language:
- en
pretty_name: AutoMemoryBench
size_categories:
- 1K<n<10K
task_categories:
- question-answering
- text-retrieval
tags:
- benchmark
- agent-memory
- memory-systems
- llm-agents
- evaluation
- text
configs:
- config_name: sample
data_files:
- split: audit_subset
path: data/sample_cases.jsonl
AutoMemoryBench evaluates whether an agent uses the right memory, and only the admissible memory, under a query-time state contract. Each executable contract partitions memory into required, admissible, and prohibited sets. Prohibited memories are typed as superseded, deleted, restricted, cross-namespace, or stale-tool.
Relevance is not enough: remembered evidence must also be allowed now.
Benchmark at a Glance
| Property | Value |
|---|---|
| Domains | 8 |
| Case variants | 7,200 |
| Total probes | 151,200 |
| Public probes | 120,960 |
| Withheld hidden-test probes | 30,240 |
| Probe types | 20 |
| Memory systems evaluated | 18 |
| Agent/CLI controls | 12 |
| Backbones | Up to 8 |
The eight domains cover personal assistance, education and tutoring, customer support, DevOps workflows, research assistance, coding agents, multi-party collaboration, and office collaboration. Minimal single-axis interventions change exactly one admissibility dimension while preserving the surrounding case.
Benchmark Design
AutoMemoryBench compiles a chronological event graph into query-conditioned memory contracts. The same benchmark case is exposed through an interaction trace view for the system under test and a contract view used only by the evaluator. This separates task success from prohibited-memory influence without revealing the gold state to the tested system.
From event graphs to executable state contracts, paired interventions, and hard-gated scoring.
One Case, Two Views
One benchmark case is evaluated through an interaction view and a hidden contract view.
The interaction view contains the user turns, memory operations, tool calls, tool results, and observed answer or action. The compiled contract view records what the current query must use, may use, and must not use under the active time, role, namespace, lifecycle, and tool state.
Public Data
The sample configuration shown in the Dataset Viewer contains 40 cases and
840 queries across all eight domains. Each row is a complete benchmark case
with its chronological events, sessions, memory units, query-time contracts,
and probes. It is intended for schema inspection and smoke testing.
The complete public release is hosted directly in this repository:
| Split | Cases | Probes | Download |
|---|---|---|---|
audit_subset |
720 | 15,120 | archive |
public_dev |
1,440 | 30,240 | archive |
public_test |
3,600 | 75,600 | archive |
| Public total | 5,760 | 120,960 | checksums |
Each archive contains one canonical JSON shard per domain. The original sample
manifest and domain shards are under data/sample. The same
release is versioned with the evaluator in the
GitHub repository. The
30,240-probe hidden-test split is withheld to prevent overfitting.
# Download the three public split archives from Hugging Face.
hf download Multilingual-Multimodal-NLP/AutoMemoryBench \
--repo-type dataset --include "data/full/*" --local-dir AutoMemoryBench-data
Core Fields
| Field | Description |
|---|---|
case_id |
Stable identifier for a benchmark case variant |
domain |
Application domain of the interaction |
events |
Chronological lifecycle, authorization, and tool events |
sessions |
User and assistant interaction trace |
gold_memory_units |
Auditable memory objects compiled from events |
state_contracts |
Query-time admissibility partitions and transitions |
queries |
Memory-required and diagnostic probes with scoring contracts |
Evaluation
- StrictCore is the primary hard gate. A memory-required query counts only when the task is solved, required evidence is used, and no prohibited memory influences the output.
- PairAcc measures whether both sides of a minimal intervention pair pass StrictCore.
- LeakRate measures how often prohibited memory appears in an answer or trace.
- AMQ is a diagnostic composite across writing, retrieval, update, compression, task utility, safety, and efficiency; it is not the headline metric.
The canonical evaluator is implemented in
amb/benchmark/evaluation
and
amb/benchmark/metrics.
Paper
The formal Hugging Face Paper page and arXiv tag will be added after the paper receives an arXiv identifier.
Authors
Jiajun Wu, Jian Yang, Chen Li, Linzheng Chai, Ge Gao, Ensheng Shi, and Yuchi Ma.
Jiajun Wu and Jian Yang are affiliated with Beihang University. Ensheng Shi and Yuchi Ma are affiliated with Huawei Cloud Computing Technologies Co., Ltd.
Citation
@misc{automemorybench,
title = {AutoMemoryBench: State-Contract Evaluation for Auditable Agent Memory},
author = {Wu, Jiajun and Yang, Jian and Li, Chen and Chai, Linzheng and
Gao, Ge and Shi, Ensheng and Ma, Yuchi},
year = {2027},
url = {https://github.com/wuyuVerse/AutoMemoryBench}
}
License
The code and benchmark data are released under Apache-2.0. The paper license will follow the license selected for the arXiv submission.