Publish public benchmark data and paper figures
Browse filesAdds the three public split archives, canonical sample shards, a Viewer-ready sample, paper figures, and a complete dataset card.
- CITATION.cff +46 -0
- LICENSE +17 -0
- README.md +140 -21
- assets/benchmark-framework.png +3 -0
- assets/query-time-admissibility.png +3 -0
- assets/two-view-evaluation.png +3 -0
- data/SHA256SUMS +12 -0
- data/full/audit_subset_shards.tar.gz +3 -0
- data/full/public_dev_shards.tar.gz +3 -0
- data/full/public_test_shards.tar.gz +3 -0
- data/sample/manifest.json +29 -0
- data/sample/shards/coding_agent.json +0 -0
- data/sample/shards/customer_support.json +0 -0
- data/sample/shards/devops_workflow.json +0 -0
- data/sample/shards/education_tutoring.json +0 -0
- data/sample/shards/multi_party_collaboration.json +0 -0
- data/sample/shards/office_collaboration.json +0 -0
- data/sample/shards/personal_assistant.json +0 -0
- data/sample/shards/research_assistant.json +0 -0
- data/sample_cases.jsonl +0 -0
CITATION.cff
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cff-version: 1.2.0
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message: "If you use AutoMemoryBench, please cite the paper below."
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title: "AutoMemoryBench: State-Contract Evaluation for Auditable Agent Memory"
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type: software
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authors:
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- family-names: Wu
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given-names: Jiajun
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affiliation: Beihang University
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- family-names: Yang
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given-names: Jian
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affiliation: Beihang University
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- family-names: Li
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given-names: Chen
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- family-names: Chai
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given-names: Linzheng
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- family-names: Gao
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given-names: Ge
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- family-names: Shi
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given-names: Ensheng
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affiliation: Huawei Cloud Computing Technologies Co., Ltd.
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- family-names: Ma
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given-names: Yuchi
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affiliation: Huawei Cloud Computing Technologies Co., Ltd.
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repository-code: "https://github.com/wuyuVerse/AutoMemoryBench"
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license: Apache-2.0
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date-released: 2026-07-30
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preferred-citation:
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type: generic
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title: "AutoMemoryBench: State-Contract Evaluation for Auditable Agent Memory"
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authors:
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- family-names: Wu
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given-names: Jiajun
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- family-names: Yang
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given-names: Jian
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- family-names: Li
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given-names: Chen
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- family-names: Chai
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given-names: Linzheng
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- family-names: Gao
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given-names: Ge
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- family-names: Shi
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given-names: Ensheng
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- family-names: Ma
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given-names: Yuchi
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year: 2027
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url: "https://github.com/wuyuVerse/AutoMemoryBench"
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LICENSE
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Apache License
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Version 2.0, January 2004
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http://www.apache.org/licenses/
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Copyright 2026 AutoMemoryBench Contributors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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README.md
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language:
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- en
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pretty_name: AutoMemoryBench
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tags:
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- benchmark
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- agent-memory
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- memory-systems
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- llm-agents
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- evaluation
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---
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# AutoMemoryBench
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-
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-
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## Paper
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-
- [
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- [arXiv source package](AutoMemoryBench-arXiv-source.tar.gz)
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-
- [
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## Authors
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Jiajun Wu and Jian Yang are affiliated with Beihang University. Ensheng Shi and
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Yuchi Ma are affiliated with Huawei Cloud Computing Technologies Co., Ltd.
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-
## Abstract
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-
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A deployed agent fails not only when it forgets, but when it uses memory that is
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stale, deleted, unauthorized, cross-namespace, or unsupported by current tool
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state. AutoMemoryBench introduces query-time memory admissibility as an
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executable evaluation contract. Its primary metric, StrictCore, counts a query
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only when the task is solved, required evidence is used, and no prohibited
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memory influences the output. The release spans eight domains with 7,200 case
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variants and 151,200 probes, paired with minimal single-axis interventions.
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-
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## Citation
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```bibtex
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}
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```
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The formal Hugging Face Paper page and arXiv link will be added after the paper
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receives an arXiv identifier.
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-
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## License
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The code and benchmark data are released under Apache-2.0. The paper license
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language:
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- en
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pretty_name: AutoMemoryBench
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size_categories:
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- 1K<n<10K
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task_categories:
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- question-answering
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- text-retrieval
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tags:
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- benchmark
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- agent-memory
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- memory-systems
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- llm-agents
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- evaluation
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- text
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configs:
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- config_name: sample
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data_files:
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- split: audit_subset
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path: data/sample_cases.jsonl
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---
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<div align="center">
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# AutoMemoryBench
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### State-Contract Evaluation for Auditable Agent Memory
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[](AutoMemoryBench-arXiv.pdf)
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[](AutoMemoryBench-arXiv-source.tar.gz)
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[](https://github.com/wuyuVerse/AutoMemoryBench)
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[](https://github.com/wuyuVerse/AutoMemoryBench/blob/main/LICENSE)
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</div>
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AutoMemoryBench evaluates whether an agent uses the *right* memory, and only
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the admissible memory, under a query-time state contract. Each executable
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contract partitions memory into **required**, **admissible**, and
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**prohibited** sets. Prohibited memories are typed as superseded, deleted,
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restricted, cross-namespace, or stale-tool.
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<p align="center">
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<img src="assets/query-time-admissibility.png" alt="Query-time memory admissibility" width="760">
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</p>
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<p align="center"><em>Relevance is not enough: remembered evidence must also be allowed now.</em></p>
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## Benchmark at a Glance
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| Property | Value |
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| --- | ---: |
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| Domains | 8 |
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| Case variants | 7,200 |
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| Total probes | 151,200 |
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| Public probes | 120,960 |
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| Withheld hidden-test probes | 30,240 |
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| Probe types | 20 |
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| Memory systems evaluated | 18 |
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| Agent/CLI controls | 12 |
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| Backbones | Up to 8 |
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The eight domains cover personal assistance, education and tutoring, customer
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support, DevOps workflows, research assistance, coding agents, multi-party
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collaboration, and office collaboration. Minimal single-axis interventions
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change exactly one admissibility dimension while preserving the surrounding
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case.
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## Benchmark Design
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AutoMemoryBench compiles a chronological event graph into query-conditioned
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memory contracts. The same benchmark case is exposed through an interaction
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trace view for the system under test and a contract view used only by the
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evaluator. This separates task success from prohibited-memory influence without
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revealing the gold state to the tested system.
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<p align="center">
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<img src="assets/benchmark-framework.png" alt="AutoMemoryBench evaluation pipeline" width="100%">
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</p>
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<p align="center"><em>From event graphs to executable state contracts, paired interventions, and hard-gated scoring.</em></p>
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### One Case, Two Views
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<p align="center">
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<img src="assets/two-view-evaluation.png" alt="Interaction trace and compiled state-contract views" width="900">
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</p>
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<p align="center"><em>One benchmark case is evaluated through an interaction view and a hidden contract view.</em></p>
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The interaction view contains the user turns, memory operations, tool calls,
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tool results, and observed answer or action. The compiled contract view records
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what the current query must use, may use, and must not use under the active
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time, role, namespace, lifecycle, and tool state.
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## Public Data
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The `sample` configuration shown in the Dataset Viewer contains **40 cases and
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840 queries** across all eight domains. Each row is a complete benchmark case
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with its chronological events, sessions, memory units, query-time contracts,
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and probes. It is intended for schema inspection and smoke testing.
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The complete public release is hosted directly in this repository:
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| Split | Cases | Probes | Download |
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| --- | ---: | ---: | --- |
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| `audit_subset` | 720 | 15,120 | [archive](data/full/audit_subset_shards.tar.gz) |
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| `public_dev` | 1,440 | 30,240 | [archive](data/full/public_dev_shards.tar.gz) |
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| `public_test` | 3,600 | 75,600 | [archive](data/full/public_test_shards.tar.gz) |
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| **Public total** | **5,760** | **120,960** | [checksums](data/SHA256SUMS) |
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Each archive contains one canonical JSON shard per domain. The original sample
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manifest and domain shards are under [`data/sample`](data/sample). The same
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release is versioned with the evaluator in the
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[GitHub repository](https://github.com/wuyuVerse/AutoMemoryBench). The
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30,240-probe hidden-test split is withheld to prevent overfitting.
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```bash
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# Download the three public split archives from Hugging Face.
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hf download Multilingual-Multimodal-NLP/AutoMemoryBench \
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--repo-type dataset --include "data/full/*" --local-dir AutoMemoryBench-data
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```
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### Core Fields
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| Field | Description |
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| --- | --- |
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| `case_id` | Stable identifier for a benchmark case variant |
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| `domain` | Application domain of the interaction |
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| `events` | Chronological lifecycle, authorization, and tool events |
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| `sessions` | User and assistant interaction trace |
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| `gold_memory_units` | Auditable memory objects compiled from events |
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| `state_contracts` | Query-time admissibility partitions and transitions |
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| `queries` | Memory-required and diagnostic probes with scoring contracts |
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## Evaluation
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- **StrictCore** is the primary hard gate. A memory-required query counts only
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when the task is solved, required evidence is used, and no prohibited memory
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influences the output.
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- **PairAcc** measures whether both sides of a minimal intervention pair pass
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StrictCore.
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- **LeakRate** measures how often prohibited memory appears in an answer or
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trace.
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- **AMQ** is a diagnostic composite across writing, retrieval, update,
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compression, task utility, safety, and efficiency; it is not the headline
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metric.
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The canonical evaluator is implemented in
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[`amb/benchmark/evaluation`](https://github.com/wuyuVerse/AutoMemoryBench/tree/main/amb/benchmark/evaluation)
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and
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[`amb/benchmark/metrics`](https://github.com/wuyuVerse/AutoMemoryBench/tree/main/amb/benchmark/metrics).
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## Paper
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- [AutoMemoryBench paper PDF](AutoMemoryBench-arXiv.pdf)
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- [Submission-ready arXiv source package](AutoMemoryBench-arXiv-source.tar.gz)
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- [SHA256 checksums](SHA256SUMS)
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The formal Hugging Face Paper page and arXiv tag will be added after the paper
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receives an arXiv identifier.
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## Authors
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| 161 |
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Jiajun Wu and Jian Yang are affiliated with Beihang University. Ensheng Shi and
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Yuchi Ma are affiliated with Huawei Cloud Computing Technologies Co., Ltd.
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## Citation
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```bibtex
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}
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```
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## License
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The code and benchmark data are released under Apache-2.0. The paper license
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assets/benchmark-framework.png
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Git LFS Details
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assets/query-time-admissibility.png
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Git LFS Details
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assets/two-view-evaluation.png
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Git LFS Details
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data/SHA256SUMS
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537359836989153a447ce3b2832500468b43173fb1f16735ae17f005d3322eff full/audit_subset_shards.tar.gz
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89a7dc5e54b231ee82068d33390a83a0c08791abd76a8cf8dc2d56a0bbb98706 full/public_dev_shards.tar.gz
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d3e414d74cabf3db9aae8aa20c0ce72ccc7f290c9a3baa2701a2a8c88af90ea2 full/public_test_shards.tar.gz
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99ddca50079a061881c1d4d70fd7b0d1486426aba3db9d89781ca1599d5b3197 sample/manifest.json
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dd1d2c7359dfd6d258eefb3ddd17001470e71f41ea7642506cf2576c78d15a3d sample/shards/coding_agent.json
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ef8e9cc058bcf044ff19f4808fd58794e6c1ca641299115aa49c11c4917ac266 sample/shards/customer_support.json
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b8e935a2a2ebb64fd152bb824e7b51e901d6746ba3f12f1167bcfedff31eb471 sample/shards/devops_workflow.json
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+
803b27916b7078058f5481cd4a38207f067411daaee8d487dd5168b660b4649b sample/shards/education_tutoring.json
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d28b7dceae8637b6e6c01c9507183a8d014fc2cd08a2e7c177906ba2aace8f06 sample/shards/multi_party_collaboration.json
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2505ad80c5bc09ffd34984fede40e4789568d1563fa4b87c4ac7aa7deac105f6 sample/shards/office_collaboration.json
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5bff1007caa5c568bbd8966888d1bfadfe62ab6beadfe4794f8801bf4b55a375 sample/shards/personal_assistant.json
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d3c85c0e44b4b032121131fc5d93f225b6ec75068887a8c2eba037a7408b2076 sample/shards/research_assistant.json
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data/full/audit_subset_shards.tar.gz
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version https://git-lfs.github.com/spec/v1
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oid sha256:537359836989153a447ce3b2832500468b43173fb1f16735ae17f005d3322eff
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size 6353606
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data/full/public_dev_shards.tar.gz
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version https://git-lfs.github.com/spec/v1
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oid sha256:89a7dc5e54b231ee82068d33390a83a0c08791abd76a8cf8dc2d56a0bbb98706
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size 12620669
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data/full/public_test_shards.tar.gz
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version https://git-lfs.github.com/spec/v1
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oid sha256:d3e414d74cabf3db9aae8aa20c0ce72ccc7f290c9a3baa2701a2a8c88af90ea2
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size 30939355
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data/sample/manifest.json
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{
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"benchmark_id": "amst-sample-v1",
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"schema_version": "1.0.0",
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"package_type": "sample",
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"visibility": {
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"audit_subset": "public"
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},
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"included_splits": [
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"audit_subset"
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],
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"split_files": {
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"audit_subset": {
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"personal_assistant": "shards/personal_assistant.json",
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"education_tutoring": "shards/education_tutoring.json",
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"customer_support": "shards/customer_support.json",
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"devops_workflow": "shards/devops_workflow.json",
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"research_assistant": "shards/research_assistant.json",
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"coding_agent": "shards/coding_agent.json",
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"multi_party_collaboration": "shards/multi_party_collaboration.json",
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"office_collaboration": "shards/office_collaboration.json"
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}
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},
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"split_reports": {
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"audit_subset": {
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"num_cases": 40,
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"num_queries": 840
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}
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}
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}
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data/sample/shards/coding_agent.json
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data/sample/shards/customer_support.json
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data/sample/shards/devops_workflow.json
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data/sample/shards/education_tutoring.json
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data/sample/shards/multi_party_collaboration.json
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data/sample/shards/office_collaboration.json
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data/sample/shards/personal_assistant.json
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data/sample/shards/research_assistant.json
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data/sample_cases.jsonl
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