2/25/2026
A Survey on Federated Fine-tuning of Large Language Models
Comprehensive survey of Federated Learning combined with LLM fine-tuning (FedLLM), covering privacy-preserving collaborative model adaptation methods.
2/25/2026
Towards Trustworthy GUI Agents: A Survey
Survey of trustworthy GUI agents built on LLMs, identifying execution gap challenges in real-world digital environment automation with irreversible actions.
2/25/2026
CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee
CONTINA provides confidence intervals for traffic demand prediction with coverage guarantees for traffic operations planning.
2/25/2026
Regularity and Stability Properties of Selective SSMs with Discontinuous Gating
Analysis of selective State-Space Models stability properties under discontinuous gating using passivity and Input-to-State Stability frameworks.
2/25/2026
RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
RefLoRA improves LoRA fine-tuning of large models by identifying optimal low-rank factorizations to address convergence and performance degradation issues.
2/25/2026
Performance Asymmetry in Model-Based Reinforcement Learning
Analysis of performance asymmetry in Model-Based RL agents on Atari100k, showing dramatic variance across task types despite high average performance.
2/25/2026
Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness
Framework for robust multivariate time series forecasting addressing channel dependencies, asynchronous sampling, and missing data simultaneously.
2/25/2026
Wasserstein Barycenter Soft Actor-Critic
Wasserstein Barycenter Soft Actor-Critic algorithm improves sample efficiency in off-policy reinforcement learning via directed exploration.
2/25/2026
Foundation Models for Causal Inference via Prior-Data Fitted Networks
CausalFM framework trains Prior-Data Fitted Networks as foundation models for causal inference via in-context learning on tabular data.
2/25/2026
FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
Frequency-domain occlusion method for interpreting time series neural networks, benchmarking frequency-based attribution approaches.
2/25/2026
Complexity-aware fine-tuning
Efficient fine-tuning method for LLMs using entropy-based complexity detection to apply chain-of-thought reasoning selectively on difficult examples.
2/25/2026
Transfer Learning in Infinite Width Feature Learning Networks
Theoretical analysis of transfer learning in infinitely wide neural networks under gradient flow, quantifying pretraining benefits.
2/25/2026
A hierarchy tree data structure for behavior-based user segment representation
Behavior-based User Segmentation proposes tree-based data structure for hierarchical user representation in recommendation systems.
2/25/2026
One-Step Flow Q-Learning: Addressing the Diffusion Policy Bottleneck in Offline Reinforcement Learning
One-Step Flow Q-Learning accelerates Diffusion Q-Learning for offline reinforcement learning by enabling single-step denoising without auxiliary modules.
2/25/2026
Uncertainty Propagation Networks for Neural Ordinary Differential Equations
Uncertainty Propagation Networks extend neural ODEs to model both state trajectories and uncertainty quantification in continuous-time systems.
2/25/2026
Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling
Method for heart rate prediction from heterogeneous health device data using unified representations for personalized monitoring.
2/25/2026
Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
MCTD-ME combines masked diffusion models with Monte Carlo Tree Search for protein design, addressing long-range dependencies and search space challenges.
2/25/2026
From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Probabilistic Scenarios paradigm for time series forecasting generates finite scenario sets instead of samples to address computational and coverage limitations.
2/25/2026
RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility
RHYTHM framework uses LLMs as spatio-temporal predictors with hierarchical temporal tokenization for human mobility prediction.
2/25/2026
Polychromic Objectives for Reinforcement Learning
Polychromic objectives framework for reinforcement learning fine-tuning preserves policy diversity during RLFT to prevent mode collapse.
2/25/2026
Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Recursive Self-Aggregation (RSA) test-time scaling method combines parallel and sequential inference to improve LLM reasoning capabilities.
2/25/2026
Cautious Weight Decay
Cautious Weight Decay (CWD) optimizer modification applies weight decay only to parameters aligned with optimizer updates.
2/25/2026
TeamFormer: Shallow Parallel Transformers with Progressive Approximation
TeamFormer proposes shallow parallel Transformer architecture with progressive approximation for efficient training and inference.
2/25/2026
Latent-Augmented Discrete Diffusion Models
Latent-Augmented Discrete Diffusion (LADD) improves discrete diffusion models for fast language generation by modeling cross-token dependencies.
2/25/2026
Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study
Comparative study of machine learning models (LASSO, random forest, XGBoost, neural network) for liver disease prediction.
2/25/2026
Towards Scalable Oversight via Partitioned Human Supervision
Framework for scalable AI oversight by partitioning complex multi-domain evaluation tasks among domain-specific human experts.
2/25/2026
ContextPilot: Fast Long-Context Inference via Context Reuse
ContextPilot accelerates long-context LLM inference by enabling context reuse via KV-cache optimization for RAG and agent memory layers.
2/25/2026
Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning
Interpretable machine learning applied to urinary metabolomics data for ADHD biomarker discovery.
2/25/2026
PhysE-Inv: A Physics-Encoded Inverse Modeling approach for Arctic Snow Depth Prediction
Physics-encoded inverse modeling framework for Arctic snow depth prediction combining sequential architecture with domain knowledge.
2/25/2026
SAGE-5GC: Security-Aware Guidelines for Evaluating Anomaly Detection in the 5G Core Network
Framework for evaluating anomaly detection in 5G networks accounting for non-IID data and adaptive attackers.
2/25/2026
LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordinal Data
LORE framework learns intrinsic dimensionality and ordinal embeddings from triplet comparisons for subjective perceptual spaces.
2/25/2026
Towards Robust Scaling Laws for Optimizers
Research on scaling laws for LLM pretraining with different optimizers beyond AdamW, examining new optimizers like Muon, Shampoo, and SOAP.
2/25/2026
Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs
Research on optimization algorithms for LLM reinforcement learning, comparing SGD vs Adam optimizers and their effectiveness in RL training phases.
2/25/2026
AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine Learning Engineering
AceGRPO method for autonomous ML engineering agents using adaptive curriculum and group relative policy optimization to overcome behavioral stagnation.
2/25/2026
VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training
VESPO algorithm for stable off-policy LLM training via importance sampling with variance reduction to prevent policy divergence and collapse.
2/25/2026
KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
Vector quantization compression for MoE LLMs using KLT-guided SVD and bias-corrected quantization for ultra-low-bit model deployment.
2/25/2026
MUSE: Multi-Tenant Model Serving With Seamless Model Updates
Multi-tenant ML serving system handling seamless model updates while maintaining decision thresholds across clients with distribution shifts.
2/25/2026
Pawsterior: Variational Flow Matching for Structured Simulation-Based Inference
Pawsterior framework for simulation-based inference using variational flow matching with structured domain constraints for bounded parameters.
2/25/2026
Silent Inconsistency in Data-Parallel Full Fine-Tuning: Diagnosing Worker-Level Optimization Misalignment
Analysis of worker-level optimization misalignment in data-parallel LLM fine-tuning despite parameter synchronization, termed silent inconsistency.
2/25/2026
Divine Benevolence is an $x^2$: GLUs scale asymptotically faster than MLPs
Numerical analysis showing GLU variants scale asymptotically faster than MLPs, explaining architectural dominance in frontier LLMs.
2/25/2026
\"UberWeb: Insights from Multilingual Curation for a 20-Trillion-Token Dataset
Study of multilingual data curation across 13 languages identifying interference patterns and optimal training strategies for 20-trillion-token dataset.
2/25/2026
GLM-5: from Vibe Coding to Agentic Engineering
GLM-5 foundation model transitioning from vibe coding to agentic engineering with DSA cost reduction and async RL infrastructure for improved autonomy.
2/25/2026
Anatomy of Capability Emergence: Scale-Invariant Representation Collapse and Top-Down Reorganization in Neural Networks
Study of representation collapse during neural network training across five model scales showing scale-invariant emergence patterns in 119 task combinations.
2/25/2026
AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models
AI-CARE metric for evaluating ML models on carbon emissions and energy consumption alongside standard performance metrics.
2/25/2026
Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
Research on interpretable Graph Neural Networks using symbolic methods to overcome message-passing limitations and Weisfeiler-Lehman expressivity barriers.
2/25/2026
Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints
Neuro-symbolic framework (NSGGM) for molecule and graph generation combining neural proposals with symbolic guarantees for controllable generation.
2/25/2026
Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders
MPZCH indexing mechanism for large-scale recommendation systems to mitigate embedding collisions and improve model freshness in embedding tables.
2/25/2026
MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM Reasoning
MASPO algorithm for LLM reasoning via reinforcement learning, addressing gradient utilization, probability mass, and signal reliability in trust region mechanisms.
2/25/2026
A Theoretical Framework for Modular Learning of Robust Generative Models
Theoretical framework for training modular LLMs by combining domain-specific experts without heuristic dataset weighting, matching monolithic model performance.
2/25/2026
Multi-Round Human-AI Collaboration with User-Specified Requirements
Framework for multi-round human-AI collaboration ensuring AI complements rather than undermines human decision-making via counterfactual harm and complementarity principles.