AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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17,191 papers
Together, the findings support a claim-indexed reporting standard that connects each claim to the evaluation design needed to test it, and evidence for repeatability and improved procedures remains less developed than product evaluation.
Factual recall is a lookup: some representation inside the network serves as the key, and what that key is made of decides which queries can retrieve the fact, what an edit must cover, and what a retriever can read. We call it the factual access state. In autoregressive (AR) models the subject position enriches attribu...
TopologyAware RouteConsistency Modeling (TRCM), which reuses node representations from a standard autoregressive construction pipeline to predict pairwise same-route probabilities and injects them into customer selection as a route-consistency signal, supports explicit route-consistency modeling as an effective structu...
Large language models (LLMs) are increasingly used within everyday applications where their responses have significant downstream impacts. For responsible deployment of such systems, we desire insight into how much we can trust their individual outputs. A popular approach for estimating LLM confidence is to train probe...
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AnchorRM, an anchor-based residual memory mechanism that reconstructs event-specific intra-batch node states without executing sequential recurrent updates, substantially improves the scalability and training efficiency of memory-based temporal graph learning while maintaining competitive predictive performance.
Safety pretraining has emerged as a promising paradigm for embedding safety deeply into a model during pretraining, yielding robustness to subsequent fine-tuning that conventional safety post-training often lacks. We refer to this safety acquired during model pretraining as native safety. Yet native safety is expensive...
Retrospective Self-Distillation (RSD), a multi-round framework that turns such experience into dense supervision using privileged retrospective context, and frontier-guided context-policy evolution, where a stronger frontier model proposes constrained edits to how privileged context is constructed.
Mitigating catastrophic risks from large language models requires excising hazardous operational capabilities, such as cyberwarfare exploitation and pathogen enhancement, without degrading benign scientific utility. In these dual-use domains, legitimate defensive science and malicious attacks share vocabulary, syntax,...
EmbGA is presented, an embedding-space genetic algorithm that recovers concepts present in a set of images but absent from the prompt that generated them, and runs much faster than language-model-guided search.
It is argued that continual prompt evolution is a practical, training-free alternative to retraining for multimodal forecasting.
On-policy learning is central to post-training: training on a model's own generations limits distribution shift and helps preserve existing capabilities. On hard problems, however, on-policy RL provides no reward-based learning signal when all attempts fail. On-policy distillation provides next-token supervision from a...
Vector-Borda Empirical-Gap Elimination (VB-EGE) is presented, a fixed-confidence adaptive sampling algorithm for efficiently identifying models on the Pareto frontier in arena-style evaluation and established a worst-case lower bound showing that VB-EGE is minimax-optimal up to logarithmic factors.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
A Blog post by Ai2 on Hugging Face
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
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