PAL-Bench: Evidence-Grounded Profile Reconstruction from Longitudinal Personal Albums
作者: Qiwei Yan, Zhiqiang Yuan, Zexi Jia, Nanxing Hu, Kailin Lyu, Jie Zhou, Jinchao Zhang
分类: cs.AI
发布日期: 2026-06-15
💡 一句话要点
提出PAL-Bench以解决个人相册的证据基础重建问题
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 个人相册 证据基础重建 多模态数据 隐私保护 社交身份绑定 基准测试 时间证据聚合
📋 核心要点
- 现有方法在个人相册的证据基础重建上面临挑战，缺乏有效的社交身份绑定和证据引用机制。
- 论文提出PAL-Bench，通过构建潜在私有世界和编程证据路径，提供了一种新的基准测试框架。
- 实验表明，尽管系统能够恢复部分所有者事实，但在重复身份和证据引用方面仍存在困难。
📝 摘要（中文）
长时间个人相册是弱模式多模态数据库，包含噪声感知记录，其关键事实需要跨越面孔、文本、时间戳、地点和重复事件进行关联。现有的视觉、视频和文档基准测试未能覆盖相册规模的社交身份绑定和证据引用的重建任务。为此，本文提出PAL-Bench，一个在公共记录合同下的受控基准，旨在支持证据基础的重建。其证据编译器构建潜在的私有世界，编程目标级证据路径，渲染相册像素，并通过感知管道重新测量，最终导出审计的公共/私有视图。实验结果显示，PAL-Bench在多个系统和诊断下揭示了可行的个人资料总结与忠实社交重建之间的差距。
🔬 方法详解
问题定义：本文旨在解决个人相册的证据基础重建问题，现有方法在社交身份绑定和证据引用方面存在不足，难以有效评估。
核心思路：PAL-Bench通过构建潜在的私有世界和编程目标级证据路径，提供了一种新的方法来处理多模态数据的集成与重建。
技术框架：整体架构包括证据编译器、感知管道和审计模块，系统通过这些模块生成和处理公共与私有记录。
关键创新：PAL-Bench的创新在于其证据编译器能够在保护隐私的前提下，构建与真实私人相册相匹配的证据结构，解决了现有方法无法安全发布的隐私问题。
关键设计：设计中采用了隐私保护审计机制，确保参与者只能接触到感知衍生的公共记录，目标、标识符映射和证据路径保持隐藏。
🖼️ 关键图片
📊 实验亮点
实验结果显示，PAL-Bench在七个系统和两个计算匹配的诊断中，采用七项指标协议揭示了可行的个人资料总结与忠实社交重建之间的差距。尽管系统能够恢复部分所有者事实，但在处理重复身份和证据引用方面仍存在显著挑战。
🎯 应用场景
PAL-Bench的研究成果可广泛应用于社交媒体分析、个人数据管理和隐私保护等领域。其提供的基准测试框架为未来的多模态数据集成和时间证据聚合提供了重要的参考，具有潜在的商业价值和社会影响。
📄 摘要（原文）
Longitudinal personal albums are weak-schema multimodal databases: noisy perceptual records whose key facts require joins across faces, text, timestamps, locations, and repeated events. Existing visual, video, document, and lifelog benchmarks test sub-problems, but not album-scale profile reconstruction with social identity binding and evidence citation. Benchmarking this task is difficult because the ground truth needed for evaluation--owner profiles, social graphs, face-name maps, and evidence provenance--is private state that real albums cannot safely release. We introduce PAL-Bench, a controlled benchmark for evidence-grounded reconstruction under a public-record contract. Its Evidence Compiler builds latent private worlds, programs target-level evidence paths, renders album pixels, re-measures them through perception pipelines, and exports audited public/private views. Agents receive only perception-derived public records; targets, identifier maps, and evidence paths remain hidden. PAL-Bench contains 50 synthetic users, 36,659 public photo records, and 2,799 targets over owner facts, identities, and relations. A privacy-preserving audit with 10 participants confirms that PAL-Bench evidence structures match real private albums, though equivalent releases remain privacy-prohibitive. Across seven systems and two compute-matched diagnostics, a seven-metric protocol reveals a gap between plausible profile summarization and faithful social reconstruction: systems recover some owner facts but struggle with recurring identities and evidence citation. PAL-TRACE, a reference framework that freezes identity bindings before owner-fact mining, performs best but leaves hard identity resolution far from solved. PAL-Bench provides a testbed for perceptual entity resolution, multimodal data integration, temporal evidence aggregation, and provenance-aware structured prediction.