Using cheap ChatGPT clones, this paper cracks million-step reasoning with perfect accuracy - superintelligence via process, not power.
What if reliability didn’t depend on billion-param behemoths? A new paper proves you can chain a million reasoning steps with zero errors using older, cheaper models via agent voting on microtasks.[3]
Here’s the what: Instead of trusting one model, it decomposes epic tasks into tiny subtasks. Swarms of agents vote on solutions, ensuring quality through process. Even budget GPT variants nail it, hitting near-zero error rates on 5-hour+ benchmarks.[3]
Devs, this unlocks god-tier automation: think codebase-wide refactors, scientific sims, or legal doc analysis without hallucinations. Scales to infinite chains, flirting with superintelligence definitions.[3]
Context: Current frontier models hit 50% errors after hours; this sidesteps model limits entirely. Beats scaling hype - echoes open-source tricks like Llama with GQA, but for orchestration. Competitive edge over closed labs relying on raw compute.[1][3]
Action items: Grab the paper, spin up agents in CrewAI or Autogen with voting loops. Test on your thorniest task. Will swarms commoditize AGI, or is the real moat in the process? Your move.