---
title: "Cross Domain Test Time Scaling: Scale Knowledge and Reasoning on Cross Domains"
authors:
  - "Minxi Yan"
  - "Yihua Shao"
  - "Yanling Pan"
  - "Siyu Chen"
  - "Hongjuan Pei"
  - "Hao Tang"
  - "Fei Ma"
  - "Jingcai Guo"
  - "Nicu Sebe"
doi: "10.24963/ijcai.2026/217"
url: "https://ijcai.org/proceedings/2026/217"
pdf: "https://ijcai.org/proceedings/2026/0217.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "1947-1955"
track: "Main Track"
keywords:
  - "Machine Learning: Foundation models"
  - "Machine Learning: Cost-sensitive learning"
  - "Computer Vision: Transfer, low-shot, semi- and un- supervised learning"
  - "Machine Learning: Learning sparse models"
  - "Machine Learning: Open-World/Open-Set/OOD Learning"
---

# Cross Domain Test Time Scaling: Scale Knowledge and Reasoning on Cross Domains

Minxi Yan, Yihua Shao, Yanling Pan, Siyu Chen, Hongjuan Pei, Hao Tang, Fei Ma, Jingcai Guo, Nicu Sebe

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 1947-1955. DOI: [10.24963/ijcai.2026/217](https://doi.org/10.24963/ijcai.2026/217)

## Abstract

Test-time scaling (TTS) has demonstrated remarkable potential in enhancing the reasoning capabilities of Large Language Models (LLMs) and Large Vision-Language Models (LVLMs). However, its application has primarily been limited to domains such as mathematics and programming, owing to their reasoning-intensive nature and the ease of result verification. Its utility in other knowledge-intensive fields, such as medicine and general scientific research, remains underexplored.
To bridge this gap and unlock the potential of TTS in broader domains, we propose Cross-Domain TTS, a novel framework that enables task-tailored scaling. This framework consists of two key components: a conformal prediction-based cold-start strategy and an information-gain-based dynamic reasoning adjustment. The CP-based cold-start strategy guides the model's initialization during test-time scaling based on conformal prediction theory, while the information-gain-based dynamic reasoning adjustment guides the model's reasoning progress through a progress vector according to the information gain of reasoning steps. We conducted experiments using LLMs and LVLMs on cross-domain benchmarks. Our results demonstrate that the proposed framework consistently improves performance across various domain-specific datasets. For instance, in the medical domain, it achieves an improvement of up to 17% in pass@1 accuracy while reducing inference latency and saving up to 30% in token consumption. Code is available at https://github.com/Yan0613/Cross-Domain-TTS.