---
title: "Test-Time Scaling in Multimodal Foundation Models: A"
description: "The authors, from Sun Yat-sen University, present what they describe as the first dedicated survey of the area. 121 references."
authors: ["Cong Wan", "Ying He", "Zhongzhan Huang", "Hefeng Wu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.383/"
doi: "10.18653/v1/2026.findings-acl.383"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wan-2026-test-time-scaling-multimodal-foundation/"
---
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# Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning

**Authors:** Cong Wan, [Ying He](https://md.research.elamur.ai/free/en/authors/ying-he/), Zhongzhan Huang and [Hefeng Wu](https://md.research.elamur.ai/free/en/authors/hefeng-wu/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.383/) · [PDF](https://aclanthology.org/2026.findings-acl.383.pdf) · [DOI 10.18653/v1/2026.findings-acl.383](https://doi.org/10.18653/v1/2026.findings-acl.383) · [OpenAlex](https://openalex.org/W7166816453)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Test-time scaling (TTS) improves model output by spending extra compute at inference instead of adding parameters or training data. It is spreading quickly from LLMs to Multimodal Foundation Models (MFMs), but this multimodal work has not yet been organized into one framework. The authors, from Sun Yat-sen University, present what they describe as the first dedicated survey of the area. It covers two model families: MLLMs and diffusion models. The iterative denoising and Classifier-Free Guidance of diffusion models make them naturally suited to TTS. The survey sorts existing methods into three families: sampling-based, feedback-based and search-based. It then shows how these are applied in multimodal generation and reasoning, and an appendix collects relevant benchmarks. The survey ends by naming open problems: combining strategies, stopping errors from compounding in long reasoning chains, and controlling hallucinations during the inference process rather than afterwards. For practitioners, the survey works as a map of current inference-time techniques for vision-language systems.

## Key points
- **Taxonomy:**
  - Sampling-based methods: Best-of-N and majority voting (e.g., TTGen, SANA 1.5, UniGen, RoboMonkey, Video-RTS).
  - Feedback-based methods: reward models and iterative refinement (e.g., VisualPRM, RoVer, Reflect-DiT, GenPilot, UI2Code^N).
  - Search-based methods: beam search, tree search, and heuristic or adaptive search (e.g., Mulberry, VisuoThink, ZoomEye, MindJourney, TTS-VAR).
- **Application patterns:**
  - Vision-Language-Action control mostly uses sampling-based methods plus tree search.
  - Multimodal math reasoning relies mainly on MCTS, with roll-back of faulty steps.
  - Video tasks use search or iterative refinement to find the segments relevant to a query.
- **Paper resources:** Fig. 1 tracks multimodal TTS papers on arXiv from 2024 Q4 to 2025 Q4, and Appendix A lists benchmarks.
- **Future directions:** hybrid scaling (citing Marco-o1 as an LLM example), handling the Error Snowballing Effect, and process-level hallucination suppression.
- **Scope limits:** only image and video vision-language settings are covered, with no audio, and there is no comparison against techniques built for pure LLMs.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Sun Yat-sen University](https://md.research.elamur.ai/free/en/universities/sun-yat-sen-university/) — University, Guangzhou, China · 53 works · 9,185 citations

📚 Shelves (3): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works

🧭 Topics (3): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works · [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,426 works · [🖼️ Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/) · 657 works

## Cite

Cite as: Cong Wan, Ying He, Zhongzhan Huang and Hefeng Wu. 2026. Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wan2026test,
  title = {{Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning}},
  author = {Cong Wan, Ying He, Zhongzhan Huang and Hefeng Wu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.383},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.383}
}
```

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*Updated 1 November 2026*