# QoNext: Towards Next-generation QoE for Foundation Models

arXiv: https://arxiv.org/abs/2509.21889 · HTML: https://mlsanity.com/paper/2509.21889/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 81 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 30 of 32)

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- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
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- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision](https://mlsanity.com/paper/2309.14181/) (2023)
- [Evaluating Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2310.19736/) (2023)
- [UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark](https://mlsanity.com/paper/2404.09619/) (2024)
- [HumanAesExpert: Advancing a Multi-Modality Foundation Model for Human Image Aesthetic Assessment](https://mlsanity.com/paper/2503.23907/) (2025)
- [HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation](https://mlsanity.com/paper/2505.11454/) (2025)
- [Topic-based Evaluation for Conversational Bots](https://mlsanity.com/paper/1801.03622/) (2018)
- [Creation-MMBench: Assessing Context-Aware Creative Intelligence in MLLM](https://mlsanity.com/paper/2503.14478/) (2025)
- [EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences](https://mlsanity.com/paper/2405.15708/) (2024)
- [Information Density Principle for MLLM Benchmarks](https://mlsanity.com/paper/2503.10079/) (2025)
- [AACessTalk: Fostering Communication between Minimally Verbal Autistic Children and Parents with Contextual Guidance and Card Recommendation](https://mlsanity.com/paper/2409.09641/) (2024)
- [HERM: Benchmarking and Enhancing Multimodal LLMs for Human-Centric Understanding](https://mlsanity.com/paper/2410.06777/) (2024)
- [Impact of Response Latency on User Behaviour in Mobile Web Search](https://mlsanity.com/paper/2101.09086/) (2021)
- [DEnsity: Open-domain Dialogue Evaluation Metric using Density Estimation](https://mlsanity.com/paper/2305.04720/) (2023)
- [GazeSwipe: Enhancing Mobile Touchscreen Reachability through Seamless Gaze and Finger-Swipe Integration](https://mlsanity.com/paper/2503.21094/) (2025)

## Abstract (arXiv)

Existing evaluations of foundation models predominantly focus on output correctness, treating interaction as a static exchange of information. However, such perspectives overlook the essence of the LLM-driven conversational experience, which is determined not only by content quality but, crucially, by dynamic service attributes such as generation velocity and latency patterns. To address this gap, we introduce QoNext, the first framework that adapts Quality of Experience (QoE) principles from networking and multimedia to the holistic assessment of human-AI interaction. QoNext identifies experiential factors that shape user experience and incorporates them into controlled experiments in simulated interaction scenarios, where human ratings are collected under diverse configurations. From these studies we construct the QoNext Database and train the QoNext Model, a neural predictor that estimates user experience directly from measurable system parameters. Our results demonstrate that QoNext effectively decodes the underlying mechanisms of user satisfaction and enables precise prediction of human sentiment across varied service conditions.