If AI+People Covering The Weak Spots Can Solve Complex Math, We Can Solve AI

so, some of this was written with AI assistance, but the idea presented has legs. Extend a bit of trust here and read on. This isn’t some wild new idea, it’s what big labs are already doing that we can now try as well. Let’s look at what happened this year. A model pushed something on the Riemann Hypothesis from 41.6% to 67.2%, verified in the Lean proof assistant and reviewed by outside mathematicians, not just announced. A separate model resolved a 27-year-open question in group theory for something like $2,000 in compute. Neither of those happened because the AI decided on its own to work on math. A human picked the target. The AI did the rigorous, exhaustive, checkable work of chasing it down. That's the part I think people keep missing. We're not really waiting on AI to have ideas. We're waiting on someone to ask the right question. It's worth remembering how the Transformer itself actually happened, because I think it's the same shape. The core move in "Attention Is All You Need" (Vaswani et al., 2017) wasn't some black-box leap, it was one specific, statable hypothesis: recurrence isn't actually necessary for sequence modeling. The paper was the work of building and testing that hypothesis to its conclusion. A guess, sharp enough to state in one sentence, handed to a process built to work out the consequences. So why do we keep treating "what comes after the Transformer" like it needs some mystical spark? There's already a real, running version of exactly this process. A multi-agent system called AIRA-Compose (Pepe et al., 2026, arxiv.org/abs/2605.15871) searches combinations of attention, MLP, and Mamba components, tests candidates cheap and small, then scales up only the ones that hold. It's already found architectures — AIRAformer-D and AIRAhybrid-D — that beat Llama 3.2 by 2.4-3.8% on downstream tasks, with better compute-scaling curves than the baseline. That's a small, real result, not a projection. I want to be upfront about the limits, because I think they're the actually interesting part. Those wins are confirmed at proxy scale, not yet at frontier scale, and the same research groups running these pipelines report that most tested hypotheses come back as noise, not breakthroughs. That's not a failure of the method. That's what an honest filter looks like when it's working. If you want to actually try this, here's roughly the shape that's worked so far: Start the same way the math results did. Pick a specific, narrow, well-defined target, not "improve AI" but something falsifiable, like "does removing X change performance." State it as a clean assumption, the way "recurrence isn't necessary" was stated, not as an open-ended "come up with something new" prompt. Vague prompting is what produced most of the noise in these pipelines; specific hypotheses are what produced the actual wins. Let the model do the part it's demonstrably good at: assembling and recombining known components against that specific hypothesis, and being ruthless about checking whether the result is real. Don't take a single glowing self-report at face value; the Riemann and Astra results only mattered because they were checked by an outside proof assistant and independent reviewers, not because the model said it worked. Then test cheap before you test big. Train a small, fast baseline model on the standard architecture, train an identical small model with your proposed change, same data, same size, no other differences, and compare. This is exactly how AIRA-Compose and similar systems keep costs low, running proxy-scale comparisons before anyone commits real compute to scaling something up. If there's no real signal at small scale, that's usually your answer. If there is, that's your reason to consider scaling further, keeping in mind that a small-scale win doesn't guarantee it holds at frontier scale, so treat it as a strong lead worth pursuing, not a finished result. Worth noting this isn't limited to architecture. The same human-hypothesis, AI-execution, small-model-verification loop applies to other open problems in this space too. Long-horizon reliability, continual learning, self-improving training loops, all of these have the same shape: a specific, statable "what if we tried X instead of Y," tested cheap, scaled only if it earns it. If you've got a precise, testable "assume X isn't needed" for any of these, that's the actual bottleneck right now. I encourage all of you to spin up your best models and start testing methods for each of the biggest roadblocks in AI right now. The more people we have asking and testing, the faster we’ll get solutions. Remember, the AI doesn’t have to think for itself for now. You just need to guide it and cover for its weak points in long-term planning, extrapolating, logical leaps and asking the right questions. We don’t need to wait for the big companies to innovate, we just need to ask the right questions and all work together with centralized findings, we can move much faster.