# Design Notes

## Purpose

Most memory benchmarks measure semantic recall in benign settings.

This benchmark targets retrieval failure modes that matter in agent memory systems:

- retrieving the wrong person
- retrieving the wrong environment
- retrieving an outdated fact instead of the current one
- retrieving something semantically close but operationally wrong

## Benchmark principles

- retrieval-focused, not generation-focused
- one query, many plausible distractors
- exact relevant entry ids are known in advance
- metadata such as tags, depth, speaker, and timestamp may be present but are optional
- cases should remain small enough to inspect by hand

## Scoring

Suggested scoring:

- `Recall@1`
- `Recall@5`
- `MRR`
- error bucket counts by `adversary_type`

## Expansion ideas

- more software-specific adversaries
- benchmark splits by domain
- fact-update and contradiction-specific suites
- Hugging Face dataset packaging