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This paper proposes 4MAS, a novel neural architecture inspired by biological bilaterality and memory consolidation, to address catastrophic forgetting in lifelong learning, achieving competitive results on benchmark datasets.

The paper empirically characterizes the learning geometry of hybrid quantum forecasting models, comparing them to classical baselines using Neural Tangent Kernel dynamics and other metrics, showing that similar generalization can emerge from different optimization trajectories.

This paper introduces the Disjoint-Bridge Operator-Substitution Certificate (DBOSC) to certify the interchangeability of modality compilers in physical language representation and evaluates ordered execution in controlled elastoplastic systems, separating distinct capabilities in attribute access, response substitution, fusion closure, and ordered execution.

The paper proposes DeltaMomentum, a key-value based momentum update rule that adapts forgetting rates based on input frequency, demonstrating faster convergence in neural network training across various scales.

This paper empirically studies retraining policies for streaming machine learning systems under concept drift, budget, and latency constraints.

The paper proposes LLM-Detector, a framework that uses large language models with in-context learning to perform tabular anomaly detection without fine-tuning, demonstrating consistent improvements over existing methods on multiple datasets.

The paper introduces EventTime, a multi-resolution framework using multiscale contrastive learning to estimate post-event financial losses from cybersecurity incidents, and presents the SECURE dataset for this purpose.

This paper introduces Disease Continuum Positioning (DCP), a longitudinal Bayesian learning framework that continuously estimates Alzheimer's disease severity from neuroimaging data, providing an uncertainty-aware score for disease progression prediction.

This paper conducts a systematic study on template-based acceleration for neural network robustness verification and introduces FastCert, a technique to automatically distribute templates for improved performance.

The paper proposes mechanistic tomography as a unified framework for designing measurements to recover internal mechanisms in AI models, improving control-oriented interpretability through interventions and calibration.

This paper presents a method to improve confidence estimates for black-box large language models by building classifiers that predict response correctness, outperforming existing zero-shot methods with minimal computational overhead.

This paper evaluates quantum-classical hybrid machine learning for lung cancer detection using cfDNA fragmentomics and methylation data, showing competitive performance of quantum kernel models against classical baselines.

Holtercare-Bench is a multimodal benchmark introduced to evaluate long-term dynamic ECG analysis using the Holtercare-23K dataset, revealing performance gaps in current MLLMs and providing improvements through fine-tuning for clinical applications.

This paper proposes the Triangular Fuzzy Rescaling Distance (d_TR), a metric that integrates normalization directly into distance calculations for comparing triangular fuzzy numbers, suitable for heterogeneous data applications.

The paper proposes a supervised machine learning model for estimating helicopter weight during takeoff, using data from Airbus's fleet, and details its implementation for on-board use with regulatory compliance.

Greater Manchester's NHS board refuses to adopt Palantir's federated data platform, citing superior local systems and public trust concerns, amid broader UK and European debates on Palantir's contracts.

The article lists early Labor Day tech deals on gadgets such as Apple AirTags, Dyson vacuums, and Sony headphones, featuring discounts on tested and recommended products.

The paper introduces ConceptGuard, a benchmark for evaluating context-sensitive unlearning in large language models using dual-use concepts, revealing that current unlearning techniques perform poorly under this practical evaluation framework.

This paper introduces G-CARL, a grounded checklist-aligned reinforcement learning framework for patient-oriented medical report interpretation, along with the MMedReport benchmark, demonstrating improved factuality and alignment with patient needs.

Introduces ContractScrub, a benchmark for evaluating LLMs on legal contract scrubbing tasks, revealing that current frontier models perform poorly on this domain-specific challenge.