# Maker Mdap

Knowledge base from \"Solving a Million-Step LLM Task with Zero Errors\" (Meyerson...

- Repository: https://github.com/coco-research/coco (797 stars)
- Category: Design
- License: see repository
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add coco-research/coco --skill maker-mdap -g -a claude-code -y`
Codex: `npx skills add coco-research/coco --skill maker-mdap -g -a codex -y`
Cursor: `npx skills add coco-research/coco --skill maker-mdap -g -a cursor -y`
Gemini CLI: `npx skills add coco-research/coco --skill maker-mdap -g -a gemini-cli -y`

## Description

Knowledge base from \"Solving a Million-Step LLM Task with Zero Errors\" (Meyerson, Paolo, Dailey, Shahrzad, Francon, Hayes, Qiu, Hodjat, Miikkulainen, arXiv:2511.09030). Use when designing long-horizon, multi-step agentic LLM pipelines that need very high reliability, applying Maximal Agentic Decomposition (MAD), first-to-ahead-by-k voting, or red-flagging error correction, estimating cost/reliability scaling laws for multi-agent systems, or studying Massively Decomposed Agentic Processes (MDAPs).

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

<!-- argument-hint: [topic, framework name, or chapter number] -->

# Solving a Million-Step LLM Task with Zero Errors
**Authors**: Elliot Meyerson, Giuseppe Paolo, Roberto Dailey, Hormoz Shahrzad, Olivier Francon,
Conor F. Hayes, Xin Qiu, Babak Hodjat, Risto Miikkulainen (Cognizant AI Lab / UT Austin)
**Source**: [arXiv:2511.09030](https://arxiv.org/abs/2511.09030) (Nov 2025) ([Notice](NOTICE.md)) | **Pages**: ~29 | **Chapters**: 7 | **Generated**: 2026-08-26

## How to Use This Skill

- **Without arguments**, load core frameworks below for reference
- **With a topic**, ask about `voting`, `red-flagging`, `cost scaling`, `decomposition`, or
  another indexed topic; I find and read the relevant chapter
- **With a chapter**, ask for `ch03`; I load that specific chapter
- **Browse**, ask "what chapters do you have?" to see the full index

When you ask about a topic not covered in Core Frameworks below, I will read the relevant
chapter file before answering.

---

## Core Frameworks & Mental Models

**The problem**: LLMs have a persistent per-step error rate. A 1%-per-step error rate is
expected to fail by step ~100. Standard benchmarks (independent examples, averaged accuracy)
hide this, they don't expose what happens once you chain thousands or millions of *dependent*
steps. This paper solves a task requiring over **one million** dependent LLM steps with **zero
errors**, and argues the fix is architectural, not "wait for a smarter model."

**MDAP (Massively Decomposed Agentic Process)**, the general paradigm: decompose a task into
the smallest possible subtasks, then apply subtask-level error correction. **MAKER** is this
paper's concrete implementation: **M**aximal **A**gentic decomposition + first-to-ahead-by-**K**
**E**rror correction + **R**ed-flagging.

The rest: https://github.com/coco-research/coco/blob/HEAD/skills/maker-mdap/SKILL.md