3/2/2026
MoDora: Tree-Based Semi-Structured Document Analysis System
Document analysis system for semi-structured documents with tables, charts, and hierarchical content for question-answering tasks.
3/2/2026
Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding?
Analysis of why diffusion language models converge to autoregressive decoding instead of truly parallel generation despite theoretical advantages.
3/2/2026
Detoxifying LLMs via Representation Erasure-Based Preference Optimization
Defense method for LLMs against toxic outputs using representation erasure-based preference optimization, more robust than DPO/NPO approaches.
3/2/2026
U-CAN: Utility-Aware Contrastive Attenuation for Efficient Unlearning in Generative Recommendation
Machine unlearning technique for generative recommendation systems using LLMs to remove sensitive user attributes from model parameters.
3/2/2026
EvoX: Meta-Evolution for Automated Discovery
EvoX framework combines LLM-driven optimization with evolutionary search for automated discovery of programs, prompts, and algorithms.
3/2/2026
Human Supervision as an Information Bottleneck: A Unified Theory of Error Floors in Human-Guided Learning
Information-theoretic analysis of human supervision as bottleneck explaining persistent LLM errors from annotation noise and subjectivity.
3/2/2026
Uncertainty-aware Language Guidance for Concept Bottleneck Models
Framework using LLM guidance to annotate concepts for interpretable Concept Bottleneck Models with uncertainty awareness.
3/2/2026
FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments
FedDAG improves federated learning under data heterogeneity by combining data and gradient similarity for client clustering.
3/2/2026
Neural Operators Can Discover Functional Clusters
Theoretical work proving neural operators can discover functional clusters in infinite-dimensional spaces.
3/2/2026
Active Value Querying to Minimize Additive Error in Subadditive Set Function Learning
Active learning approach for querying values of subadditive set functions with applications to combinatorial optimization.
3/2/2026
Rudder: Steering Prefetching in Distributed GNN Training using LLM Agents
Rudder uses LLM agents to steer prefetching optimization in distributed GNN training for adaptive performance.
3/2/2026
Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing
Analysis of learning dynamics when multiple platforms compete for users, showing convergence to poor models under certain conditions.
3/2/2026
Flowette: Flow Matching with Graphette Priors for Graph Generation
Flowette uses flow matching with graph neural networks to generate graphs with recurring subgraph motifs.
3/2/2026
SDMixer: Sparse Dual-Mixer for Time Series Forecasting
SDMixer proposes sparse dual-stream architecture for multivariate time series forecasting using frequency and temporal analysis.
3/2/2026
Normalisation and Initialisation Strategies for Graph Neural Networks in Blockchain Anomaly Detection
Systematic ablation study of initialization and normalization strategies for GNNs in blockchain fraud detection.
3/2/2026
When Does Multimodal Learning Help in Healthcare? A Benchmark on EHR and Chest X-Ray Fusion
Benchmark evaluating multimodal fusion of EHR and chest X-rays for clinical decision support under missingness and fairness constraints.
3/2/2026
BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization
BTTackler diagnoses training problems in deep learning to guide efficient hyperparameter optimization beyond accuracy-based methods.
3/2/2026
On the Convergence of Single-Loop Stochastic Bilevel Optimization with Approximate Implicit Differentiation
Theoretical analysis of single-loop stochastic bilevel optimization convergence for meta-learning and hyperparameter optimization.
3/2/2026
FlexGuard: Continuous Risk Scoring for Strictness-Adaptive LLM Content Moderation
FlexGuard proposes continuous risk scoring for LLM content moderation that adapts to varying strictness levels across platforms and time.
3/2/2026
FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA
FedRot-LoRA addresses rotational misalignment in federated LoRA fine-tuning of LLMs, improving communication-efficient training on decentralized data.
3/2/2026
Selective Denoising Diffusion Model for Time Series Anomaly Detection
Diffusion-based method for time series anomaly detection using selective denoising instead of conditional reconstruction strategies.
3/2/2026
Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning
MoST contrastive learning method for disentangled mode-specific representations in multi-mode tensor time series.
3/2/2026
Optimizer-Induced Low-Dimensional Drift and Transverse Dynamics in Transformer Training
Geometric analysis of transformer training trajectories revealing low-dimensional drift direction and transverse oscillatory dynamics.
3/2/2026
Bridging Dynamics Gaps via Diffusion Schr\"odinger Bridge for Cross-Domain Reinforcement Learning
BDGxRL uses Diffusion Schrödinger Bridge to address dynamics gaps in cross-domain reinforcement learning without target reward supervision.
3/2/2026
OPTIAGENT: A Physics-Driven Agentic Framework for Automated Optical Design
OPTIAGENT uses LLM-based agentic framework with physics-driven optimization for automated optical design and lens system configuration.
3/2/2026
MAGE: Multi-scale Autoregressive Generation for Offline Reinforcement Learning
MAGE multi-scale autoregressive generation framework for offline RL addressing long-horizon tasks with sparse rewards via hierarchical decomposition.
3/2/2026
Provable Subspace Identification of Nonlinear Multi-view CCA
Provable identifiability framework for nonlinear multi-view canonical correlation analysis via subspace identification.
3/2/2026
UPath: Universal Planner Across Topological Heterogeneity For Grid-Based Pathfinding
Learning-based pathfinding using neural networks to approximate informed heuristics for grid-based search across different map topologies.
3/2/2026
MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models
MPU framework for privacy-preserving knowledge unlearning in LLMs without sharing server parameters or client forget sets.
3/2/2026
Actor-Critic Pretraining for Proximal Policy Optimization
Actor-critic pretraining approach for PPO that leverages expert data to reduce environment interactions required for RL training.
3/2/2026
Beyond State-Wise Mirror Descent: Offline Policy Optimization with Parameteric Policies
Theoretical investigation of offline reinforcement learning with general function approximation and parametric policies beyond state-wise methods.
3/2/2026
Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspective
Q-learning approach for learning safe policies from expert demonstrations with unknown constraints in constrained MDPs.
3/2/2026
FedNSAM:Consistency of Local and Global Flatness for Federated Learning
FedNSAM addresses sharpness-aware minimization in federated learning under high data heterogeneity, ensuring both local and global model flatness.
3/2/2026
LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding
LK Losses directly optimize acceptance rate in speculative decoding for LLM inference, improving upon KL divergence proxy objectives for draft model training.
3/2/2026
Hierarchical Concept-based Interpretable Models
Hierarchical Concept Embedding Models improve interpretability of deep neural networks by mapping inputs to human-interpretable concept representations with inter-concept relationships.
3/2/2026
Learning Generation Orders for Masked Discrete Diffusion Models via Variational Inference
Learns optimal generation orders for masked discrete diffusion models via variational inference to balance parallel generation and sample quality.
3/2/2026
Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments
Outlines vision for foundation world models as persistent compositional representations enabling agents to learn and adapt in open worlds.
3/2/2026
RewardUQ: A Unified Framework for Uncertainty-Aware Reward Models
Proposes RewardUQ framework for uncertainty-aware reward models in LLM alignment that reduces annotation costs and prevents overoptimization.
3/2/2026
pathsig: A GPU-Accelerated Library for Truncated and Projected Path Signatures
Introduces pathsig, a PyTorch-native GPU-accelerated library for computing path signatures as trainable features for sequential data.
3/2/2026
Adaptive Correlation-Weighted Intrinsic Rewards for Reinforcement Learning
Proposes ACWI framework that adaptively balances intrinsic and extrinsic rewards online for sparse reward reinforcement learning exploration.
3/2/2026
Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence
Surveys agentic AI systems with planning, tool use, and self-management capabilities applied to Open RAN network control and optimization.
3/2/2026
Learning with a Budget: Identifying the Best Arm with Resource Constraints
Studies best arm identification problem with heterogeneous resource costs and constraints across multiple resource types.
3/2/2026
What You Read is What You Classify: Highlighting Attributions to Text and Text-Like Inputs
Proposes explainable AI method for discrete token inputs like text using attribution highlighting to identify important tokens in transformers.
3/2/2026
Multi-Objective Reinforcement Learning for Large-Scale Tote Allocation in Human-Robot Collaborative Fulfillment Centers
Applies multi-objective reinforcement learning to optimize container consolidation in human-robot collaborative fulfillment centers.
3/2/2026
An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks
Proposes federated learning approach for anomaly detection in heterogeneous IoT networks while preserving privacy through distributed training.
3/2/2026
Adaptive Combinatorial Experimental Design: Pareto Optimality for Decision-Making and Inference
Investigates trade-off between regret minimization and statistical power in combinatorial multi-armed bandits using Pareto optimality framework.
3/2/2026
Time Series Foundation Models as Strong Baselines in Transportation Forecasting: A Large-Scale Benchmark Analysis
arXiv paper benchmarking general-purpose time-series foundation models for zero-shot transportation forecasting across multiple datasets.
3/2/2026
Histopathology Image Normalization via Latent Manifold Compaction
arXiv paper on Latent Manifold Compaction for unsupervised harmonization of histopathology images across different batch effects and scanners.
3/2/2026
Coverage-Aware Web Crawling for Domain-Specific Supplier Discovery via a Web--Knowledge--Web Pipeline
arXiv paper proposing Web-Knowledge-Web pipeline for discovering suppliers in specialized industries via iterative web crawling and knowledge base integration.
3/2/2026
Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations
Analyzes limitations of standard identifiability metrics (MCC, DCI, R²) on synthetic benchmarks, revealing implicit structural assumptions in representation learning evaluation.