arXiv cs.CL· Pe\~na Arroyo Gallego, Mauro Ruiz Moreno, Alejandro Acien, Sergio Romero-Tapiador, Teresa Arroyo-Gallego·· 12 小时前AI 评分31
PresenceBench：评估大语言模型对用户状态的适应能力
PresenceBench: An Exploratory Evaluation of Large Language Model Adaptation to User State
AI 导读
研究者提出 PresenceBench，用于测量 AI 智能层能否利用外部提供的用户状态（如准备度、心流、稳定性）来改善响应。
正文 · 原文
Abstract:Presence-aware intelligence is the capacity of an AI system's intelligence layer to adapt appropriately to the functional state of the person it serves (here readiness, flow, and stability) while preserving task quality and safety. We introduce PresenceBench, an instrument for measuring it. PresenceBench evaluates how the intelligence layer uses a supplied state, not how that state is sensed, and breaks the capability into measurable properties. Two are tested here. Receptivity: does the model take up the state and give a better response than with no signal? Specificity: does any improvement depend on the correct state, rather than on extra structured context? A third, adaptation across states, is a required endpoint not measured in this release. A five-arm causal design answers both questions, against each model's own baseline and an equally structured mismatched-state control, scored by a correctness-gated human paired comparison. The exploratory evaluation covered four models across eighteen single-turn, six fixed three-turn and six simulated natural-conversation scenarios. Supplying a synthetic state, raw or translated into a response policy, gave human-preference point estimates above 0.5 for all four models (Claude Sonnet 5, DeepSeek V4 Pro, Gemini 3.6 Flash, GPT-5.6 Terra), with three of eight 95% intervals excluding chance. Specificity was not resolved: no per-model interval excluded chance, and the design cannot yet separate the state values from a generic instruction to adapt. Policy delivery reduced priced cost and output tokens against each model's baseline; these are resource-use changes, not efficiency gains, because correctness non-inferiority was not established. Safety-floor failures were rare (13 of 360 cases), with a non-significant excess under policy delivery. We report the instrument, the frozen results, and the controls a confirmatory study needs.
| Comments: | 33 pages, 6 figures, 13 tables |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.23625 [cs.HC] |
| (or arXiv:2610.23625v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.23625 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Teresa Arroyo-Gallego [view email]
[v1]
Fri, 16 Oct 2026 15:33:04 UTC (866 KB)
来源：arXiv cs.CL · arxiv.org