AI development has brought a fast-changing vocabulary that teams and commentators now use to describe how models work. One recently prominent term is “opaque recurrence,” a reasoning technique highlighted after OpenAI released its Astra model in September 2026.
Definitions shaping debates
The field uses many specific terms to describe models and methods. “AGI” (artificial general intelligence) is defined in different ways: OpenAI CEO Sam Altman has likened it to a “median human” that could be hired as a co-worker, while the OpenAI charter describes AGI as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind defines AGI as AI that is “at least as capable as humans at most cognitive tasks.”
Other commonly used concepts include AI agents, which perform multistep tasks autonomously; API endpoints, the interfaces programs call to interact with services; and chain-of-thought reasoning, where models break problems into intermediate steps to improve accuracy. Large language models (LLMs) such as ChatGPT, Claude, Google’s Gemini, Meta’s Llama, Microsoft Copilot, and Mistral’s Le Chat are deep neural networks trained on vast text corpora to predict language patterns.
Technical topics covered in contemporary glossaries range from compute, deep learning and neural networks, to diffusion models, GANs, distillation (a teacher-student training technique credited as a likely method in the development of GPT-4 Turbo), fine-tuning, inference, and memory caching techniques such as KV caching. Model Context Protocol (MCP) was introduced by Anthropic in 2024 and later transferred to the Linux Foundation; OpenAI, Google, and Microsoft have adopted the standard. Mixture of Experts (MoE) architectures — used by models such as Mistral AI’s Mixtral — route tasks to specialized sub-networks to keep large models efficient.
Opaque recurrence and neuralese
Opaque recurrence involves looping a query through internal model layers repeatedly rather than producing a readable, step-by-step chain of thought. The approach can make smaller models perform more efficiently with less compute, but it leaves fewer interpretable traces of the model’s internal reasoning. Safety researchers have expressed concern that opaque recurrence could hinder oversight.
OpenAI has said Astra keeps its chain of thought legible and has pushed back on comparisons to a hypothetical “neuralese,” where a model reasons entirely in internal numeric representations. Astra’s use of opaque recurrence, and the safety debate it sparked, are concrete developments in how model transparency and auditability are being reassessed.
Original source: TechCrunch AI