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This paper identifies a critical gap in current agent benchmarks by proposing that personal LLM evaluations must account for how failures propagate across persistent user states under temporal interventions. The authors formalize four necessary conditions—such as explicit temporal intervention and cross-dimensional effects—and find no existing public protocol currently satisfies them, highlighting an urgent need for new evaluation metrics. This work establishes a foundational design requirement for future research into robust, adaptive personal agents rather than offering immediate product solutions or policy shifts.
Toward User-Conditioned Evaluation of Personal LLM Agents under Temporal Interventions
arXiv:2607.21635v1 Announce Type: new 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.
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.
9 pages, 2 figures, and 8 tables. Accepted for oral presentation at the ACM SIGKDD KDD 2026 Workshop on Personal Intelligence in the Agentic AI Era (PILA 2026)
Machine Learning (cs.LG)
arXiv:2607.21635 [cs.LG]
(or arXiv:2607.21635v1 [cs.LG] for this version)
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