Early science acceleration experiments with GPT-5

OpenAI has launched GPT-5.2, its latest AI model, which is designed to enhance productivity and has shown mixed results in tests compared to its predecessor, GPT-5.1. This development comes amid increasing competition from Google's Gemini 3, which has rapidly gained a significant user base.

The introduction of Time Puzzles marks a significant advancement in evaluating iterative temporal reasoning in large language models (LLMs). This task combines factual temporal anchors with cross-cultural calendar relations, generating puzzles that challenge LLMs' reasoning capabilities. Despite the simplicity of the dataset, models like GPT-5 achieved only 49.3% accuracy, highlighting the difficulty of the task.

A new framework called From Rows to Reasoning (FRTR) has been introduced to enhance the reasoning capabilities of Large Language Models (LLMs) when dealing with complex spreadsheets. This framework includes FRTR-Bench, a benchmark featuring 30 enterprise-grade Excel workbooks, which aims to improve the understanding of multimodal data by breaking down spreadsheets into granular components.

A new benchmark called KidVis has been introduced to evaluate the visual perceptual capabilities of Multimodal Large Language Models (MLLMs), specifically assessing their performance against that of 6-7 year old children across six atomic visual capabilities. The results reveal a significant performance gap, with human children scoring an average of 95.32 compared to GPT-5's score of 67.33.

A recent study introduces Uniqueness-Aware Reinforcement Learning (UARL), a novel approach aimed at enhancing the problem-solving capabilities of large language models (LLMs) by rewarding rare and effective solution strategies. This method addresses the common issue of exploration collapse in reinforcement learning, where models tend to converge on a limited set of reasoning patterns, thereby stifling diversity in solutions.