The fictional name of the hamlet where the story begins is **Kraighten**....
ViewIn the GPT-5 evaluation on HealthBench Hard, the score for the gpt-5-thinking model is reported to be 46.2%, which shows a substantial improvement from 31.6% for OpenAI o3. The gpt-5-thinking-mini model also performed well, achieving a score of 40.3% on HealthBench Hard, outperforming all previous m...
ViewGPT-5 reduces hallucinations by focusing on training models to browse effectively for up-to-date information and minimizing hallucinations when relying on their internal knowledge. The system demonstrated a significantly lower hallucination rate compared to its predecessors, with gpt-5-thinking exhi...
View"We have a proactive multi-layered defense stack which includes model safety training." — Unknown "These safeguards sufficiently minimize the associated risks under our Preparedness Framework." — Unknown "We believe this risk is sufficiently minimized under our Preparedness Framework." — Unknown "We...
ViewThe GPT-5 System Card describes a unified system of models designed to answer a wide variety of queries with both fast responses and deeper reasoning capabilities. The system comprises variants such as gpt-5-main, gpt-5-main-mini, gpt-5-thinking, gpt-5-thinking-mini, and gpt-5-thinking-nano. The car...
ViewThe model card for gpt-oss-120b and gpt-oss-20b outlines their capabilities and safety measures, emphasizing that they are designed for instruction following, tool use, and reasoning. These models utilize a mixture-of-experts architecture with quantization techniques to operate efficiently. Evaluati...
ViewQuantization reduces the memory footprint of the models. Models are post-trained with quantization of the Mixture-of-Experts weights. Weights are quantized to 4.25 bits per parameter. Quantizing MoE weights enables the larger model to fit on a single 80GB GPU. The smaller model can run on systems wi...
ViewMultilingual capabilities were evaluated using the MMMLU evaluation. The gpt-oss-120b at high reasoning performs nearly as well as OpenAI o4-mini. The MMMLU evaluation included professionally human-translated versions in 14 languages. gpt-oss-120b's average accuracy in MMMLU high reasoning is 81.3%....
View"Safety is foundational to our approach to open models." — OpenAI "Rigorously assessing an open-weights release’s risks should include testing for a reasonable range of ways a malicious party could feasibly modify the model." — OpenAI "We confirmed that the default model does not reach our indicativ...
ViewThe evaluation framework for the Test-Time Diffusion Deep Researcher (TTD-DR) agents is designed to rigorously assess the performance of these agents in generating long-form, comprehensive research reports. The framework encompasses several components including the definition and application of eval...
ViewTest-Time Diffusion Deep Researcher (TTD-DR) is a novel deep research framework. TTD-DR improves report generation by modeling it as a diffusion process. TTD-DR outperforms existing deep research agents in generating complex research reports. OpenAI Deep Research is a leading research agent in compa...
ViewThe most important takeaways from the text include the evolution of model training, where earlier models required extensive fine-tuning, which was time-consuming. In contrast, current methods leverage in-context learning, allowing for quicker adaptations to new tasks. This shift marks a significant ...
ViewThe music industry continues to lag behind video streaming revenues primarily due to several factors. Music monetisation has significantly lagged consumption, attributed to a lack of price increases, dilution from bundles, and limited customer segmentation compared to video streaming services like N...
ViewEmerging markets are playing an increasingly decisive role in reshaping the global music streaming landscape. Recent research highlights that since 2021, emerging markets have become the major driver of subscription growth, with their contribution to net subscriber additions rising significantly. By...
View"We believe that the past 12 months have largely allayed initial fears around the impact of Generative AI for music labels." — Unknown "We believe the industry is still in an experimental phase; new gen AI music start-ups are proliferating." — Unknown "We look for the first commercial licensing agre...
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