February 2026

Decentralized federated learning (DFL) enables edge devices to collaboratively train models through local training and fully decentralized device-to-device (D2D) model exchanges. However, these energy-intensive operations often rapidly deplete limited device batteries, reducing their operational lifetime and degrading the learning performance. To address this limitation, we apply energy harvesting technique to DFL systems, allowing edge devices to extract ambient energy and operate sustainably. We first derive the convergence bound for wireless DFL with energy harvesting, showing that the convergence […]

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Most time series foundation models are pretrained by directly predicting future observations, which often yields weakly structured latent representations that capture surface noise rather than coherent and predictable temporal dynamics. In this work, we introduce EIDOS, a foundation model family that shifts pretraining from future value prediction to latent-space predictive learning. We train a causal Transformer to predict the evolution of latent representations, encouraging the emergence of structured and temporally coherent latent states. To ensure stable targets for […]

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How Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt This piece by Margaret-Anne Storey is the best explanation of the term cognitive debt I’ve seen so far. Cognitive debt, a term gaining traction recently, instead communicates the notion that the debt compounded from going fast lives in the brains of the developers and affects their lived experiences and abilities to “go fast” or to make changes. Even if AI agents produce code that could […]

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Providing extensive context via prompting is vital for leveraging the capabilities of Large Language Models (LLMs). However, lengthy contexts significantly increase inference latency, as the computational cost of self-attention grows quadratically with sequence length. To mitigate this issue, context compression-particularly soft prompt compressio-has emerged as a widely studied solution, which converts long contexts into shorter memory embeddings via a trained compressor. Existing methods typically compress the entire context indiscriminately into a set of memory tokens, requiring the compressor […]

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In recommendation systems, representing user-item interactions as a bipartite network is a fundamental approach that provides a structured way to model relationships between users and items, allowing for efficient predictions via network science. Collaborative filtering is one of the most widely used and actively researched techniques for recommendation systems, its rationale is to predict user preferences based on shared patterns in user interactions, and vice versa. Memory-based collaborative filtering relies on directly analyzing user-item interactions to provide recommendations […]

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Background/Objectives: The rapid advancement of artificial intelligence (AI) has had a notable impact in the healthcare field, particularly in the realm of assessment and diagnosis. One specific area where the integration of AI technologies shows promise is the evaluation of progressive neurological disorders (PNDs). PNDs are characterized by a progressive decline in neurological function, resulting in changes in cognition, movement, and communication. PNDs pose significant challenges in terms of early detection and categorization. Speech and voice changes are […]

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