Datasets:

Kkryptonite
/

CUE-Mem

---
pretty_name: CUE-Mem Benchmark
license: mit
tags:
  - multimodal
  - conversational-ai
  - memory
  - benchmark
  - personalization
  - arxiv:2609.32574
  - text
  - image
  - audio
language:
  - zh
  - en
task_categories:
  - question-answering
  - image-text-to-text
size_categories:
  - 1K<n<10K
---
<div align="center">

# 🧠 CUE-Mem

### Benchmarking Long-Term User Memory via Implicit Cues in Multimodal Conversations

**多模态对话隐式线索驱动的长期用户记忆评测基准**

[![Paper](https://img.shields.io/badge/arXiv-2609.32574-B31B1B)](https://arxiv.org/abs/2609.32574)
[![Code](https://img.shields.io/badge/GitHub-CUE--MEM-181717?logo=github)](https://github.com/yulinlp/CUE-MEM)
[![Demo](https://img.shields.io/badge/Online_Demo-Explore-2563EB)](https://reichenbach1854-hash.github.io/CUE-Mem/)

**Text · Image · Audio | Explicit & Implicit Cues | Long-Term User Memory**

[English](#english) · [中文说明](#中文说明) · [Quick Start / 快速开始](#quick-start) · [Citation / 引用](#citation)

</div>

![Explicit and implicit user memory across multimodal conversations / 多模态对话中的显式与隐式用户记忆](figure/intro.png)

*From visible routines to subtle clues: a cat bowl, a cat tree, and a background meow jointly suggest that the user has a cat.*
*从日常活动到隐式线索：猫碗、猫爬架与背景中的猫叫声，共同指向用户养猫这一信息。*

---

## English

### Overview

What should an agent remember when a user never says it directly? A recurring object in a photograph or a background sound in a voice message may carry information that matters in a later conversation.

**CUE-Mem** evaluates long-term user memory across text, images, and audio, covering both explicit statements and implicit cues. See the [paper](https://arxiv.org/abs/2609.32574) for benchmark design and evaluation protocols.

This repository distributes **conversation histories, questions, media, and metadata**. Construction and evaluation code lives on [GitHub](https://github.com/yulinlp/CUE-MEM); the [interactive demo](https://reichenbach1854-hash.github.io/CUE-Mem/) presents selected examples.

### Dataset at a glance

Counts below are computed from this release's metadata. A conversation record means one row in `dialogue_turns.jsonl`, not an individual speaker message. Sessions are counted using profile and session ID together.

| Component | Count |
| :--- | ---: |
| User profiles | 20 |
| Conversation sessions | 648 |
| Conversation records | 5,798 |
| Evaluation questions | 2,674 |
| Images | 4,876 |
| Audio files | 2,228 |
| Total media files | 7,104 |

Dialogue and question text is primarily Chinese; some descriptive fields are in English. Bilingual documentation does not imply parallel translations of every example.

<p align="center">
  <a href="figure/dataset_statistics.pdf"><img src="figure/dataset_statistics.png" alt="Question distribution by task, modality, and evidence type / 按任务、模态与证据类型划分的问题分布" width="640"></a>
</p>

*Question distribution in the paper. T / I / A denote text, image, and audio; Exp. / Imp. denote explicit and implicit evidence.*
*论文中的问题分布：T / I / A 分别表示文本、图像和音频，Exp. / Imp. 分别表示显式和隐式证据。[View PDF / 查看原始 PDF](figure/dataset_statistics.pdf).*

### Tasks

The release's `point` field distinguishes question categories. Image-based questions can supply visual answer options through `option_images`.

| Task | Focus | `point` values | Questions |
| :--- | :--- | :--- | ---: |
| Entity Recall | User-related entities and attributes | `entity_text`, `entity_img` | 1,128 |
| Long Pattern | Persistent preferences and habits | `pref_text`, `pref_img` | 363 |
| Personalized Recommendation | Apply user memory to recommendations | `rec_text`, `rec_img` | 364 |
| Answer Refusal | Insufficient evidence or misleading premises | `adversarial_text` | 819 |

[![Examples of the four memory tasks / 四类记忆任务示例](figure/task_example.png)](figure/task_example.pdf)

*Four tasks, illustrated with questions, answer choices, and supporting memory clues. Open the PDF to inspect the details.*
*四类任务的具体示例，包括问题、候选答案与记忆线索。[View PDF / 查看原始 PDF](figure/task_example.pdf).*

**Label detail:** `qa_type` is heterogeneous: some values are `explicit` / `implicit`, others are entity categories (`Relationship`, `Pets`, `Items`), and a few are empty. Do not use it as a universal binary evidence label. Preserve optional fields such as `entity_explicitness` and follow the evaluation code's interpretation.

### Construction Pipeline / 数据构建流程

![Benchmark construction: script synthesis, multimodal dialogue synthesis, and data review / 基准构建：脚本合成、多模态对话合成与数据审核](figure/benchmark.png)

*Construction pipeline: profile and event planning → multimodal dialogue synthesis → automatic checks and human review.*
*构建流程：用户档案与事件规划 → 多模态对话合成 → 自动检查与人工审核。*

### Files and annotations

Files are published directly at the repository root, without an extra `CUE-Mem-Benchmark/` wrapper or archive extraction step. See the shared directory tree below.

Each `history_with_qa_p*.json` contains `character_profile`, `multi_session_dialogues`, and `human-annotated QAs`. The flattened JSONL files are convenient entry points for analysis.

| Metadata file | Contents | Useful fields |
| :--- | :--- | :--- |
| `dialogue_turns.jsonl` | One conversation record per line | `p_id`, `session_id`, `round`, `date`, `task_id`, `user`, `assistant`, `input_image`, `input_voice_message` |
| `questions.jsonl` | One question per line | `qa_id`, `p_id`, `question`, `answer`, `point`, `qa_type`, `clue`, `option_images`, `source_file` |
| `media_manifest.jsonl` | Media inventory | `path`, `media_type`, `format`, `size_bytes` |
| `checksums.sha256` | SHA-256 checksums for `data/` files | Hash and relative file path |

Fields vary by modality and task. Audio-only turns may have no `user` text field. Captions are available in `image_caption`, `voice_caption`, and `user_voice_message_caption` where present.

Media paths, `source_file`, and manifest `path` use `/` separators and are relative to the dataset root. However, `clue` can contain evidence identifiers such as `D09:02` or `D09-001.wav`, which are not necessarily repository paths. Include `p_id` when joining session or turn references across profiles.

### Evaluation notes

Keep reference answers, rationales, and evidence annotations outside the evaluated system's memory unless the protocol explicitly provides them, such as an oracle-evidence baseline. State whether the system receives raw media, captions, or both. Preserve profile/session boundaries and conversation order; report the dataset revision, question selection, model, and memory configuration.

Generated scenarios and profile media should not be interpreted as verified observations about real people. Consult the [paper](https://arxiv.org/abs/2609.32574) for construction details and limitations, and the [GitHub README](https://github.com/yulinlp/CUE-MEM#readme) for environment setup, dataset preparation, and RQ1–RQ3 evaluation instructions.

---

## 中文说明

### 基准简介

用户没有直接说出口的信息，智能体能否在之后的对话中记住并使用？照片中反复出现的物品、语音里的环境声音，都可能是理解用户的重要线索。

**CUE-Mem** 面向文本、图像和音频交织的长期对话，评测记忆系统对显式信息与隐式线索的保留、检索和利用能力。基准设计与评测协议见[论文](https://arxiv.org/abs/2609.32574)。

本仓库提供**对话历史、问答、媒体文件及元数据**；构建与评测代码位于 [GitHub](https://github.com/yulinlp/CUE-MEM)，可通过[在线 Demo](https://reichenbach1854-hash.github.io/CUE-Mem/)浏览精选示例。

### 数据规模与任务

当前发布数据包含 **20 个用户档案、648 个会话、5,798 条对话记录、2,674 道问题**，以及 **4,876 张图像、2,228 个音频文件**。会话按用户和会话 ID 联合计数；对话记录数对应 `dialogue_turns.jsonl` 的行数，并非将双方消息分别计数。

| 任务 | 评测目标 | `point` 字段 | 题数 |
| :--- | :--- | :--- | ---: |
| 实体回忆（Entity Recall） | 记住与用户相关的实体及属性 | `entity_text`、`entity_img` | 1,128 |
| 长期模式（Long Pattern） | 从多次交互中归纳偏好和习惯 | `pref_text`、`pref_img` | 363 |
| 个性化推荐（Personalized Recommendation） | 将用户记忆用于新的推荐问题 | `rec_text`、`rec_img` | 364 |
| 拒答（Answer Refusal） | 识别证据不足或问题前提不可靠的情况 | `adversarial_text` | 819 |

对话和问题主要为中文，部分描述字段为英文；双语说明并不代表全部样本都有平行翻译。图像类问题可通过 `option_images` 提供图片选项。

### 文件与字段

仓库根目录直接包含 `README.md`、`LICENSE`、`data/` 和 `metadata/`，没有额外套一层目录，下载后无需拼接或解压分卷。完整目录树见下方。

`data/dialog/base/` 中每个用户对应一个 JSON，包含 `character_profile`（用户档案）、`multi_session_dialogues`（多会话对话）和 `human-annotated QAs`（问答）。`data/event/` 保存对话图像与音频，`data/qa/` 保存问题图片，`data/profile/generated_portraits/` 保存生成的用户／实体参考图像。

| 元数据文件 | 内容与读取要点 |
| :--- | :--- |
| `dialogue_turns.jsonl` | 每行一条对话记录；用 `p_id`、`session_id`、`round` 定位，媒体见 `input_image`、`input_voice_message` |
| `questions.jsonl` | 每行一道问题；包含问题、答案、任务类型、线索及部分题目的图片选项 |
| `media_manifest.jsonl` | 媒体清单：`path`、`media_type`、`format`、`size_bytes` |
| `checksums.sha256` | `data/` 文件的 SHA-256 校验值，用于检查下载完整性 |

不同模态和题型的字段不完全一致。音频轮次可能没有 `user` 文本字段；描述可见 `image_caption`、`voice_caption`、`user_voice_message_caption` 等可选字段。

读取时请注意：

- 媒体路径、`source_file` 和清单中的 `path` 相对于数据集根目录；`clue` 中的 `D09:02`、`D09-001.wav` 等可能是线索 ID，不能一律当作磁盘路径。跨用户关联时应同时使用 `p_id`。
- `qa_type` 并非统一的显式／隐式二分类：部分为 `explicit` 或 `implicit`，实体题也使用 `Relationship`、`Pets`、`Items`，另有少量空值。请结合 `entity_explicitness` 等字段及评测代码解释标签。

### 评测建议

请保持用户、会话边界和原始对话顺序，并记录数据版本、抽样方式、模型及记忆系统配置。除协议明确允许的情况（如 oracle-evidence 基线）外，不应将标准答案、解释或标注线索提前写入被评测系统的记忆。使用原始媒体、文本描述或二者组合时，也应在结果中说明。

生成场景及用户媒体不应被视为真实人物的已核实经历。构建方法和局限性见[论文](https://arxiv.org/abs/2609.32574)；环境配置、数据准备及 RQ1–RQ3 实验步骤见 [GitHub README](https://github.com/yulinlp/CUE-MEM#readme)。

---

## Repository Layout / 仓库结构

```text
.
├── README.md
├── LICENSE
├── data/
│   ├── dialog/base/                  # Profile histories + QA / 用户历史与问答
│   │   ├── history_with_qa_p0.json
│   │   └── ...
│   ├── event/images/                 # Event images / 对话事件图像
│   ├── event/voice_mixed/            # Event audio / 对话音频
│   ├── qa/pref_images/               # Preference / 偏好题图片
│   ├── qa/rec_images/                # Recommendation / 推荐题图片
│   ├── qa/entity_images/             # Entity / 实体题图片
│   └── profile/generated_portraits/  # Profile/entity references / 档案与实体参考图
└── metadata/
    ├── dialogue_turns.jsonl
    ├── questions.jsonl
    ├── media_manifest.jsonl
    └── checksums.sha256
```

<a id="quick-start"></a>

## Quick Start / 快速开始

### 1. Download / 下载

Download the full release with the Hugging Face CLI. / 使用 Hugging Face CLI 下载完整数据：

```bash
python -m pip install -U huggingface_hub
hf download Kkryptonite/CUE-Mem --repo-type dataset --local-dir benchmark-data
```

For a lightweight first look, download only metadata and profile JSON files. Download media before multimodal evaluation. / 若只想先查看结构，可仅下载元数据与用户 JSON；多模态评测前需补齐媒体：

```python
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="Kkryptonite/CUE-Mem",
    repo_type="dataset",
    local_dir="benchmark-data",
    allow_patterns=["README.md", "LICENSE", "metadata/*", "data/dialog/base/*.json"],
)
```

For reproducibility, set `revision` to a commit hash. See the [download guide](https://huggingface.co/docs/huggingface_hub/guides/download). / 固定版本时可设置 `revision` 为 commit hash，详见[下载说明](https://huggingface.co/docs/huggingface_hub/guides/download)。

### 2. Read records / 读取数据

```python
import json
from pathlib import Path
root = Path("benchmark-data")
def read_jsonl(relative_path):
    with (root / relative_path).open(encoding="utf-8") as handle:
        for line in handle:
            if line.strip():
                yield json.loads(line)
question = next(read_jsonl("metadata/questions.jsonl"))
print(question["qa_id"], question["question"])
for label, relative_path in question.get("option_images", {}).items():
    print(label, root / relative_path)
turn = next(read_jsonl("metadata/dialogue_turns.jsonl"))
print(turn["p_id"], turn["session_id"], turn["round"])
for field in ("input_image", "input_voice_message"):
    for relative_path in turn.get(field, []):
        print(field, root / relative_path)
```

### 3. Verify files / 校验完整性

Run after downloading the full `data/` directory. / 完整下载 `data/` 后执行：

```bash
cd benchmark-data
# Linux
sha256sum -c metadata/checksums.sha256
# macOS (alternative / 替代命令)
shasum -a 256 -c metadata/checksums.sha256
```

---

## License / 许可证

This repository includes an [MIT License](LICENSE). Refer to the file for the full terms. / 本仓库附有 [MIT 许可证](LICENSE)，完整条款以该文件为准。

## Citation

If you use CUE-Mem in your research, please cite the [paper](https://arxiv.org/abs/2609.32574). / 如在研究中使用 CUE-Mem，请引用以下论文：

```bibtex
@misc{hu2026cuemem,
  title={CUE-Mem: Benchmarking Long-Term User Memory via Implicit Cues in Multimodal Conversations},
  author={Yulin Hu and Yanyan Zhao and Zimo Long and Xing Fu and Mengtong Ji and Weixiang Zhao and Yutai Hou and Qianchao Wang and Dandan Tu},
  year={2026},
  eprint={2609.32574},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2609.32574}
}
```

Code and evaluation questions: [GitHub Issues](https://github.com/yulinlp/CUE-MEM/issues). Dataset questions: [Hugging Face Discussions](https://huggingface.co/datasets/Kkryptonite/CUE-Mem/discussions). / 代码与评测问题请提交至 [GitHub Issues](https://github.com/yulinlp/CUE-MEM/issues)，数据集问题可在 [Hugging Face Discussions](https://huggingface.co/datasets/Kkryptonite/CUE-Mem/discussions) 反馈。

	---
	pretty_name: CUE-Mem Benchmark
	license: mit
	tags:
	- multimodal
	- conversational-ai
	- memory
	- benchmark
	- personalization
	- arxiv:2609.32574
	- text
	- image
	- audio
	language:
	- zh
	- en
	task_categories:
	- question-answering
	- image-text-to-text
	size_categories:
	- 1K<n<10K
	---

	<div align="center">

	# 🧠 CUE-Mem

	### Benchmarking Long-Term User Memory via Implicit Cues in Multimodal Conversations

	多模态对话隐式线索驱动的长期用户记忆评测基准

	[![Paper](https://img.shields.io/badge/arXiv-2609.32574-B31B1B)](https://arxiv.org/abs/2609.32574)
	[![Code](https://img.shields.io/badge/GitHub-CUE--MEM-181717?logo=github)](https://github.com/yulinlp/CUE-MEM)
	[![Demo](https://img.shields.io/badge/Online_Demo-Explore-2563EB)](https://reichenbach1854-hash.github.io/CUE-Mem/)

	Text · Image · Audio \| Explicit & Implicit Cues \| Long-Term User Memory

	[English](#english) · [中文说明](#中文说明) · [Quick Start / 快速开始](#quick-start) · [Citation / 引用](#citation)

	</div>

	![Explicit and implicit user memory across multimodal conversations / 多模态对话中的显式与隐式用户记忆](figure/intro.png)

	From visible routines to subtle clues: a cat bowl, a cat tree, and a background meow jointly suggest that the user has a cat.
	从日常活动到隐式线索：猫碗、猫爬架与背景中的猫叫声，共同指向用户养猫这一信息。

	---

	## English

	### Overview

	What should an agent remember when a user never says it directly? A recurring object in a photograph or a background sound in a voice message may carry information that matters in a later conversation.

	CUE-Mem evaluates long-term user memory across text, images, and audio, covering both explicit statements and implicit cues. See the [paper](https://arxiv.org/abs/2609.32574) for benchmark design and evaluation protocols.

	This repository distributes conversation histories, questions, media, and metadata. Construction and evaluation code lives on [GitHub](https://github.com/yulinlp/CUE-MEM); the [interactive demo](https://reichenbach1854-hash.github.io/CUE-Mem/) presents selected examples.

	### Dataset at a glance

	Counts below are computed from this release's metadata. A conversation record means one row in `dialogue_turns.jsonl`, not an individual speaker message. Sessions are counted using profile and session ID together.

	\| Component \| Count \|
	\| :--- \| ---: \|
	\| User profiles \| 20 \|
	\| Conversation sessions \| 648 \|
	\| Conversation records \| 5,798 \|
	\| Evaluation questions \| 2,674 \|
	\| Images \| 4,876 \|
	\| Audio files \| 2,228 \|
	\| Total media files \| 7,104 \|

	Dialogue and question text is primarily Chinese; some descriptive fields are in English. Bilingual documentation does not imply parallel translations of every example.

	<p align="center">
	<a href="figure/dataset_statistics.pdf"><img src="figure/dataset_statistics.png" alt="Question distribution by task, modality, and evidence type / 按任务、模态与证据类型划分的问题分布" width="640"></a>
	</p>

	Question distribution in the paper. T / I / A denote text, image, and audio; Exp. / Imp. denote explicit and implicit evidence.
	论文中的问题分布：T / I / A 分别表示文本、图像和音频，Exp. / Imp. 分别表示显式和隐式证据。[View PDF / 查看原始 PDF](figure/dataset_statistics.pdf).

	### Tasks

	The release's `point` field distinguishes question categories. Image-based questions can supply visual answer options through `option_images`.

	\| Task \| Focus \| `point` values \| Questions \|
	\| :--- \| :--- \| :--- \| ---: \|
	\| Entity Recall \| User-related entities and attributes \| `entity_text`, `entity_img` \| 1,128 \|
	\| Long Pattern \| Persistent preferences and habits \| `pref_text`, `pref_img` \| 363 \|
	\| Personalized Recommendation \| Apply user memory to recommendations \| `rec_text`, `rec_img` \| 364 \|
	\| Answer Refusal \| Insufficient evidence or misleading premises \| `adversarial_text` \| 819 \|

	[![Examples of the four memory tasks / 四类记忆任务示例](figure/task_example.png)](figure/task_example.pdf)

	Four tasks, illustrated with questions, answer choices, and supporting memory clues. Open the PDF to inspect the details.
	四类任务的具体示例，包括问题、候选答案与记忆线索。[View PDF / 查看原始 PDF](figure/task_example.pdf).

	Label detail: `qa_type` is heterogeneous: some values are `explicit` / `implicit`, others are entity categories (`Relationship`, `Pets`, `Items`), and a few are empty. Do not use it as a universal binary evidence label. Preserve optional fields such as `entity_explicitness` and follow the evaluation code's interpretation.

	### Construction Pipeline / 数据构建流程

	![Benchmark construction: script synthesis, multimodal dialogue synthesis, and data review / 基准构建：脚本合成、多模态对话合成与数据审核](figure/benchmark.png)

	Construction pipeline: profile and event planning → multimodal dialogue synthesis → automatic checks and human review.
	构建流程：用户档案与事件规划 → 多模态对话合成 → 自动检查与人工审核。

	### Files and annotations

	Files are published directly at the repository root, without an extra `CUE-Mem-Benchmark/` wrapper or archive extraction step. See the shared directory tree below.

	Each `history_with_qa_p*.json` contains `character_profile`, `multi_session_dialogues`, and `human-annotated QAs`. The flattened JSONL files are convenient entry points for analysis.

	\| Metadata file \| Contents \| Useful fields \|
	\| :--- \| :--- \| :--- \|
	\| `dialogue_turns.jsonl` \| One conversation record per line \| `p_id`, `session_id`, `round`, `date`, `task_id`, `user`, `assistant`, `input_image`, `input_voice_message` \|
	\| `questions.jsonl` \| One question per line \| `qa_id`, `p_id`, `question`, `answer`, `point`, `qa_type`, `clue`, `option_images`, `source_file` \|
	\| `media_manifest.jsonl` \| Media inventory \| `path`, `media_type`, `format`, `size_bytes` \|
	\| `checksums.sha256` \| SHA-256 checksums for `data/` files \| Hash and relative file path \|

	Fields vary by modality and task. Audio-only turns may have no `user` text field. Captions are available in `image_caption`, `voice_caption`, and `user_voice_message_caption` where present.

	Media paths, `source_file`, and manifest `path` use `/` separators and are relative to the dataset root. However, `clue` can contain evidence identifiers such as `D09:02` or `D09-001.wav`, which are not necessarily repository paths. Include `p_id` when joining session or turn references across profiles.

	### Evaluation notes

	Keep reference answers, rationales, and evidence annotations outside the evaluated system's memory unless the protocol explicitly provides them, such as an oracle-evidence baseline. State whether the system receives raw media, captions, or both. Preserve profile/session boundaries and conversation order; report the dataset revision, question selection, model, and memory configuration.

	Generated scenarios and profile media should not be interpreted as verified observations about real people. Consult the [paper](https://arxiv.org/abs/2609.32574) for construction details and limitations, and the [GitHub README](https://github.com/yulinlp/CUE-MEM#readme) for environment setup, dataset preparation, and RQ1–RQ3 evaluation instructions.

	---

	## 中文说明

	### 基准简介

	用户没有直接说出口的信息，智能体能否在之后的对话中记住并使用？照片中反复出现的物品、语音里的环境声音，都可能是理解用户的重要线索。

	CUE-Mem 面向文本、图像和音频交织的长期对话，评测记忆系统对显式信息与隐式线索的保留、检索和利用能力。基准设计与评测协议见[论文](https://arxiv.org/abs/2609.32574)。

	本仓库提供对话历史、问答、媒体文件及元数据；构建与评测代码位于 [GitHub](https://github.com/yulinlp/CUE-MEM)，可通过[在线 Demo](https://reichenbach1854-hash.github.io/CUE-Mem/)浏览精选示例。

	### 数据规模与任务

	当前发布数据包含 20 个用户档案、648 个会话、5,798 条对话记录、2,674 道问题，以及 4,876 张图像、2,228 个音频文件。会话按用户和会话 ID 联合计数；对话记录数对应 `dialogue_turns.jsonl` 的行数，并非将双方消息分别计数。

	\| 任务 \| 评测目标 \| `point` 字段 \| 题数 \|
	\| :--- \| :--- \| :--- \| ---: \|
	\| 实体回忆（Entity Recall） \| 记住与用户相关的实体及属性 \| `entity_text`、`entity_img` \| 1,128 \|
	\| 长期模式（Long Pattern） \| 从多次交互中归纳偏好和习惯 \| `pref_text`、`pref_img` \| 363 \|
	\| 个性化推荐（Personalized Recommendation） \| 将用户记忆用于新的推荐问题 \| `rec_text`、`rec_img` \| 364 \|
	\| 拒答（Answer Refusal） \| 识别证据不足或问题前提不可靠的情况 \| `adversarial_text` \| 819 \|

	对话和问题主要为中文，部分描述字段为英文；双语说明并不代表全部样本都有平行翻译。图像类问题可通过 `option_images` 提供图片选项。

	### 文件与字段

	仓库根目录直接包含 `README.md`、`LICENSE`、`data/` 和 `metadata/`，没有额外套一层目录，下载后无需拼接或解压分卷。完整目录树见下方。

	`data/dialog/base/` 中每个用户对应一个 JSON，包含 `character_profile`（用户档案）、`multi_session_dialogues`（多会话对话）和 `human-annotated QAs`（问答）。`data/event/` 保存对话图像与音频，`data/qa/` 保存问题图片，`data/profile/generated_portraits/` 保存生成的用户／实体参考图像。

	\| 元数据文件 \| 内容与读取要点 \|
	\| :--- \| :--- \|
	\| `dialogue_turns.jsonl` \| 每行一条对话记录；用 `p_id`、`session_id`、`round` 定位，媒体见 `input_image`、`input_voice_message` \|
	\| `questions.jsonl` \| 每行一道问题；包含问题、答案、任务类型、线索及部分题目的图片选项 \|
	\| `media_manifest.jsonl` \| 媒体清单：`path`、`media_type`、`format`、`size_bytes` \|
	\| `checksums.sha256` \| `data/` 文件的 SHA-256 校验值，用于检查下载完整性 \|

	不同模态和题型的字段不完全一致。音频轮次可能没有 `user` 文本字段；描述可见 `image_caption`、`voice_caption`、`user_voice_message_caption` 等可选字段。

	读取时请注意：

	- 媒体路径、`source_file` 和清单中的 `path` 相对于数据集根目录；`clue` 中的 `D09:02`、`D09-001.wav` 等可能是线索 ID，不能一律当作磁盘路径。跨用户关联时应同时使用 `p_id`。
	- `qa_type` 并非统一的显式／隐式二分类：部分为 `explicit` 或 `implicit`，实体题也使用 `Relationship`、`Pets`、`Items`，另有少量空值。请结合 `entity_explicitness` 等字段及评测代码解释标签。

	### 评测建议

	请保持用户、会话边界和原始对话顺序，并记录数据版本、抽样方式、模型及记忆系统配置。除协议明确允许的情况（如 oracle-evidence 基线）外，不应将标准答案、解释或标注线索提前写入被评测系统的记忆。使用原始媒体、文本描述或二者组合时，也应在结果中说明。

	生成场景及用户媒体不应被视为真实人物的已核实经历。构建方法和局限性见[论文](https://arxiv.org/abs/2609.32574)；环境配置、数据准备及 RQ1–RQ3 实验步骤见 [GitHub README](https://github.com/yulinlp/CUE-MEM#readme)。

	---

	## Repository Layout / 仓库结构

	```text
	.
	├── README.md
	├── LICENSE
	├── data/
	│ ├── dialog/base/ # Profile histories + QA / 用户历史与问答
	│ │ ├── history_with_qa_p0.json
	│ │ └── ...
	│ ├── event/images/ # Event images / 对话事件图像
	│ ├── event/voice_mixed/ # Event audio / 对话音频
	│ ├── qa/pref_images/ # Preference / 偏好题图片
	│ ├── qa/rec_images/ # Recommendation / 推荐题图片
	│ ├── qa/entity_images/ # Entity / 实体题图片
	│ └── profile/generated_portraits/ # Profile/entity references / 档案与实体参考图
	└── metadata/
	├── dialogue_turns.jsonl
	├── questions.jsonl
	├── media_manifest.jsonl
	└── checksums.sha256
	```

	<a id="quick-start"></a>

	## Quick Start / 快速开始

	### 1. Download / 下载

	Download the full release with the Hugging Face CLI. / 使用 Hugging Face CLI 下载完整数据：

	```bash
	python -m pip install -U huggingface_hub
	hf download Kkryptonite/CUE-Mem --repo-type dataset --local-dir benchmark-data
	```

	For a lightweight first look, download only metadata and profile JSON files. Download media before multimodal evaluation. / 若只想先查看结构，可仅下载元数据与用户 JSON；多模态评测前需补齐媒体：

	```python
	from huggingface_hub import snapshot_download

	snapshot_download(
	repo_id="Kkryptonite/CUE-Mem",
	repo_type="dataset",
	local_dir="benchmark-data",
	allow_patterns=["README.md", "LICENSE", "metadata/", "data/dialog/base/.json"],
	)
	```

	For reproducibility, set `revision` to a commit hash. See the [download guide](https://huggingface.co/docs/huggingface_hub/guides/download). / 固定版本时可设置 `revision` 为 commit hash，详见[下载说明](https://huggingface.co/docs/huggingface_hub/guides/download)。

	### 2. Read records / 读取数据

	```python
	import json
	from pathlib import Path

	root = Path("benchmark-data")

	def read_jsonl(relative_path):
	with (root / relative_path).open(encoding="utf-8") as handle:
	for line in handle:
	if line.strip():
	yield json.loads(line)

	question = next(read_jsonl("metadata/questions.jsonl"))
	print(question["qa_id"], question["question"])
	for label, relative_path in question.get("option_images", {}).items():
	print(label, root / relative_path)

	turn = next(read_jsonl("metadata/dialogue_turns.jsonl"))
	print(turn["p_id"], turn["session_id"], turn["round"])
	for field in ("input_image", "input_voice_message"):
	for relative_path in turn.get(field, []):
	print(field, root / relative_path)
	```

	### 3. Verify files / 校验完整性

	Run after downloading the full `data/` directory. / 完整下载 `data/` 后执行：

	```bash
	cd benchmark-data

	# Linux
	sha256sum -c metadata/checksums.sha256

	# macOS (alternative / 替代命令)
	shasum -a 256 -c metadata/checksums.sha256
	```

	---

	## License / 许可证

	This repository includes an [MIT License](LICENSE). Refer to the file for the full terms. / 本仓库附有 [MIT 许可证](LICENSE)，完整条款以该文件为准。

	## Citation

	If you use CUE-Mem in your research, please cite the [paper](https://arxiv.org/abs/2609.32574). / 如在研究中使用 CUE-Mem，请引用以下论文：

	```bibtex
	@misc{hu2026cuemem,
	title={CUE-Mem: Benchmarking Long-Term User Memory via Implicit Cues in Multimodal Conversations},
	author={Yulin Hu and Yanyan Zhao and Zimo Long and Xing Fu and Mengtong Ji and Weixiang Zhao and Yutai Hou and Qianchao Wang and Dandan Tu},
	year={2026},
	eprint={2609.32574},
	archivePrefix={arXiv},
	primaryClass={cs.AI},
	url={https://arxiv.org/abs/2609.32574}
	}
	```

	Code and evaluation questions: [GitHub Issues](https://github.com/yulinlp/CUE-MEM/issues). Dataset questions: [Hugging Face Discussions](https://huggingface.co/datasets/Kkryptonite/CUE-Mem/discussions). / 代码与评测问题请提交至 [GitHub Issues](https://github.com/yulinlp/CUE-MEM/issues)，数据集问题可在 [Hugging Face Discussions](https://huggingface.co/datasets/Kkryptonite/CUE-Mem/discussions) 反馈。