AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,222 papers
The Particle Accelerator Principle transitions generative-AI-assisted scientific research from passive textual acceptance to an active, rigorous experimental discipline. A Large Language Model is not an oracle, but a complex, non-linear experimental apparatus.By treating individual AI runs as candidate collision events...
We quantify the mutual information I between topological sector labels T and CHSH values S in two-dimensional lattice models. Throughout, I denotes a classical Shannon mutual information between a global discrete label and a scalar measured value; it is not an entanglement entropy between spatial regions. In the Z₂ lat...
Whether a temporal (Leggett–Garg) measurement can certify the computational power of an anyonic braid representation—its density in the unitary group, the property underlying universal topological quantum computation—has, to our knowledge, not been studied. We give a complete operational characterization of when the di...
We numerically test the three-time Leggett–Garg inequality K₃ ≤ 1 for the standard B₃ Fibonacci-anyon braiding representation on the two-dimensional fusion space of three τ anyons. Exhaustive enumeration over all 4^L braid words up to length L=11 and random sampling to L=40 show that K₃ saturates the Lüders bound 3/2 t...
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We construct a sequential measurement protocol on the n=8 Fibonacci-anyon fusion space of dimension D = F₇ = 13 whose CHSH-form expectation value attains |S| = 3.406, above the value 2√2≈ 2.828 that bounds CHSH correlations under the standard setting-locality precondition, which this construction does not satisfy. The...
We report a complete sequence-by-sequence landscape of CHSH-form values for six Fibonacci anyons in two encodings, covering all words of length L=3–12 with two generators and L=2–9 with three. In the two-generator d₁ᵦ encoding, braiding alone cannot exceed the classical value: the two generators act on different anyons...
We quantify the mutual information I between topological sector labels T and CHSH values S in two-dimensional lattice models. Throughout, I denotes a classical Shannon mutual information between a global discrete label and a scalar measured value; it is not an entanglement entropy between spatial regions. In the Z₂ lat...
Software issue resolution task aims to address real-world issues in software repositories based on natural language descriptions provided by users, representing a key aspect of software maintenance. With the rapid development of large language models (LLMs) in reasoning and generative capabilities, LLM-based approaches...
Proteins can be studied through their sequence statistics or structural properties. These represent complementary views that are useful but lack a quantitative framework to tell, family by family, which is most informative and how to combine them. We introduce a framework that builds both views in parallel: amino acid...
Agentic analytics with governed tool use: Portable multi-cloud study Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Credential: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Abstract Enterprise generative AI becomes dependable only wh...
AI copilots for cloud engineering: Google Cloud study Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Credential: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Abstract Enterprise generative AI becomes dependable only when retrieval, t...
Long-context versus retrieval architectures: Google Cloud study Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Credential: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Abstract Enterprise generative AI becomes dependable only when re...
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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