Shattering the Illusion: Maker Achieves Million-Step, Zero-Error LLM Reasoning (www.cognizant.com)

Researchers at AI Lab and UT Austin introduced MAKER (Maximal Agentic decomposition, K-threshold Error mitigation, and Red-flagging), a system that completed a task requiring over one million LLM steps with zero errors by restructuring reasoning rather than enlarging models. MAKER breaks a long-horizon problem into atomic microtasks, runs many tiny agents in parallel on each step, and uses a lightweight voting and filtering protocol to accept actions only when a clear local consensus emerges. The team reports this approach eliminates context drift and prevents localized mistakes from compounding, overcoming the accuracy collapse seen in frontier reasoning models on complex, multi-step tasks. Technically, MAKER rests on three mechanisms: Maximal Agentic Decomposition (MAD) isolates single decisions to minimize context and error scope; first-to-ahead-by-k voting accepts the action that gets k more votes than alternatives, turning modest per-step accuracy gains into exponentially improving global reliability; and Red-Flagging detects structurally suspect outputs (e.g., excessive length or bad formatting) and resamples to reduce correlated failures. The authors derive formal scaling predictions: required votes grow only logarithmically with total steps while cost scales roughly linearly, versus exponential cost when agents cover multiple steps. Implication: carefully structured, massively parallel microagents plus simple local checks can enable practical, provably reliable long-horizon LLM reasoning without simply relying on ever-larger models.