Cost Of Remembering — Data skill for Claude Code

The Cost of Remembering

Everything behind the paper *The Cost of Remembering: Filesystem Memory Against Long Context on LongMemEval*: the harness, every run configuration, the raw token records, the memories the agents actually built, and the LaTeX source that turns them into tables and figures.

The problem it starts from is that the longer an assistant has known you, the more every question costs. Putting the whole history in the context window means re-reading the entire relationship to answer one thing, at full price, every time. This measures what a folder costs instead.

**The short version.** Give an agent an empty folder and a set of conventions for filing things in it. Have it read one conversation at a time and write notes. Later, ask it a question and let it walk the folders to answer. Compare that against putting the whole conversation history in the model's context window. On [LongMemEval](https://github.com/xiaowu0162/LongMemEval), the two are statistically indistinguishable on accuracy, and the folder reads **97% fewer tokens** and costs **95% less** per question.

No embedding model. No vector database. Markdown files in directories.

---

Why this repo exists

People have been giving language models a folder of markdown files for a while now and reporting that it works. `CLAUDE.md`, `AGENTS.md`, memory banks, Obsidian vaults, Karpathy's LLM wiki. What was missing was a number. This is the number, plus the harness that produced it, so you can disagree with it using data rather than intuition.

The conventions under test are [ICM](https://github.com/RinDig/icm-architect), which is one attempt to write down the layer these approaches have in common.

What is in here

lme-icm/          the harness
  lme/            library: ingestion, tools, agent loops, judging, cost
  run_cost.py     the driver for every run in the paper
  run_eval.py     the earlier read-only evaluation (phase 1)
  smoke_*.py      offline and mocked checks that cost noth