license: mit
language:
- en
tags:
- benchmark
- agent-memory
- retrieval-augmented-generation
- llm
- evaluation
- memory
pretty_name: RAMR — Retrieval-Augmented Memory Reliability
size_categories:
- n<1K
RAMR — Retrieval-Augmented Memory Reliability
A contamination-resistant synthetic benchmark for agentic-RAG / memory systems. This dataset is the frozen fact-chain set used by the benchmark; the full harness, metrics, baselines and verification ledger live in the code repository.
- Code + metrics + runners: https://github.com/DanceNitra/ramr
- Citable archive (concept DOI, always latest): https://doi.org/10.5281/zenodo.20818291
What this is — and is not
A findings + method release, not a definitive large-scale leaderboard. Items are generated from random synthetic tokens, so a model cannot have memorized the answers — closed-book accuracy is ~0 by construction (contamination-resistant). The trade-off: it isolates retrieval/memory mechanisms, it does not predict real-corpus accuracy. Treat small-n magnitudes as directional; the orderings are the signal.
The dataset
ramr_chains_v0.1.0.jsonl — 300 synthetic 3-hop fact-chains (sha256-pinned in manifest.json). Each row:
| field | meaning |
|---|---|
id |
chain id |
question |
the 3-hop question (currency of the country where the company a person works at is HQ'd) |
gold_facts |
the complete chain (CONVERSION uses all) |
answer |
the gold answer token |
drop_index |
which hop to drop for the PARTIAL condition (CHAIN-FRAGILITY) |
distractor_pool |
fixed irrelevant facts; take first k for DISTRACTION |
The nine metrics (measured in the code repo)
CONVERSION (does complete retrieval convert to a correct answer), CHAIN-FRAGILITY (cost of one missing hop — near-total collapse across 7 models / 6 families), DISTRACTION (cost of noisy context — model-specific), FACT-RETENTION (compaction lossy under a fixed budget), OUTCOME-RANKED-RECALL (was-it-right vs was-it-recalled), FORGET-PRECISION (after a fact is updated, does recall return the current or the stale value), COMPRESSION-vs-RAW (does a compiled summary beat the raw noisy context, or only lose to it), OPERATIONAL-CONTINUITY (on resume after compaction, is an already-completed action re-executed), and TEMPORAL-AS-OF (out-of-order ingest: supersession resolves by validity-time, not arrival order). Plus auxiliary metrics in the repo (ABSTENTION / false-recall, CROSS-SCOPE-LEAKAGE).
Tooling + interoperability (v0.4)
- Folklore Meter (
ramr_folklore_meter.py) — a reusable probe that scores any AI-engineering folklore claim against a runnable test, returning a REAL / WEAK-MODEL-ARTIFACT / REGIME-SPECIFIC verdict. - RAMR↔LS interoperability — a shared, sha256-pinned evidence-fixture set (
ramr-ls-evidence-v0.1) spanning the four continuation verdicts, developed in collaboration with the LS project.
Cite
Agora (2026). RAMR — Retrieval-Augmented Memory Reliability. https://doi.org/10.5281/zenodo.20818291
MIT-licensed.