MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling

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.

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.

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 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.

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 paper discusses the transition from legacy Fortran codebases to modern Kokkos frameworks in the context of High-Performance Computing (HPC). As HPC evolves towards heterogeneous GPU-accelerated systems, the lack of native Fortran bindings necessitates the modernization of legacy codes for portability. Kokkos offers a C++ abstraction for performance portability, but converting Fortran to Kokkos requires significant expertise. The study introduces an autonomous AI workflow utilizing large language models (LLMs) to automate the translation and optimization of Fortran kernels, addressing the c…