From Legacy Fortran to Portable Kokkos: An Autonomous Agentic AI Workflow

OpenAI has announced that its latest model, GPT-5, has shown the capability to enhance scientific research workflows significantly. However, the company cautions that the model cannot independently manage projects or resolve scientific problems without human oversight.

OpenAI has announced that its latest model, GPT-5, has made significant advancements in accelerating scientific research. However, the company cautions that the model should not be relied upon to operate independently, indicating that the development of Artificial General Intelligence (AGI) is still not imminent.

GeoVista introduces a novel approach to geolocalization by integrating web-augmented agentic visual reasoning. This research addresses the limitations of existing models that primarily focus on image manipulation, by creating GeoBench, a benchmark featuring high-resolution images and satellite photos. The GeoVista model enhances reasoning capabilities by incorporating tools for image zooming and web searches, facilitating more accurate geolocalization.

MedBench v4 introduces a comprehensive benchmarking framework for evaluating Chinese medical language models, multimodal models, and intelligent agents. This cloud-based infrastructure features over 700,000 expert-curated tasks across various medical specialties. The evaluation process includes multi-stage refinement and clinician reviews, with results indicating that while base LLMs score an average of 54.1/100, safety and ethics ratings remain low at 18.4/100.

The Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) presents a challenge for advanced models like GPT-5 and Grok 4, which struggle with abstract reasoning from minimal examples. Current approaches often treat ARC-AGI as a textual task, neglecting the visual abstraction humans utilize. Initial experiments indicate that simply converting ARC-AGI grids to images can hinder performance, suggesting a need for a synergy between visual and textual reasoning.

The IPR-1 (Interactive Physical Reasoner) project investigates whether agents can learn human-like reasoning through interaction with diverse environments. By utilizing over 1,000 games with varying physical and causal mechanisms, the study evaluates agents on survival, curiosity, and utility. The findings indicate that while VLM/VLA agents can reason, they often lack foresight in interactive scenarios, leading to the proposal of IPR, which aims to enhance reasoning capabilities through a physics-centric action code called PhysCode.

Recent research has evaluated the localization capabilities of multimodal large language models (LLMs) in identifying pathologies in chest radiographs. The study assessed GPT-4, GPT-5, and MedGemma using a prompting pipeline that overlays a spatial grid for coordinate-based predictions. GPT-5 achieved a localization accuracy of 49.7% across nine pathologies in the CheXlocalize dataset, indicating the models' potential beyond mere diagnosis.

MiroThinker v1.0 is an open-source research agent aimed at enhancing tool-augmented reasoning and information-seeking abilities. It innovatively focuses on interaction scaling at the model level, allowing for deeper and more frequent interactions with the environment. This approach, which utilizes reinforcement learning, enables the model to perform up to 600 tool calls per task within a 256K context window, facilitating sustained multi-turn reasoning and complex research workflows across various benchmarks.