Datasets:
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25be136 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | # 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- retrieving an outdated fact instead of the current one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
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