January 2026

Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural grounding to respect the immutable laws of the physical world. Consequently, while these agents implicitly function as world models, their simulations often suffer from physical hallucinations-generating plans that are logically sound but physically unexecutable. Existing alignment strategies predominantly rely on resource-intensive training or fine-tuning, which attempt to compress dynamic environmental rules into static model parameters. However, such parametric encapsulation […]

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So I am currently pursuing my undergrad and want to create an adaptive honeypot using RL(specifically DQN) and Cowrie Honeypot as my project. But I don’t have any idea on how to start or what to do and not do. I have beginner level knowledge of Q-Learning and Deep Q-Learning. Any help will be appreciated… submitted by /u/dn874 [link] [comments]

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Deep learning systems achieve remarkable empirical performance, yet the stability of the training process itself remains poorly understood. Training unfolds as a high-dimensional dynamical system in which small perturbations to optimization, data, parameters, or learning signals can induce abrupt and irreversible collapse, undermining reproducibility and scalability. We propose a unified dynamical perspective that characterizes training stability as an intrinsic property of learning systems, organized along four interacting dimensions: optimization, environmental/data, parametric, and learning-signal stability. We operationalize this perspective […]

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The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore, it is crucial to detect Active Regions (ARs) before they start forming on the solar surface. This will enable the development of early-warning capabilities for upcoming space weather disturbances. For this reason, we prepared the Solar Active Region Emergence Dataset (SolARED). The dataset is derived from full-disk maps of the Doppler velocity, magnetic field, and […]

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Knowledge Graphs~(KGs) often suffer from unreliable knowledge, which restricts their utility. Triple Classification~(TC) aims to determine the validity of triples from KGs. Recently, text-based methods learn entity and relation representations from natural language descriptions, significantly improving the generalization capabilities of TC models and setting new benchmarks in performance. However, there are still two critical challenges. First, existing methods often ignore the effective semantic interaction among different KG components. Second, most approaches adopt single binary classification training objective, leading […]

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We’re working on a system called Gennie that sits at an interesting intersection of reinforcement learning, human-in-the-loop systems, and noisy real-world environments. The core problem we’re exploring is this: In real-world settings, users issue short, ambiguous, and sometimes incorrect commands (often via voice) under time pressure. The system must decide when to act, when to request confirmation, and when to do nothing, balancing speed and accuracy. The reward signal isn’t immediate and is often delayed or implicit (task […]

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