AI in the news: June 26, 2026 — Open-Source Learns to Build Its Own Scaffolding

Open-Source Learns to Build Its Own Scaffolding DeepReinforce released Ornith-1.0, an open-source coding model family that doesn't just compete with frontier lab models — it beats one of Anthropic's named Claude models on two coding benchmarks. The key isn't the benchmark number; it's how it got there: the model learns to write its own training scaffold, jointly optimizing the support structure and the solution at the same time. This release crystallizes the week's deepest pattern — open-source is no longer catching up, it's beginning to define the architecture others will copy. Featured story DeepReinforce Releases Ornith-1.0: An Open-Source Coding Model Family That Learns Its Own RL Scaffolds — MarkTechPost Also today Reinforcement Learning without Ground-Truth Solutions can Improve LLMs — arXiv cs.LG `[4aba859fe3]` Joint Learning of Experiential Rules and Policies for Large Language Model Agents — arXiv cs.AI `[01c1883e58]` E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation — arXiv cs.AI `[2eea1821ce]` Prompt Injection in Automated Résumé Screening with Large Language Models: Single and Multi-Injection Settings — arXiv cs.AI `[8672035565]` HarmVideoBench: Benchmarking Harmful Video Understanding in Large Multimodal Models — arXiv cs.CL `[06501089eb]` NuclearQAv2: A Structured Benchmark for Evaluating Domain-Science Competence in Large Language Models — arXiv cs.CL `[2680e5539a]` When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models — arXiv cs.AI `[1b75e30257]` Hallucination in World Models is Predictable and Preventable — arXiv cs.LG `[0c5b898598]` When are likely answers right? On Sequence Probability and Correctness in LLMs — arXiv cs.LG `[de01579989]` Hierarchical Muon: Tiled Newton-Schulz Updates for Efficient Muon Optimization — arXiv cs.LG `[c590a37cac]`