Maker Mdap
Knowledge base from \"Solving a Million-Step LLM Task with Zero Errors\" (Meyerson...
Install
npx skills add coco-research/coco --skill maker-mdap -g -a claude-code -y- Repository
- coco-research/coco797
- Category
- Design
- Updated
- 3d ago
- License
- See repository
- Context cost
- Mediumabout 1,748 tokens
- Runs scripts
- No, instructions only
What it does
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).
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 (Nov 2025) (Notice) | 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: Maximal Agentic decomposition + first-to-ahead-by-K Error correction + Red-flagging.
