Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·29d ago·5 min read
SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints
TL;DR
SovereignPA-Bench introduces a framework for evaluating user-owned personal agents that adapt to changing user intentions and consent constraints. It assesses how these agents perform under platform mediation scenarios.
✦ Why It Matters
Engineers should prioritize developing personal agents with dynamic consent management to enhance user satisfaction and adaptability.
Key Takeaways
- Implement SovereignPA-Bench for evaluating personal agents.
- Focus on user sovereignty in agent design.
- Prioritize consent-aware actions in agent evaluations.
- Utilize human audits to assess privacy and consent.
Full Summary
Personal agents serve as intermediaries that manage user preferences and interactions with services, yet existing benchmarks often overlook user sovereignty—balancing user interests with privacy and consent. SovereignPA-Bench addresses this gap by providing an executable framework to assess personal agents under various constraints, including evolving user intent and platform mediation.
The benchmark distinguishes between ObservableState, visible to agents, and HiddenLabels, accessible only to evaluators, allowing for comprehensive performance metrics. Evaluations across 120 scenarios and multiple model families yielded 3,840 trajectories, revealing that full-sovereign scaffolding significantly enhances sovereignty scores compared to other baselines.
Human audits confirmed high agreement on privacy and consent issues, while highlighting the subjective nature of manipulation assessments. These findings suggest that personal-agent evaluations should prioritize consent-aware and evidence-based actions over mere task completion.