UXBench: Benchmarking User Experience in AI Assistants

• URL: http://arxiv.org/abs/2606.09570v2
• Date: Tue, 09 Jun 2026 13:55:26 GMT
• Title: UXBench: Benchmarking User Experience in AI Assistants
• Abstract summary: We present UXBench, the first user-centric benchmark grounded in real user feedback signals.<n>The dataset closely reflects real user distributions, covering 8 scenarios, 83 domains, and diverse failure patterns.<n>We show that user feedback prediction is a learnable capability, where a reward model trained from in-the-wild feedback signals can achieve well-calibrated accuracy.
• Score: 39.65761394260019
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. We present UXBench, the first user-centric benchmark grounded in real user feedback signals for evaluating preference alignment and dialogue generation. The benchmark consists of three interconnected tasks, UX Judge, UX Eval, and UX Recovery, with 7,400 test instances extracted from over 70K interaction logs of a mainstream Chinese AI assistant. The dataset closely reflects real user distributions, covering 8 scenarios, 83 domains, and diverse failure patterns that pose severe challenges. Extensive experiments on 26 frontier language models provide novel insights into how well models perceive user experience and how improvements in model capability contribute to better dialogue engagement. Through comprehensive analysis of model behavior and performance gaps, we show that user feedback prediction is a learnable capability, where a reward model trained from in-the-wild feedback signals can achieve well-calibrated accuracy. We further document the systematic biases of LLM-as-a-judge evaluation protocols and compare typical response strategies that directly affect user experience. UXBench establishes a new evaluation landscape and calls for greater attention to tailored UX optimization, contributing to a user-centric scaling law that shapes the success of AI assistants.

Related papers

• Learning Dynamic User Personas from Implicit Interaction Streams via Iterative Refinement [4.027292457517535]
  We propose IRIS, a framework that learns dynamic user personas directly from implicit interaction streams.<n>IRIS produces stable personas and distinguishes individual users while revealing limitations of memory-only approaches.<n>We validate IRIS on anonymized real-world Reddit r/AmItheAsshole (AITA) data, with personas built solely from each author's historical interactions.
  arXiv  Detail & Related papers  (2026-07-29T04:56:14Z)
• From Feelings to Metrics: Understanding and Formalizing How Users Vibe-Test LLMs [48.36707944399574]
  We study how vibe-testing works in practice and then formalize it to support systematic analysis.<n>In experiments on coding benchmarks, we find that combining personalized prompts and user-aware evaluation can change which model is preferred.
  arXiv  Detail & Related papers  (2026-04-15T17:57:08Z)
• ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders [48.83868690303791]
  We introduce ConvApparel, a new dataset of human-AI conversations designed to address this gap.<n>Its unique dual-agent data collection protocol -- using both "good" and "bad" recommenders -- enables counterfactual validation.<n>We propose a comprehensive validation framework that combines statistical alignment, a human-likeness score, and counterfactual validation.
  arXiv  Detail & Related papers  (2026-02-18T23:00:21Z)
• OutboundEval: A Dual-Dimensional Benchmark for Expert-Level Intelligent Outbound Evaluation of Xbench's Professional-Aligned Series [36.88936933010042]
  OutboundEval is a comprehensive benchmark for evaluating large language models (LLMs) in intelligent outbound calling scenarios.<n>We design a benchmark spanning six major business domains and 30 representative sub-scenarios, each with scenario-specific process decomposition, weighted scoring, and domain-adaptive metrics.<n>We introduce a dynamic evaluation method that adapts to task variations, integrating automated and human-in-the-loop assessment to measure task execution accuracy, professional knowledge application, adaptability, and user experience quality.
  arXiv  Detail & Related papers  (2025-10-24T08:27:58Z)
• UserRL: Training Interactive User-Centric Agent via Reinforcement Learning [104.63494870852894]
  Reinforcement learning (RL) has shown promise in training agentic models that engage in dynamic, multi-turn interactions.<n>We propose UserRL, a unified framework for training and evaluating user-centric abilities through standardized gym environments.
  arXiv  Detail & Related papers  (2025-09-24T03:33:20Z)
• User Experience Estimation in Human-Robot Interaction Via Multi-Instance Learning of Multimodal Social Signals [2.7138092972120766]
  This study proposes a UX estimation method for human-robot interaction (HRI) by leveraging multimodal social signals.<n>Unlike conventional models that rely on momentary observations, our approach captures both short- and long-term interaction patterns.<n> Experimental results demonstrate that our method outperforms third-party human evaluators in UX estimation.
  arXiv  Detail & Related papers  (2025-07-31T13:34:15Z)
• What Users Value and Critique: Large-Scale Analysis of User Feedback on AI-Powered Mobile Apps [2.352412885878654]
  We present the first comprehensive, large-scale study of user feedback on AI-powered mobile apps.<n>We leverage a curated dataset of 292 AI-driven apps across 14 categories with 894K AI-specific reviews from Google Play.<n>Our pipeline surfaces both satisfaction with one feature and frustration with another within the same review.
  arXiv  Detail & Related papers  (2025-06-12T14:56:52Z)
• Multi-agents based User Values Mining for Recommendation [52.26100802380767]
  We propose a zero-shot multi-LLM collaborative framework for effective and accurate user value extraction.<n>We apply text summarization techniques to condense item content while preserving essential meaning.<n>To mitigate hallucinations, we introduce two specialized agent roles: evaluators and supervisors.
  arXiv  Detail & Related papers  (2025-05-02T04:01:31Z)
• Mind the Gap! Static and Interactive Evaluations of Large Audio Models [55.87220295533817]
  Large Audio Models (LAMs) are designed to power voice-native experiences.<n>This study introduces an interactive approach to evaluate LAMs and collect 7,500 LAM interactions from 484 participants.
  arXiv  Detail & Related papers  (2025-02-21T20:29:02Z)
• Lusifer: LLM-based User SImulated Feedback Environment for online Recommender systems [0.0]
  Reinforcement learning (RL) recommender systems often rely on static datasets that fail to capture the fluid, ever changing nature of user preferences in real-world scenarios.<n>We introduce Lusifer, an LLM-based simulation environment designed to generate dynamic, realistic user feedback for RL-based recommender training.
  arXiv  Detail & Related papers  (2024-05-22T05:43:15Z)
• Versatile User Identification in Extended Reality using Pretrained Similarity-Learning [16.356961801884562]
  We develop a similarity-learning model and pretrained it on the "Who Is Alyx?" dataset. In comparison with a traditional classification-learning baseline, our model shows superior performance. Our approach paves the way for easy integration of pretrained motion-based identification models in production XR systems.
  arXiv  Detail & Related papers  (2023-02-15T08:26:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.