Datasets:
| # 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| - retrieving an outdated fact instead of the current one | |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 | |