Navigating Gigapixel Pathology Images with Large Multimodal Models

Physicist Steve Hsu has published a research paper based on an idea generated by GPT-5, highlighting the potential of AI in scientific inquiry while cautioning about its reliability, likening it to a 'brilliant but unreliable genius.'

The recent release of GPT-5 by OpenAI has sparked a negative shift in public sentiment towards AI, with many users criticizing the model for its perceived flaws rather than recognizing its capabilities. This backlash has led to claims that AI progress is stagnating, with some commentators labeling the technology as 'AI slop'.

OpenAI has developed a version of GPT-5 that can admit to its own errors, a significant step in addressing concerns about AI honesty and transparency. This new capability, referred to as 'confessions', aims to enhance the reliability of AI systems by encouraging them to self-report misbehavior. However, experts caution that this is not a comprehensive solution to the broader safety issues surrounding AI technology.

The introduction of ViRectify presents a new benchmark aimed at evaluating the error correction capabilities of multimodal large language models (MLLMs) in complex video reasoning tasks. This benchmark addresses the existing gap in systematic evaluation, providing a dataset of over 30,000 instances across various domains such as dynamic perception and scientific reasoning.

A new benchmark called AdversarialAnatomyBench has been introduced to evaluate vision-language models (VLMs) against naturally occurring rare anatomical variants, revealing significant performance drops in state-of-the-art models like GPT-5 and Gemini 2.5 Pro when faced with atypical anatomy. The accuracy decreased from 74% on typical anatomy to just 29% on atypical cases.

A comparative study has been conducted on the object counting capabilities of two multi-modal large language models, GPT-4o and GPT-5, focusing on their performance in zero-shot scenarios using only textual prompts. The evaluation was carried out on the FSC-147 and CARPK datasets, revealing that both models achieved results comparable to state-of-the-art methods, with some instances exceeding them.

A recent paper has introduced a quantifiable framework for defining Artificial General Intelligence (AGI), proposing that AGI should match the cognitive versatility of a well-educated adult. This framework is based on the Cattell-Horn-Carroll theory and evaluates AI systems across ten cognitive domains, revealing significant gaps in current AI models, particularly in long-term memory storage.

Anthropic and OpenAI have recently showcased their respective AI models, Claude Opus 4.5 and GPT-5, highlighting their distinct approaches to security validation through system cards and red-team exercises. Anthropic's extensive 153-page system card contrasts with OpenAI's 60-page version, revealing differing methodologies in assessing AI robustness and security metrics.