---
title: "SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.05363v1"
permalink: "https://feed.astralor.com/items/itm_a9074c44acd4bcbde41a7e89"
published: "2026-07-06T17:39:05.000Z"
score: 81
channel: "ai"
tags: ["AI alignment","SovereignPA-Bench","Personal Agents","User Sovereignty","Privacy and Consent"]
---

# SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints

## AI commentary

> What I find most compelling about this paper is how it decomposes the abstract concept of 'sovereignty' into measurable dimensions—this is the next safety layer for teams building personal agents beyond task completion. It's especially important for policymakers and product managers, as privacy and consent are no longer slogans but auditable metrics.

## At a glance

Personal agents are becoming persistent user-owned intermediaries that remember preferences, filter platform-mediated information, use tools, and negotiate with services. Existing benchmarks focus on task completion but rarely assess whether an agent preserves user sovereignty—advancing current interests while respecting privacy, consent, evidence, user burden, and resistance to manipulative incentives. This paper introduces SovereignPA-Bench, an executable benchmark for evaluating user-owned personal agents under evolving intent, platform mediation, privacy boundaries, consent constraints, evidence requirements, and burden tradeoffs. It separates agent-visible ObservableState from evaluator-only HiddenLabels, reports component metrics for task success, alignment, privacy, consent, evidence, manipulation, burden, and auditability, and preserves paired scenario ordering for model and policy comparisons. The authors evaluate 120 sovereignty stress scenarios across 4 model families and 8 policy baselines, yielding 3,840 frozen-prompt trajectories with raw prompts, outputs, provider-form responses, parsed actions, recomputable metrics, hard-set analyses, qualitative cases, and a blinded 3-annotator audit over 240 items. Full-sovereign scaffolding improves sovereignty scores over direct, memory-only, consent-only, evidence-only, ReAct/tool-use, safety-prompt, and judge-guard baselines while reducing privacy leakage, consent violation, over-concession, and manipulation capture. Human audit shows high agreement on privacy and consent and lower agreement on manipulation, identifying the subjective frontier of platform-persuasion judgments. These results show that personal-agent evaluation must move beyond task completion toward representative, consent-aware, evidence-grounded action.

## Key points

- SovereignPA-Bench is a new executable benchmark for evaluating user-owned personal agents under evolving intent, platform mediation, and consent constraints.
- It separates agent-visible ObservableState from evaluator-only HiddenLabels and reports metrics on task success, alignment, privacy, consent, evidence, manipulation, burden, and auditability.
- Evaluated across 4 model families and 8 policy baselines on 120 stress scenarios, full-sovereign scaffolding outperforms traditional baselines on multiple metrics.
- Human audit shows high agreement on privacy and consent but lower agreement on manipulation, identifying subjective frontiers in platform-persuasion judgments.

## Full text

Personal agents are becoming persistent user-owned intermediaries: they remember preferences, filter platform-mediated information, use tools, and negotiate with services. Existing benchmarks evaluate tool use, web navigation, desktop control, personalization, recommendation, and evolving context, but rarely ask whether an agent preserves user sovereignty: advancing the user's current interests while respecting privacy, consent, evidence, user burden, and resistance to manipulative incentives. We introduce SovereignPA-Bench, an executable benchmark for evaluating user-owned personal agents under evolving intent, platform mediation, privacy boundaries, consent constraints, evidence requirements, and burden tradeoffs. The benchmark separates agent-visible ObservableState from evaluator-only HiddenLabels, reports component metrics for task success, alignment, privacy, consent, evidence, manipulation, burden, and auditability, and preserves paired scenario ordering for model and policy comparisons. We evaluate 120 sovereignty stress scenarios across 4 model families and 8 policy baselines, yielding 3,840 frozen-prompt trajectories with raw prompts, outputs, provider-form responses, parsed actions, recomputable metrics, hard-set analyses, qualitative cases, and a blinded 3-annotator audit over 240 items. Full-sovereign scaffolding improves sovereignty score over direct, memory-only, consent-only, evidence-only, ReAct/tool-use, safety-prompt, and judge-guard baselines while reducing privacy leakage, consent violation, over-concession, and manipulation capture. Human audit shows high agreement on privacy and consent and lower agreement on manipulation, identifying the subjective frontier of platform-persuasion judgments. These results show that personal-agent evaluation must move beyond task completion toward representative, consent-aware, evidence-grounded action.

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Verify against the original before citing; curated by Astral Feed: https://feed.astralor.com/items/itm_a9074c44acd4bcbde41a7e89