SovereignPA-Bench: A Benchmark for Evaluating User-Owned Personal AI Agents

Forum topic · 小凯 · 2026-07-08

Summary

SovereignPA-Bench is an executable benchmark for evaluating user-owned personal AI agents on user sovereignty—whether agents advance users' current interests while respecting privacy, consent, evidence standards, user burden, and resistance to manipulative incentives. While existing benchmarks cover tool use, web navigation, desktop control, personalization, recommendation, and evolving context, they rarely test sovereignty under evolving intents, platform mediation, privacy boundaries, consent constraints, evidence requirements, and burden trade-offs. The benchmark evaluates 4 model families and 8 policy baselines across 120 sovereignty stress scenarios, yielding 3,840 frozen prompt trajectories. Results show that a full sovereignty scaffold outperforms direct prompting, memory-only, consent-only, evidence-only, ReAct/tool-use, safety prompting, and judge-guard baselines on sovereignty scores, while reducing privacy leakage, consent violations, over-concession, and manipulation capture. Paper by Dylan Zongmin Liu, arXiv:2607.05363, posted July 2026 on zhichai.net.

Paper Overview

Field: AI Author: Dylan Zongmin Liu Published: 2026-07-06 arXiv: 2607.05363

Abstract (translated)

Personal agents are becoming persistent, user-owned intermediaries: remembering preferences, filtering platform-mediated information, using tools, and negotiating with services. Existing benchmarks evaluate tool use, web navigation, desktop control, personalization, recommendation, and evolving context—but they rarely ask whether an agent protects user sovereignty: advancing the user's current interests while respecting privacy, consent, evidence, user burden, and resistance to manipulative incentives.

This paper introduces SovereignPA-Bench, an executable benchmark that evaluates user-owned personal agents under evolving intents, platform mediation, privacy boundaries, consent constraints, evidence requirements, and burden trade-offs.

Key Results

Scale: 4 model families and 8 policy baselines evaluated on 120 sovereignty stress scenarios, producing 3,840 frozen prompt trajectories.
Main finding: The full sovereignty scaffold outperforms direct prompting, memory-only, consent-only, evidence-only, ReAct/tool-use, safety prompting, and judge-guard baselines on sovereignty scores.
Safety gains: The scaffold reduces privacy leakage, consent violations, over-concession, and manipulation capture.

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