AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Category
9,568 papers
This work defines a modular late-fusion function that combines semantic similarity (cosine of embeddings) and structural similarity (bibliographic coupling) with a tunable weight alpha whose value is chosen according to the specific task.
Simulations demonstrate that TopoCompress effectively reduces cross-server traffic and deployment resource consumption while maintaining controllable inference quality, enabling efficient distributed MoE inference over bandwidth- and resource-constrained edge infrastructures.
A new loss function is proposed, which helps mitigate the effect of excess silence in training, improving STOI, frequency-weighted segmental SNR, and frequency-weighted segmental SNR from 4.35 to 5.12.
The Vector Archive Mapping ECAPA (VAM-ECAPA), a novel system designed to enhance feature extraction from short-duration speech, is proposed, which enriches information-scarce features by mapping them against a learnable Vector Archive of canonical speaker traits.
Reach audiences
Advertise in front of researchers, engineers, and readers.
Flash-dLLM is introduced, a training-free inference acceleration framework for fast and memory-efficient dLLMs that proposes an efficient KV-cache-driven draft-and-verify decoding strategy, where the dLLM itself serves as both drafter and verifier without requiring an auxiliary model.
The number of agents is revealed as a new scaling dimension for multi-agent organizations to expand the frontier of general intelligence, offering a practical solution for complex tasks under hard latency constraints or time budgets.
Distinguishing GPT-assisted from independently authored student writing has become a critical challenge in academia. This paper evaluates the discriminative capability of interpretable stylometric features extracted solely from submitted text. Using data from 90 participants who wrote both independently and with ChatGP...
GravityOCR is introduced, a parameter-shared AR-block-diffusion model jointly trained for parallel drafting and causal AR verification, and Verifying drafts before commitment lets the model commit multiple output tokens per round without a separate drafting network.
We introduce Knowledge Pull Requests (KPRs), a framework for continual document authoring that makes each change interpretable. Documents require ongoing revision as new knowledge surfaces from other sources, languages, or times, but existing approaches either edit with no account of what knowledge changed or regenerat...
PersonaWeaver is introduced, which disentangles world building from behavioral specification and models behavior through setting general, diverse, manually curated banks of moral positions and conversational reactions and allows us to test how far LLM(s) can be pushed beyond their default behavioral patterns across set...
A methodological framework for constructing new semantic knowledge at a higher level of abstraction is proposed under the notions of semantic compatibility and incompatibility for NLI, which will enable the development of more robust agents and interpretable reasoning, guiding AI toward reliable language understanding.
Large language models (LLMs) have increasingly been used to investigate how children acquire syntax at an early stage of development. This is notably the central scientific goal of the BabyLM challenge, a community-wide effort to develop models that achieve human-level syntactic performance while being trained on devel...
Natural Language Processing
Our most capable vision-language-action model enhances robot dexterity, reliability, and safety
A Blog post by Ai2 on Hugging Face
A Blog post by IBM Research on Hugging Face
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
