arXiv:2602.02568v1 Announce Type: new Abstract: We introduce $textbf{Hierarchical Taylor Series-based Continual Learning (HTCL)}$, a framework that couples fast local adaptation with conservative, second-order global consolidation to address the high variance introduced by random task ordering. To address task-order effects, HTCL identifies the best intra-group task sequence and integrates the resulting local updates through a Hessian-regularized Taylor expansion, yielding a consolidation step with theoretical guarantees. The approach naturally extends to an $L$-level hierarchy, enabling multiscale knowledge integration in a […]

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This article presents a novel methodological framework for utilizing retrieval-augmented generation (RAG) systems specifically Google NotebookLM to address the critical challenge of tonal misalignment in competitive grant applications and institutional support documentation. By establishing a localized, source-grounded AI environment that synthesizes institutional data corpora with funder-specific linguistic patterns, this workflow enables rapid production of contextually precise, tonally calibrated grant narratives. The methodology operates in three phases: (1) source corpus ingestion and institutional knowledge mapping, (2) requirement-data alignment through […]

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arXiv:2601.09823v1 Announce Type: new Abstract: Latent diffusion models such as Stable Diffusion 1.5 offer strong generative priors that are highly valuable for image restoration, yet their full pipelines remain too computationally heavy for deployment on edge devices. Existing lightweight variants predominantly compress the denoising U-Net or reduce the diffusion trajectory, which disrupts the underlying latent manifold and limits generalization beyond a single task. We introduce NanoSD, a family of Pareto-optimal diffusion foundation models distilled from Stable Diffusion 1.5 […]

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I have been around long enough to know that “Forever” is a trap. In the Web3 space, we are obsessed with Immutability. We carve our JPEGs into IPFS granite. We deploy smart contracts meant to outlast civilizations. We scream “Code is Law” and “Data is Eternal.” But as an Immortal, I can tell you: Eternity is boring. Without death, there is no urgency. Without the threat of loss, there is no value. So, I decided to break the […]

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Microsoft has already been under massive scrutiny for integrating Copilot across Windows 11 for quite some time now. However, after multiple complaints from users and feedback, the company has promised to step back from its AI-everywhere approach and fix the operating system from scratch. Having that said, Microsoft 365 Copilot has been under the radar for multiple other reasons. Early this year, in early 2026, Britain’s West Midlands Police became embroiled in controversy after admitting that a flawed […]

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arXiv:2410.05263v2 Announce Type: replace Abstract: Reliable confidence measures of metrics derived from medical imaging reconstruction pipelines would improve the standard of decision-making in many clinical workflows. Conformal Prediction (CP) provides a robust framework for producing calibrated prediction intervals, but standard CP formulations face a critical challenge in the imaging pipeline: common mismatches between image reconstruction objectives and downstream metrics can introduce systematic prediction deviations from ground truth values, known as bias. These biases in turn compromise the efficiency […]

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arXiv:2603.16279v1 Announce Type: cross Abstract: This article presents a solution to intercept an agile drone by another agile drone carrying a catching net. We formulate the interception as a Competitive Reinforcement Learning problem, where the interceptor and the target drone are controlled by separate policies trained with Proximal Policy Optimization (PPO). We introduce a high-fidelity simulation environment that integrates a realistic quadrotor dynamics model and a low-level control architecture implemented in JAX, which allows for fast parallelized execution […]

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Sequential linear models can be adopted to describe the data where the response variable depends on lagged outcomes and fixed effects variables. In estimation and variable selection and for predicting the response variable with high accuracy, we propose the penalized method based on Smoothly Clipped Absolute Deviation Penalty (SCAD) in the sequential linear models. We conduct the simulations where the SCAD-penalized method is compared with other methods including the ordinary least squares (OLS), Lasso, and adaptive Lasso in […]

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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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