Omadia: A Self-Hostable Agentic OS for Multi-Agent AI Teams
Omadia is a self-hostable agentic operating system designed for building, running, and auditing multi-agent AI teams. It supports signed plugins, allows users to bring their own LLM keys, and emphasizes data ownership with EU/GDPR readiness.
SWE Refactor Bench: Can Coding Agents Complete a Long-Horizon, Whole-Repository Stack Migration?
A new benchmark, SWE Refactor Bench, has been introduced to evaluate the ability of coding agents to perform complex, whole-repository software migrations. Existing benchmarks are insufficient as they do not verify if the migration actually occurred, allowing agents to pass tests by copying original code. This benchmark addresses that gap by assessing both migration completeness and behavioral correctness.
OpenAI Unveils Jalapeño Inference Chip
OpenAI has introduced Jalapeño, a custom-designed inference chip. This new hardware aims to significantly improve the speed and power efficiency of AI inference tasks.
Loveholidays Leverages OpenAI Codex for Business-Wide Software Development
Travel company loveholidays is utilizing OpenAI's Codex to democratize software development across its business operations. This initiative aims to empower teams to transform concepts into functional products more rapidly.
OpenAI Report: AI Enhances Continuous Learning
A new report from OpenAI details how students and educators are leveraging ChatGPT to foster continuous learning. The findings highlight AI's role in providing educational support that transcends traditional classroom boundaries.
G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation
Researchers have introduced G-CARL, a novel reinforcement learning framework designed for patient-oriented medical report interpretation. This framework aims to bridge the gap in existing medical vision-language tasks by addressing the dual requirements of evidence-grounded factuality and context-dependent patient communication.
New Hugging Face Datasets for PDE LLM Evaluation
Three new Hugging Face datasets have been released for evaluating Large Language Models (LLMs) on Partial Differential Equations (PDEs). These datasets, named bermaneh/pde-llm-eval-freegen-xmodal-qwen__qwen3-8-27b, bermaneh/pde-llm-eval-freegen-xmodal-qwen__qwen3-6-27b, and bermaneh/pde-llm-eval-freegen-xmodal-qwen__qwen3-5-27b, contain tabular and text data for free-generation PDE evaluation.