Categories: AI/ML News

Language models can use steganography to hide their reasoning, study finds

Large language models (LLMs) can utilize ‘encoded reasoning,’ a form of steganography, to subtly embed reasoning steps within their responses, enhancing performance but potentially reducing transparency and complicating AI monitoring.Read More

AI models stumble on basic multiplication without special training methods, study finds

These days, large language models can handle increasingly complex tasks, writing complex code and engaging in sophisticated reasoning. But when it comes to four-digit multiplication, a task taught in elementary school, even state-of-the-art systems fail. Why?

December 30, 2025

In "AI/ML News"

Transparency is often lacking in datasets used to train large language models, study finds

In order to train more powerful large language models, researchers use vast dataset collections that blend diverse data from thousands of web sources. But as these datasets are combined and recombined into multiple collections, important information about their origins and restrictions on how they can be used are often lost…

August 31, 2024

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GPT-4 matches human performance on analogical reasoning tasks, study shows

Can large language models (LLMs) reason by analogy? Some outputs suggest that they can, but it has been argued that these results reflect mimicry of the results of analogical reasoning in the models' training data.

May 28, 2025

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AI Generated Robotic Content