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How AI Agents Are Solving Complicated Mathematical Problems Artificial intelligence is moving from answering questions to doing research. Multi agent systems can now explore conjectures, search proof strategies, write formal code, and verify results in theorem provers such as Lean. A recent high profile claim from OpenAI illustrates both the promise and the controversy around this shift. Generated by AI A Recent Milestone Claim: Navier–Stokes and 10,000 Agents In early September 2026, OpenAI announced that an internal, unreleased model had produced a proposed solution to the Navier–Stokes existence and smoothness problem, one of the seven Millennium Prize Problems. The company stated that about 10,000 AI agents worked on the problem in parallel for roughly 88 hours, exchanging information and refining candidate arguments. According to OpenAI, the proof shows that solutions to the three dimensional incompressible Navier–Stokes equations can develop a finite time singularity, meaning flu...
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How RAG Is Maturing: Choosing the Right Retrieval Pipeline for Your Use Case Retrieval-Augmented Generation (RAG) has moved far beyond the “experimental” phase. In the early days, RAG was simply about feeding a PDF into a vector database and asking a chatbot to summarize it. Today, the industry is shifting toward Compound AI Systems , where RAG is no longer a single step but a complex, multi-layered cognitive architecture. As we scale these systems for the enterprise, the “Naive” approach is failing. To solve for accuracy, latency, and complex reasoning, we are seeing the emergence of specialized RAG patterns. Here is the blueprint for how RAG is maturing and how to choose the right architecture for your pipeline. Press enter or click to view image in full size Generated by AI Why RAG Keeps Evolving Basic RAG splits documents into fixed chunks, embeds them, retrieves the top k by cosine similarity, and passes them to an LLM. This works for simple FAQs but breaks on exact identifiers, l...
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