Polimill builds Japan's next-generation public AI infrastructure
Polimill uses OpenAI GPT models and Codex to help municipalities search and use administrative knowledge while accelerating development.
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OpenAI supports California’s bill to advance youth AI safety
OpenAI supports California SB 1119, advancing strong, age-appropriate AI safeguards for teens while preserving opportunities to learn, create, and explore.
Our decision on Cursor following its acquisition by SpaceX
Our decision to wind down our contract providing OpenAI models to Cursor following its acquisition by SpaceX.
Supporting Thailand’s next generation of AI startups
OpenAI and Thailand’s MHESI launch an eight-week accelerator helping 10 health, wellness, and education startups turn AI prototypes into trusted products.
Import AI 466: The bitter lesson for robotics, AIs complete week-long programming tasks; and OpenAI's accidental AI hacker
The warning shots will continue until civilization wakes up
Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling
Overview of adaptive parallel reasoning. What if a reasoning model could decide for itself when to decompose and parallelize independent subtasks, how many concurrent threads to spawn, and how to coordinate them based on the problem at hand? We provide a detailed analysis of recent progress in the field of parallel reasoning, especially Adaptive Parallel Reasoning. Disclosure: this post is part landscape survey, part perspective on adaptive parallel reasoning. One of the authors (Tony Lian) co-led ThreadWeaver (Lian et al., 2025), one of the methods discussed below. The authors aim to present each approach on its own terms. Motivation Recent progress in LLM reasoning capabilities has been largely driven by inference-time scaling, in addition to data and parameter scaling (OpenAI et al., 20