Beyond Diagnosis: Evaluating Multimodal LLMs for Pathology Localization in Chest Radiographs

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.

Google has launched MedGemma 1.5, an updated open-source medical AI model that can analyze 3D medical scans such as CTs and MRIs, alongside a specialized speech tool that reportedly surpasses OpenAI's Whisper in medical dictation tasks, though with strict licensing for clinical use.

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 novel Price-Incentive Mechanism (PRINCE) has been proposed to enhance Multi-Tenant Split Federated Learning (SFL) for Foundation Models (FMs) like GPT-4, enabling efficient fine-tuning on resource-constrained devices while maintaining privacy. This mechanism addresses the coordination challenges faced by multiple SFL tenants with diverse fine-tuning needs.