#machine-learning News & Analysis

Evaluating the Reliability and Fidelity of Automated Judgment Systems of Large Language Models

From Five Dimensions to Many: Large Language Models as Precise and Interpretable Psychological Profilers

Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models

Beyond Linear Surrogates: High-Fidelity Local Explanations for Black-Box Models

A comprehensive study of LLM-based argument classification: from Llama through DeepSeek to GPT-5.2

PAI: Fast, Accurate, and Full Benchmark Performance Projection with AI

Spectral Convolution on Orbifolds for Geometric Deep Learning

Automated Explanation Selection for Scientific Discovery

Evolved Sample Weights for Bias Mitigation: Effectiveness Depends on the Fairness Objective

Best Crypto Portfolio for 2026: Messari Pivots to AI and Pepeto Gives Early Investors the Entry That Large Caps Cannot Offer

Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning

The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning

Data-Local Autonomous LLM-Guided Neural Architecture Search for Multiclass Multimodal Time-Series Classification

A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Education

Unifying Optimization and Dynamics to Parallelize Sequential Computation: A Guide to Parallel Newton Methods for Breaking Sequential Bottlenecks

AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research

Learning When to Trust in Contextual Bandits

Researchers propose CESA-LinUCB, a new approach to robust reinforcement learning that addresses 'Contextual Sycophancy' where evaluators are truthful in normal situations but biased in critical contexts. The method learns trust boundaries for each evaluator and achieves sublinear regret even when no evaluator is globally reliable.

LLM Routing as Reasoning: A MaxSAT View

Researchers propose a new constraint-based approach to LLM routing that formulates the problem as weighted MaxSAT/MaxSMT optimization, using natural language feedback to create constraints over model attributes. Testing on a 25-model benchmark shows this method can effectively route queries to appropriate LLMs based on user preferences expressed in natural language.

OMNIA: Closing the Loop by Leveraging LLMs for Knowledge Graph Completion

Researchers present OMNIA, a two-stage AI approach that combines structural and semantic reasoning to improve Knowledge Graph Completion using Large Language Models. The method clusters semantically related entities and validates them through embedding filtering and LLM-based validation, showing significant improvements in F1-scores compared to traditional models.

FedUAF: Uncertainty-Aware Fusion with Reliability-Guided Aggregation for Multimodal Federated Sentiment Analysis

Researchers propose FedUAF, a new multimodal federated learning framework that addresses challenges in sentiment analysis by using uncertainty-aware fusion and reliability-guided aggregation. The system demonstrates superior performance on benchmark datasets CMU-MOSI and CMU-MOSEI, showing improved robustness against missing modalities and unreliable client updates in federated learning environments.

A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning

Researchers developed FedCVR, a privacy-preserving federated learning framework for cardiovascular risk prediction that enables secure collaboration across medical institutions. The system achieved an F1-score of 0.84 and AUC of 0.96 while maintaining differential privacy, demonstrating that server-side adaptive optimization can preserve clinical utility under strict privacy constraints.

Locally Linear Continual Learning for Time Series based on VC-Theoretical Generalization Bounds

Researchers have developed SyMPLER, an explainable AI model for time series forecasting that uses dynamic piecewise-linear approximations to handle nonstationary environments. The model automatically determines when to add new local models based on prediction errors using Statistical Learning Theory, achieving comparable performance to black-box models while maintaining interpretability.

Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation

Researchers propose CAP-TTA, a test-time adaptation framework that helps debiased large language models better handle unfamiliar toxic prompts that cause distribution shifts. The method uses context-aware LoRA updates triggered by bias-risk thresholds to reduce toxic outputs while maintaining narrative fluency and reducing computational latency.

FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data

Researchers propose FedPBS, a new federated learning algorithm that addresses key challenges in distributed AI training including statistical heterogeneity and uneven client participation. The algorithm dynamically adapts batch sizes and applies proximal corrections to improve model convergence while preserving data privacy across distributed clients.

Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMs

Researchers propose an Iterative Semantic Reasoning Framework (ISRF) that uses large language models to improve recommendation systems by bridging explicit individual user interests with implicit group interests. The framework employs multi-step bidirectional reasoning and iterative optimization to achieve better user interest modeling than existing methods.