February 2026

Catastrophic forgetting, the tendency of neural networks to forget previously learned knowledge when learning new tasks, has been a major challenge in continual learning (CL). To tackle this challenge, CL methods have been proposed and shown to reduce forgetting. Furthermore, CL models deployed in mission-critical settings can benefit from uncertainty awareness by calibrating their predictions to reliably assess their confidences. However, existing uncertainty-aware continual learning methods suffer from high computational overhead and incompatibility with mainstream replay methods. To […]

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Details on OpenAI’s contract with the Department of War, outlining safety red lines, legal protections, and how AI systems will be deployed in classified environments.

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Standard accuracy benchmarks evaluate whether a language model produces correct outputs but not whether it produces them consistently. We demonstrate that accuracy and output predictability are independent dimensions (Pearson r = -0.24, p = 0.56, N = 8 medical LLMs) when evaluated at a critical clinical summarization position. This independence yields a four-class behavioral taxonomy: IDEAL (convergent and accurate), EMPTY (convergent but inaccurate), DIVERGENT (high variance with incomplete outputs), and RICH (moderate variance with high accuracy).The DIVERGENT class […]

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Photo by Resource Database on Unsplash Authors: Shon Mohsin, D. Eng AIJeremiah Lowhorn, D. Eng AISeth ThorMatthew Morais, D. Eng AI What if you could ask your data: “What did we know about this research topic in June 2024?” — and get a precise, structured answer? Scientific Research generates a web of interconnected entities — subjects, methods, outcomes, organizations, publications — that evolves over time. A spreadsheet can track individual facts. A relational database can join tables. But neither preserves the shape of knowledge: the web […]

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Detecting out-of-distribution (OOD) graphs is crucial for ensuring the safety and reliability of Graph Neural Networks. In unsupervised graph-level OOD detection, models are typically trained using only in-distribution (ID) data, resulting in incomplete feature space characterization and weak decision boundaries. Although synthesizing outliers offers a promising solution, existing approaches rely on fixed, non-adaptive sampling heuristics (e.g., distance- or density-based), limiting their ability to explore informative OOD regions. We propose a Policy-Guided Outlier Synthesis (PGOS) framework that replaces static […]

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Efficient representation learning is essential for optimal information storage and classification. However, it is frequently overlooked in artificial neural networks (ANNs). This neglect results in networks that can become overparameterized by factors of up to 13, increasing redundancy and energy consumption. As the demand for large language models (LLMs) and their scale increase, these issues are further highlighted, raising significant ethical and environmental concerns. We analyze our previously developed biologically inspired learning rule using information-theoretic concepts, evaluating its […]

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I am curious to review my manuscript with LLMs as sometimes my paper contains small mistakes which creates a impression that author is not good. Are there any prompt? especially for like CVPR,ECCV, ICLR papers submitted by /u/Alternative_Art2984 [link] [comments]

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Federated Learning (FL) is a popular distributed learning paradigm to break down data silo. Traditional FL approaches largely rely on gradient-based updates, facing significant issues about heterogeneity, scalability, convergence, and overhead, etc. Recently, some analytic-learning-based work has attempted to handle these issues by eliminating gradient-based updates via analytical (i.e., closed-form) solutions. Despite achieving superior invariance to data heterogeneity, these approaches are fundamentally limited by their single-layer linear model with a frozen pre-trained backbone. As a result, they can […]

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Cyber Ranges (CRs) are complex socio-technical ecosystems, combining infrastructure resources, software services, learning mechanisms, and human-in-the-loop processes for cybersecurity training, education, and experimentation. However, their design and representation are conventionally described by diverse architectural representations and a lack of standardization, making them difficult to compare, integrate, and reason in an automated manner. This paper proposes a novel framework that uniquely integrates the structural, functional, informational, and decisional aspects of CR platforms, formalizing them into a common semantic framework. […]

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