AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Category
9,568 papers
Radial Consensus Score (RCS) is introduced, a simple, efficient, and training-free method for best-of-N$ selection that enables it to recover semantically coherent correct answers despite lexical fragmentation, and suggests that geometric consensus can serve as a scalable and reliable alternative to voting-based method...
Automatic Speech Recognition (ASR) systems are widely used in everyday communication, education, healthcare, and industry, yet their performance remains uneven across speakers, particularly when dialectal variation diverges from the mainstream accents represented in training data. This study investigates ASR bias throu...
Recent work provides overwhelming evidence that LLMs, even those trained to scale their reasoning trace, quickly deteriorate at planning as problems become more complex. LLM-as-Formalizers aim to address this by employing LLMs as a bridge to translate natural language descriptions into structured planning representatio...
TabAgent is proposed, a framework for replacing generative decision components in closed-set selection tasks with a compact textual-tabular classifier trained on execution traces, establishing a paradigm for learned discriminative replacements of generative bottlenecks in production agent architectures.
Reach audiences
Advertise in front of researchers, engineers, and readers.
Do Large Language Models (LLMs) genuinely grasp the compositional semantics of events, or do they rely on surface-level probabilistic heuristics? We investigate the Imperfective Paradox, a logical phenomenon where the past progressive aspect entails event realization for activities (e.g., running $\to$ ran) but not for...
Large language models (LLMs) are increasingly used in situations where human values are at stake, such as decision-making tasks that involve reasoning when performed by humans. We investigate the so-called reasoning capabilities of LLMs over novel symbolic representations by introducing an experimental framework that t...
The sudden emergence of induction circuits and the ability to perform in-context copying in LLMs coincides with a sharp drop in training loss over a short span of the training process. This co-occurrence has been cited as evidence that it marks the point at which a wide range of in-context learning (ICL) capabilities b...
This work studies cross-lingual mathematical reasoning transfer in English-centric LLMs under controlled multilingual evaluation and finds that transfer is highly variable across initial model choice, target language, and training paradigm.
Subword-based tokenization methods often fail to preserve morphological boundaries, a limitation especially pronounced in low-resource, morphologically complex languages such as those written in the Ge'ez script. To address this, we present Morpheme-aware Subword Vocabulary Construction (MoVoC) and train MoVoC-Tok, a t...
Kongzi is proposed, a language model for historical understanding and reasoning that combines continued pre-training on historical corpora, supervised fine-tuning, and fact-enhanced reinforcement learning that improves the mean answer scores of both model sizes and their CMMLU-Chinese-History accuracy.
Non-cognitive skills are crucial for personal and social life well-being, and such skill development can be supported by narrative-based (e.g., storytelling) technologies. While generative AI enables interactive and role-playing storytelling, little is known about how users engage with and perceive the use of AI in soc...
Test-time scaling has emerged as a key inference strategy for reasoning models, where a model generates a large number of candidate solutions in the hope of finding at least one correct solution. Running all candidate solutions to completion can be computationally demanding, so recent work prunes the candidate pool aft...
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.
