AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,686 papers
This work studies causal retention: whether a frozen learned state answers a mechanism-probe map fixed independently of training, including action, context, direct target, value, and delay, which is distinct from task sufficiency and source-domain decodability.
These results establish persona-set geometry as a task-agnostic mechanism for eliciting divergent LLM outputs, and support persona diversification as a reusable complement to prompt optimization.
A Track-and-Stop style algorithm for best-arm identification with prescribed confidence is proposed, and it is proved that the algorithm almost surely achieves the asymptotically optimal cost as the error tends to zero.
The results suggest that the distribution of additional reasoning paths generated by the algorithm is heavy-tailed and that substantial capacity to overcharge remains even under the best possible audit designed to keep the false-positive rate below $\alpha = 0.1$.
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Theoretical analysis and numerical experiments demonstrate that the model achieves exact inference with $\mathcal{O}(n \log^2 n)$ training cost and $\mathcal{O}(\log^d n)$ prediction cost, establishing a principled framework for scalable and high-fidelity Gaussian process regression.
DIAL is introduced, a unified framework that combines abundant LLM comparisons with limited human comparisons to separate judge-specific position effects, learn shared structure in position-debiased LLM preferences, and adaptively calibrate that structure toward the human preference target.
Atelier is a self-supervised framework that amortizes INR fitting for reconstructed cryoEM maps, a transformer-based hypernetwork that generates high-fidelity reconstructions across a wide range of protein structures, including large multi-subunit assemblies.
The raspberry-sesame dual herbal in-situ gel demonstrated favourable physicochemical properties and appreciable in-vitro anti-inflammatory activity, suggesting its potential as a safe topical herbal alternative.
AI-workflow governance cannot be reduced to checking the final answer: an apparently safe answer may rest on a fragile evidence path that ordinary evaluation cannot see, localize or govern, so SSEG moves governance below surface-level output checking into auditable, path-specific decisions about intervention, revalidat...
LLM-as-a-Judge is a flexible framework for text evaluation, which allows us to obtain scores for the quality of a given text from various perspectives by changing the prompt template. Two main challenges in using LLM-as-a-Judge are computational cost of inference using a large language model (LLM), especially when eval...
Modern data analysis increasingly requires identifying shared latent structure across multiple high-dimensional datasets. A commonly used model assumes that the data matrices are noisy observations of low-rank matrices with a shared singular subspace. In this case, two primary methods have emerged for estimating this s...
This paper derives the first upper regret bound that explicitly accounts for changes in the observations sampling frequency and formulates practical recommendations about dataset sizes and stale data policies of TVBO algorithms.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
GPT-Lab
· gpt-lab.eu
Nov 3, 2026
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
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