/2026
Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variability
Transfer learning framework using LSTM and conformal prediction for lithium-ion battery state-of-health forecasting across manufacturing variations.
3/26/2026
Uniform Laws of Large Numbers in Product Spaces
Theoretical work on uniform laws of large numbers in product spaces extending VC dimension theory under product distribution assumptions.
3/26/2026
Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling
Sequential-AMPC uses recurrent neural networks to approximate nonlinear model predictive control offline, reducing online computation for embedded hardware control systems.
3/26/2026
Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs
AI agents using Claude Code autonomously discovered novel adversarial attack algorithms for LLMs that outperform 30+ existing methods in jailbreaking and prompt injection attacks.
3/26/2026
AVO: Agentic Variation Operators for Autonomous Evolutionary Search
Agentic Variation Operators replace fixed mutation/crossover in evolutionary search with autonomous coding agents consulting lineage and domain knowledge.
3/26/2026
TuneShift-KD: Knowledge Distillation and Transfer for Fine-tuned Models
TuneShift-KD enables knowledge distillation and transfer of fine-tuned specialized knowledge to newer LLM architectures without access to original training data.
3/26/2026
No Single Metric Tells the Whole Story: A Multi-Dimensional Evaluation Framework for Uncertainty Attributions
Multi-dimensional evaluation framework for uncertainty attribution methods in explainable AI addressing inconsistent evaluation across heterogeneous proxy tasks.
3/26/2026
UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experience
UI-Voyager is a self-evolving mobile GUI agent using rejection fine-tuning and credit assignment to learn from failed trajectories in long-horizon tasks.
3/26/2026
Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction
RAVEN applies generative pretraining to structured electronic health records using recurrence-aware next-visit event prediction on 1M+ patient dataset.
3/26/2026
DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving
DreamerAD enables efficient RL for autonomous driving via latent world model achieving 80x speedup by compressing diffusion sampling from 100 to 1 step.
3/26/2026
Polynomial Speedup in Diffusion Models with the Multilevel Euler-Maruyama Method
Multilevel Euler-Maruyama method accelerates diffusion model solving via multi-level approximators with polynomial speedup in HTMC regime.
3/26/2026
KARMA: Knowledge-Action Regularized Multimodal Alignment for Personalized Search at Taobao
KARMA applies LLMs to personalized search at Taobao by addressing knowledge-action gap through regularized multimodal alignment for next-item prediction.
3/26/2026
Beyond Masks: Efficient, Flexible Diffusion Language Models via Deletion-Insertion Processes
Deletion-Insertion Diffusion language models replace masking paradigm with discrete diffusion processes for improved computational efficiency and generation flexibility.