distillation

Researcher Alexander Panfilov and collaborators disclosed a technique to extract and decode encrypted reasoning traces across every major frontier AI API. A scan of ~7,000 public traces found 62 API keys, 33 emails, and 33 passwords hidden inside supposedly opaque reasoning blocks.

Meta has released Muse Glimmer, a 30-billion-parameter open-weight model under Apache 2.0, its first open release since Llama 4 in spring 2025. The launch comes with a Zuckerberg essay defending distillation of rival models and hinting at a future 'dynamic auction' pricing scheme for compute.

Moonshot AI has released open weights, a technical report, and supporting infrastructure for Kimi K3, a model that claims 2.5x more intelligence per unit of compute. Independent testing found notable gaps in cybersecurity and math performance compared to Western frontier models.

A joint evaluation by the UK AI Security Institute and U.S. Center for AI Standards and Innovation found Kimi K3 scores 32.2% on the ExploitBench benchmark versus 76.2% for leading U.S. models, though it beats China's GLM-5.2 at 24.4%. The gap may stem from Moonshot AI distilling Claude outputs that exclude advanced offensive cyber content.

Chinese company Moonshot AI released Kimi K3, an open source model that the company claims demonstrates frontier-level performance while trailing only Claude Fable 5 and GPT 5.6 Sol. Independent analyses from Arena.ai and Vals AI suggest the model is competitive with flagship frontier models, reigniting debate about Chinese AI capabilities and open source model development.

Anthropic announced it is limiting Mythos, its newest model, to large enterprises and critical infrastructure operators rather than releasing it publicly, claiming the model's ability to discover software security exploits poses risks. The restricted rollout strategy mirrors planned approaches by OpenAI and may serve dual purposes: managing security concerns while preventing smaller competitors from using distillation techniques to replicate frontier model capabilities.

Apple has secured full access to Google's Gemini models within its data centers and is using knowledge distillation to generate training data for smaller, on-device AI models. The approach allows Apple to create lightweight versions that replicate Gemini's reasoning patterns while running directly on Apple devices, requiring significantly less processing power.