OpenAI Introduces MRC (Multipath Reliable Connection): A New Open Networking Protocol for Large-Scale AI — 2026-05-07

## Short Segments Meta AI's NeuralBench framework is set to transform how we evaluate AI models trained on brain signals. This open-source tool standardizes benchmarking across 36 EEG tasks and 94 datasets, making it easier to compare model performance. Coming up, we'll explore how OpenAI's new networking protocol aims to solve AI bottlenecks, and later, Zyphra's latest model that outperforms its size. But first, let's dive into NeuralBench. Evaluating AI models trained on brain signals has long been inconsistent, with different research groups using varied preprocessing pipelines and datasets. Meta AI's NeuralBench aims to fix this by providing a unified framework for benchmarking NeuroAI models. Its first release, NeuralBench-EEG v1.0, is the largest open benchmark of its kind, covering 36 tasks, 94 datasets, and over 13,000 hours of EEG data. This framework allows researchers to evaluate 14 deep learning architectures under a single standardized interface, addressing the fragmented evaluation landscape in NeuroAI. By standardizing benchmarks, NeuralBench helps researchers identify which models work best for specific tasks, ranging from clinical seizure detection to decoding sensory inputs. This development is crucial as the field of NeuroAI continues to grow, with self-supervised learning techniques being adapted for brain foundation models. With NeuralBench, Meta AI provides a much-needed tool for the community, enabling more consistent and reliable evaluations of AI models trained on brain signals. Zyphra's ZAYA1-8B model is redefining what's possible with smaller AI models. This Mixture of Experts model, trained on AMD hardware, outperforms larger models on math and coding benchmarks. Let's explore how it achieves this feat. Zyphra AI has released ZAYA1-8B, a Mixture of Experts language model with 760 million active parameters and 8.4 billion total parameters. Despite its smaller size, ZAYA1-8B outperforms larger open-weight models on math and coding benchmarks. Trained end-to-end on AMD hardware, the model is available under an Apache 2.0 license on Hugging Face and as a serverless endpoint on Zyphra Cloud. ZAYA1-8B achieves competitive scores with first-generation frontier reasoning models on challenging tasks, thanks to its novel test-time compute methodology called Markovian RSA. This approach allows the model to surpass others like Claude 4.5 Sonnet and GPT-5-High on specific benchmarks. The Mixture of Experts architecture activates only a subset of parameters per input, reducing compute and memory requirements while maintaining high performance. This makes ZAYA1-8B suitable for on-device deployment and efficient test-time compute, offering lower latency compared to dense models with similar performance. Zyphra's release demonstrates the potential of smaller, efficient models in AI applications. Amazon Bedrock AgentCore Payments is set to revolutionize how AI agents transact. Built with Coinbase and Stripe, this new feature enables agents to access and pay for resources instantly. Let's see how this changes the landscape for developers. Amazon has announced Bedrock AgentCore Payments, a new feature in Amazon Bedrock AgentCore, developed in partnership with Coinbase and Stripe. This feature allows AI agents to instantly access and pay for resources like web content, APIs, and MCP servers. As AI agents take on more complex tasks, the need for seamless transactions becomes critical. Bedrock AgentCore Payments provides the infrastructure for agents to transact autonomously, with real-time billing and secure payment flows. This development simplifies the process for developers, who previously had to manage bespoke billing relationships and compliance requirements. By integrating payment capabilities directly into the agentic platform, Amazon enables developers to build, connect, and optimize agents at scale, reducing engineering effort and potential errors in payment flows. As the agentic economy evolves, Bedrock AgentCore Payments positions Amazon as a key player in supporting the next generation of AI-driven commerce. ## Feature Story OpenAI's new networking protocol, MRC, aims to tackle the hidden bottleneck in AI training: networking. Developed with industry giants like AMD and NVIDIA, MRC promises to improve GPU networking performance and resilience in large training clusters. Training frontier AI models is not just a compute problem — it's increasingly a networking challenge. OpenAI's introduction of the Multipath Reliable Connection (MRC) protocol addresses this issue head-on. Developed over two years with partners like AMD, Broadcom, Intel, Microsoft, and NVIDIA, MRC is now available through the Open Compute Project. The protocol extends RDMA over Converged Ethernet, aiming to reduce network congestion and failures that can cause costly GPU idle time during model training. With over 900 million weekly users of ChatGPT, OpenAI emphasizes the importance of predictable network performance to sustain and improve AI models at scale. MRC's release highlights the growing significance of networking in AI infrastructure, as hyperscalers scale to hundreds of thousands of GPUs. By making MRC available to the broader industry, OpenAI and its partners are paving the way for more efficient and reliable AI training environments. This development not only benefits OpenAI's operations but also sets a new standard for the industry, enabling other organizations to build on this open protocol. As AI models continue to grow in complexity and scale, MRC represents a crucial step in overcoming the networking bottlenecks that have hindered progress in AI training.