AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,379 papers
This work presents a reproducible empirical comparison of Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF) for Large Language Model alignment. Experiments are conducted on GPT-2 (124M parameters) using the Anthropic HH-RLHF dataset. The study evaluates alignment quality, reward...
Two AI systems can reach the same scientific conclusion for different reasons: one may generate the candidate sooner, another may check it more cheaply, and a third may obtain decisive evidence earlier. An end-to-end score records success while hiding which interface made success possible, which resource an interventio...
This paper records and analyzes the "Mirror Amplification Effect" observed during the author's sustained high-density dialogue with a large language model (Google Gemini). This refers to the mechanism by which AI reflects and amplifies the emotional state, vocabulary, and self-destructive patterns of the interlocutor....
Large language models produce self-referential utterances — about their own phenomenal states, internal processes, memory, capabilities and identity — in settings where privileged access to the relevant states has not been demonstrated. Recent work characterises this as self-narration rather than introspection. We iden...
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An opinionated annotated bibliography for the BDPD project. Each entry contains a full citation, the original abstract (where available, reproduced as academic fair use), and an AI Note — a single-paragraph annotation structured around three frames: what the work argues and with what method; why it is relevant to BDPD...
Abstract As large language models (LLMs) transition from static tools to fully agentic systems, their potential for transforming social science research is well recognized. This paper introduces a structured framework for understanding the diverse applications of agentic LLM systems, ranging from simple data processors...
The standard \LambdaCDM cosmological model inevitably encounters a spacetime singularity when extrapolated backward in time, where the scale factor a \to 0, density \rho \to \infty, and general relativity breaks down. While the Penrose-Hawking singularity theorems formalize this breakdown, they do not specify what phys...
We present InventOR, a prompt-configured large-language-model (LLM) workflow that generates material-level (r, Q) inventory policies from cutoff-bounded historical CSVs and inline parameter blocks through an enterprise LLM application, without project-specific model training or fine-tuning. Deterministic parsing and po...
Fault diagnosis methods for mechanical equipment should not only identify fault categories but also provide verifiable diagnostic evidence. Existing deep learning models usually output only labels or confidence scores, making it difficult to estab-lish an interpretable diagnostic reasoning process. Large language model...
Research software for international-business meta-analysis: semi-automated effect-size extraction from academic PDFs through a vendor-neutral large-language-model adapter, human-in-the-loop verification by the principal investigator, and an immutable data-lock workflow that exports a reproducible effect-size dataset fo...
We present InventOR, a prompt-configured large-language-model (LLM) workflow that generates material-level (r, Q) inventory policies from cutoff-bounded historical CSVs and inline parameter blocks through an enterprise LLM application, without project-specific model training or fine-tuning. Deterministic parsing and po...
Research software for international-business meta-analysis: semi-automated effect-size extraction from academic PDFs through a vendor-neutral large-language-model adapter, human-in-the-loop verification by the principal investigator, and an immutable data-lock workflow that exports a reproducible effect-size dataset fo...
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A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
We’re giving developers, enterprises, scientists, and consumers access to Gemini 4 Argon, our latest frontier model.
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