AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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8,786 papers
This work presents a first study of how hybrid attention impacts the multilinguality of LLMs, showing that cross-lingual representations in hybrid models develop in patterns tied to the ordering of recurrent and full-attention layers.
Coding agents are increasingly deployed for iterative development on real repositories, yet existing evaluation barely answers a basic question: \emph{do coding agents reuse existing code or reinvent the wheel?} The question matters: every duplicated implementation is a fix applied twice and agents produce code far fas...
An expert-driven methodology for constructing such alignment data and applying it to a normative framework grounded in Islamic ethical, theological, and jurisprudential traditions is presented.
Across three model families, two small groups of attention heads are identified with distinct roles in introspective reporting supporting introspective detection and localization of large language models.
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Evaluations of Large Language Models (LLMs) morality typically consider decisions in isolation, thus overlooking whether an individual's unrelated prior conduct influences the model's subsequent choices. This leaves open the question of whether, and to what extent, moral history shapes LLM decisional behaviors. Prior w...
The VEX-Bench is introduced, a unified benchmark for evaluating the verification complexity of LLM-generated misinformation, as perceived during screening, across models and generation methods, and shows that no single method dominates all dimensions, underscoring the need for multi-dimensional evaluation.
A single encoder trained contrastively to isolate the targeted fact, utilizing synthetic data generated by an LLM and dataset both disjoint from all evaluation settings improves performance on 120 of 126 evaluation settings (95%) spanning 18 model dataset combinations across language and vision-language models.
CREDO reads the confidence from a dedicated token pair in the model's output distribution and trains it by differentiable regression, turning the trained confidence into a signal for accuracy, weighting rollouts by how far confidence and outcome disagree, so that accuracy and calibration improve together.
DivOPD, a simple learner-side batch-selection method that spreads a fixed turn budget across more rollouts and, within each rollout, prioritizes turns with larger cumulative teacher-student disagreement, is introduced.
This work introduces LongPuzzleBench, 114 levels in six puzzle games played through native GUI actions, where one objective can take a human over a thousand actions on persistent boards and dead ends go unannounced.
ESCD aggregates prefix-related teacher events and supervises the total probability of byte-compatible one-step student completions, avoiding tokenizer-dependent probability splits among individual tokens, and support event entry and event completion as complementary supervision targets for cross-tokenizer knowledge tra...
This work systematically evaluates 15 LLMs spanning three model generations as both generators and detectors, revealing that detection efficacy is primarily driven by detector capability rather than generator provenance, although outputs from newer generators remain notably harder to detect.
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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