TBNAI
P-ContextBench: From Personal Fact Recall to Personalized Agent Behavior
Publication: cs.AI updates on arXiv.org
Author: Grant Nasir Brown, Abdul Basir Bari, Anmol Tadikonda, Isabel Song
Source published: 02/11/2026, 05:00:00 UTC
Briefing created: 02/11/2026, 05:58:31 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 02/11/2026, 05:03:25 UTC.
Briefings
P-ContextBench is a benchmark designed to evaluate how well Large Language Models handle personal context, using synthetic digital humans generated from demographic seeds. The system tests five capabilities including fact recovery, temporal tracking, and safe handling of conflicting requests. It was instantiated with 100 personas, 10,088 hidden facts, and 18,261 app-trace fragments, featuring 4,810 verified test cases. The study found that even high-performing SOTA models struggle with temporal tracking, pattern inference, and constraint-aware decisions, with fewer than 10% of test cases answered correctly across repeated runs.
Key points
- P-ContextBench constructs population-structured digital humans from demographic seeds reflecting the U.S. population.
- The benchmark evaluates five capabilities: single-source fact recovery, multi-source fact recovery, temporal state tracking, inference of higher-level personal patterns, and safe handling of requests conflicting with persona-specific constraints.
Context
This research addresses the difficulty of evaluating safe personal-context use in LLMs by creating a realistic, verifiable benchmark. It provides measurable targets for improving the accuracy, reliability, and safety of LLM-based personal agents.
Limitations
The evidence is limited to a single preprint from arXiv. Results are based on 100 personas and 10,088 hidden facts, with 18,261 app-trace fragments. The study reports on performance metrics rather than user experience or long-term safety outcomes. Some capabilities like temporal tracking and pattern inference showed low accuracy even in high-performing models, indicating significant uncertainty in current capabilities.
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