Toward User-Conditioned Evaluation of Personal LLM Agents under Temporal Interventions

2026-07-20

AI Summary

Current evaluation methods for personal agents assess isolated dimensions—such as memory, tool use, or safety—independently, failing to capture the evolving state dynamics and cross-component failure propagation inherent in real-world user interactions over time. This work proposes a user-conditioned state evaluation protocol that formally defines four necessary criteria for meaningful personal agent assessment: explicit temporal intervention, state persistence, cross-dimensional effect induction, and user-conditioned state change. Through focused benchmark auditing, formal modeling, and minimalistic design, the study identifies critical gaps in existing benchmarks and constructs a minimal evaluation framework that satisfies all four criteria, accompanied by a corresponding metric suite. This provides a clear and principled technical pathway for future evaluations of personal agents.

Abstract

Personal agents maintain memories, learned skills, tool configurations, and policy state that evolve with each user. Existing agent benchmarks often evaluate these capabilities in isolation: tool benchmarks test invocation under fixed APIs, memory benchmarks test recall or forgetting, and safety benchmarks test static policy compliance. We argue that personal-agent evaluation requires a different protocol: replaying the same temporal intervention across different persistent user-conditioned states and measuring how failures propagate across agent components. We formalize this requirement as four conditions: explicit temporal intervention, persistent state across the intervention, induced cross-dimensional effects, and variation in user-conditioned state. A focused audit of public benchmark protocols selected by explicit inclusion criteria identifies several close cases. Under our explicitly narrow operationalization, we did not find a protocol in that audited set satisfying all four conditions. This claim is scoped as a focused gap analysis with bounded literature coverage. This position paper proposes a minimal benchmark design and candidate reporting metrics for user-conditioned adaptation. The result is a concrete design requirement for future personal-agent evaluation, with metrics used as reporting tools for that requirement.

Problem

• personal agents
• temporal interventions
• user-conditioned evaluation
• persistent state
• benchmarking

Innovation

• personal LLM agents
• temporal interventions
• user-conditioned evaluation
• persistent state
• benchmark design