MixEval-X: Any-to-any Evaluations from Real-world Data Mixture

Jinjie Ni, Yifan Song, Deepanway Ghosal, Bo Li, David Junhao Zhang +9 more
2/12/2026

No confident code implementation found for this paper. 1 possible match can be shown below.

Semantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation

80% match2026

Consistent Adversarially Robust Linear Classification: Non-Parametric Setting

75% match2026

The Tilted Variational Autoencoder: Improving Out-of-Distribution Detection

75% match2026

Not All Tasks Are Born Equal: Understanding Zero-Shot Generalization

75% match2026

Revisiting Visual Model Robustness: A Frequency Long-Tailed Distribution View

75% match2026

Building Timeseries Dataset: Empowering Large-Scale Building Analytics

75% match2026

Cite this paper

@article{ni2026mixevalx,
  title  = {MixEval-X: Any-to-any Evaluations from Real-world Data Mixture},
  author = {Jinjie Ni and Yifan Song and Deepanway Ghosal and Bo Li and David Junhao Zhang and Xiang Yue and Fuzhao Xue and Yuntian Deng and Zian Zheng and Kaichen Zhang and Mahir Shah and Kabir Jain and Yang You and Michael Shieh},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:2d05f8e54aa648f482f7f60adf894556f60f07d9},
  journal = {ICLR 2025 2025}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2d05f8e54aa648f482f7f60adf894556f60f07d9.svg)](https://codeofpaper.com/paper/2d05f8e54aa648f482f7f60adf894556f60f07d9)

Discussion