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Topic: "reasoning-core-hypothesis"
not much happened today
gpt-oss-120b gpt-oss-20b kimi-k2 deepseek-r1 qwen-3-32b openai huggingface microsoft llamaindex ollama baseten fireworksai cerebras groq together anthropic google uk-aisi sliding-window-attention mixture-of-experts rope context-length mxfp4-format synthetic-data reasoning-core-hypothesis red-teaming benchmarking coding-benchmarks model-performance fine-tuning woj_zaremba sama huybery drjimfan jxmnop scaling01 arunv30 kevinweil xikun_zhang_ jerryjliu0 ollama basetenco reach_vb gneubig shxf0072 _lewtun
OpenAI released its first open models since GPT-2, gpt-oss-120b and gpt-oss-20b, which quickly trended on Hugging Face. Microsoft supports these models via Azure AI Foundry and Windows Foundry Local. Key architectural innovations include sliding window attention, mixture of experts (MoE), a RoPE variant, and a 256k context length. The models use a new MXFP4 format supported by llama.cpp. Hypotheses suggest gpt-oss was trained on synthetic data to enhance safety and performance, supporting the Reasoning Core Hypothesis. OpenAI announced a $500K bounty for red teaming with partners including Anthropic, Google, and the UK AISI. Performance critiques highlight inconsistent benchmarking results, with GPT-OSS-120B scoring 41.8% on the Aider Polyglot coding benchmark, trailing competitors like Kimi-K2 and DeepSeek-R1. Some users note the model excels in math and reasoning but lacks common sense and practical utility.