HorizonBench: Long-Horizon Personalization with Evolving Preferences
User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent life event. We define this problem as long-horizon personalization and observe that progress on it is limited by data availability and measurement, with no existing resource providing both naturalistic long-horizon interactions and the ground-truth provenance needed to diagnose why models fail. We introduce a data generator that produces conversations from a structured mental state graph, yielding ground-truth provenance for every preference change across 6-month timelines, and from it construct HorizonBench, a benchmark of 4,245 items from 3...
| Authors | Shuyue Stella Li, Bhargavi Paranjape, Kerem Oktar et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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- stellalisy/HorizonBenchDataset · cited by
- dinobby/MINTEvalDataset · cited by
Models and datasets referencing this paper
- stellalisy/HorizonBenchDataset
- dinobby/MINTEvalDataset
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