{"slug": "toward-user-conditioned-evaluation-of-personal-llm-agents-under-temporal", "title": "Toward User-Conditioned Evaluation of Personal LLM Agents under Temporal Interventions", "summary": "A new arXiv paper argues that personal-agent evaluation requires a protocol replaying temporal interventions across different persistent user-conditioned states, formalizing four conditions: explicit temporal intervention, persistent state, induced cross-dimensional effects, and user-conditioned state variation. An audit of public benchmarks found no protocol satisfying all four conditions, leading the authors to propose a minimal benchmark design and candidate reporting metrics for user-conditioned adaptation.", "body_md": "arXiv:2607.21635v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/toward-user-conditioned-evaluation-of-personal-llm-agents-under-temporal", "canonical_source": "https://arxiv.org/abs/2607.21635", "published_at": "2026-07-27 04:00:00+00:00", "updated_at": "2026-07-27 04:08:09.693150+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/toward-user-conditioned-evaluation-of-personal-llm-agents-under-temporal", "markdown": "https://wpnews.pro/news/toward-user-conditioned-evaluation-of-personal-llm-agents-under-temporal.md", "text": "https://wpnews.pro/news/toward-user-conditioned-evaluation-of-personal-llm-agents-under-temporal.txt", "jsonld": "https://wpnews.pro/news/toward-user-conditioned-evaluation-of-personal-llm-agents-under-temporal.jsonld"}}