recursive-decomposition

# Recursive Decomposition Long inputs rot. Details get missed, distant parts get glued together by guesswork, and the reasoning drifts. The RLM paper calls it context rot. Do not load the whole input into the window. Treat it as an environment you query: size it, narrow it, split it, hand independent parts to sub-agents, verify on a small window, synthesise in code. Based on [Recursive Language Models](https://arxiv.org/abs/2512.24601) (Zhang, Kraska, Khattab, 2025). ## How to use - `/recursive-decomposition`: apply the protocol below to the current task. - `/recursive-decomposition <path or question>`: size that input first, then run the protocol on it. ## When it applies Fit is not the test. Dense work can rot inside a million-token window. In the paper, OOLONG-Pairs is 32k tokens and GPT-5 scores 0.1% F1; RLM(depth=1) reaches 58.0% (arXiv:2512.24601, Table 1). 30k and 50k below are harness caps. | Situation | Approach | |-----------|----------| | One file, one function, or a single needle | Read directly | | Linear aggregate or list-everything, and completeness matters | Decompose (this skill) | | Pairwise, quadratic, or multi-hop across scattered sources | Decompose, even under 30k tokens | | 10+ files or 50k+ tokens | Decompose | | Under 30k tokens and a localised answer | Read directly | ## Protocol 1. **Size the input** before reading anything: count files (glob, `find`), lines (`wc -l`), bytes (`ls -lh`), pages for PDFs. 2. **Filter** the search space with sea...