AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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8,685 papers
Conditional Progressive Pruning (CPP) is proposed, a lightweight pruning framework that fully exploits multi-round MAD and is the first to fully outperform consistency methods.
Findings show why decision systems need joint evaluation of accuracy, confidence calibration, and probability coherence in the workflow used by an application.
SlopBench asks which models produce the stiff, repetitive prose readers call AI slop, a question detectors leave open once they have classified a text as machine-written. We evaluated eighteen models on 112 hand-written tasks in email, social posts, essays, and workplace chat, sampling each model on each task up to ten...
This work introduces NSV-Shift, a contrastive benchmark for evaluating whether speech-to-speech models can understand non-speech vocalizations (NSVs) and adapt their responses accordingly, and evaluates five models on NSV perception, emotion understanding, and response adaptation.
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This work investigates how large language models acquire metacognitive monitoring, the ability to know what one knows, by training 10 open-weight LLMs to predict their own accuracy on factual multiple-choice questions before answering them and finds that trained confidence reflects two distinct signals.
These findings motivate a state-action controller that starts from the base model action and selectively edits it using structural readouts, without requiring a complete predicted belief state as an intermediate representation.
This work studies two forms of demonstration, collated and interleaved demonstration, across six encoder-decoder models, spanning conventional cross-attention-based and LLM-based architectures and suggests that in-context adaptation for ASR is not unique to specific architectures, training, or demonstration approaches.
LA-CPD is proposed, a structured method that transforms noisy sentence-level score sequences into coherent authorship segments and outperforms WCP+AIC, increasing sentence-level accuracy from 0.747 to 0.796 while improving boundary localization and LLM-span delineation.
Large language models are increasingly used as interactive writing tools, where users develop stories, revise ideas, and introduce new requirements across multiple turns rather than specifying a complete brief upfront. Yet most evidence on multi-turn instruction degradation comes from tasks with objectively verifiable...
This paper identifies Unicode's NFC normalization stability policy as the structural constraint that prevents a straightforward fix, arguing for direct intervention of the Consortium in solving the active problem, and proposing a formal encoding request for the four core Yor\`ub\'a characters as the most durable path t...
CoCal is introduced, which trains a lightweight companion from rollout hidden states and verifier-derived correctness supervision while leaving task optimization unchanged, and shows that CoCal improves confidence estimation without sacrificing task performance, outperforming both RL-based concurrent methods and matche...
Generative workflows improve structural reliability and reduce judged defects on short passages, while their large recovery advantage on long documents disappears after chunking, suggesting that reference recovery alone understates alignment quality.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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.
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
A Blog post by Liquid AI on Hugging Face
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