Abstract: This study proposes LiP-LLM: integrating linear programming and dependency graph with large language models (LLMs) for multi-robot task planning. For multi-robots to efficiently perform ...

Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning ...

Artificial intelligence data annotation startup Encord, officially known as Cord Technologies Inc., wants to break down barriers to training multimodal AI models. To do that, it has just released what ...

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...

A Polish programmer running on fumes recently accomplished what may soon become impossible: beating an advanced AI model from OpenAI in a head-to-head coding competition. The 10-hour marathon left him ...

Missouri lawmakers have banned educators from leaning on a model of reading instruction called the “three-cueing” method as part of a bipartisan education package signed by Gov. Mike Kehoe on ...

Apple quietly dropped a new AI model on Hugging Face with an interesting twist. Instead of writing code like traditional LLMs generate text (left to right, top to bottom), it can also write out of ...

Google's AI advancement is not slowing down, and we might be getting yet another powerful model codenamed "Gemini Kingfall." As spotted by users on X, Gemini Kingfall briefly appeared on AI Studio for ...

OpenAI on Monday launched a new family of models called GPT-4.1. Yes, “4.1” — as if the company’s nomenclature wasn’t confusing enough already. There’s GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano, all of ...

OpenAI researchers have admitted that even the most advanced AI models still are no match for human coders — even though CEO Sam Altman insists they will be able to beat “low-level” software engineers ...

The process of updating deep learning/AI models when they face new tasks or must accommodate changes in data can have significant costs in terms of computational resources and energy consumption.