| --- |
| pretty_name: FalseMemBench |
| license: mit |
| task_categories: |
| - text-retrieval |
| language: |
| - en |
| tags: |
| - retrieval |
| - memory |
| - llm-agents |
| - adversarial |
| size_categories: |
| - n<1K |
| --- |
| |
| # FalseMemBench |
|
|
| `FalseMemBench` is a benchmark project for evaluating memory retrieval systems under adversarial distractors. |
|
|
| The goal is to measure whether a system can retrieve the right memory when many nearby but wrong memories are present. |
|
|
| ## Focus |
|
|
| The benchmark is designed for memory systems used by LLM agents. |
|
|
| It emphasizes: |
|
|
| - entity confusion |
| - environment confusion |
| - time/version confusion |
| - stale facts vs current facts |
| - speaker confusion |
| - near-duplicate paraphrases |
|
|
| ## Layout |
|
|
| - `schema/case.schema.json`: benchmark case schema |
| - `data/cases.jsonl`: current benchmark cases |
| - `docs/`: benchmark design notes |
| - `scripts/validate.py`: schema validator for the JSONL dataset |
| - `scripts/run_benchmark.py`: simple keyword baseline |
| - `scripts/run_tagmem_benchmark.py`: run the benchmark against a real `tagmem` binary |
|
|
| ## Case format |
|
|
| Each case contains: |
|
|
| - a `query` |
| - a set of `entries` |
| - one or more `relevant_ids` |
| - a single `adversary_type` |
| - optional metadata for analysis |
|
|
| ## Example |
|
|
| ```json |
| { |
| "id": "env-001", |
| "query": "What database does staging use?", |
| "adversary_type": "environment_swap", |
| "entries": [ |
| { |
| "id": "e1", |
| "text": "The staging environment uses db-staging.internal.", |
| "tags": ["staging", "database", "infra"], |
| "depth": 1 |
| }, |
| { |
| "id": "e2", |
| "text": "The production environment uses db-prod.internal.", |
| "tags": ["production", "database", "infra"], |
| "depth": 1 |
| } |
| ], |
| "relevant_ids": ["e1"] |
| } |
| ``` |
|
|
| ## Current adversary types |
|
|
| - `entity_swap` |
| - `environment_swap` |
| - `time_swap` |
| - `state_update` |
| - `speaker_swap` |
| - `near_duplicate_paraphrase` |
|
|
| Current dataset size: |
|
|
| - `573` cases |
|
|
| ## Intended use |
|
|
| The benchmark is intended to be: |
|
|
| - model-agnostic |
| - storage-agnostic |
| - metadata-friendly |
| - easy to publish to GitHub and Hugging Face |
|
|
