Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning
- URL: http://arxiv.org/abs/2606.08231v1
- Date: Sat, 06 Jun 2026 15:39:29 GMT
- Title: Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning
- Abstract summary: Test-time Scaling (TTS) has emerged as a pivotal research direction for enhancing model performance.<n>Recent advancements have adapted this paradigm to Multimodal Foundation Models (MFMs)<n>We present the first comprehensive review of TTS research for MFMs.
- Score: 26.30578541156939
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Test-time Scaling (TTS) has emerged as a pivotal research direction for enhancing model performance by dynamically allocating computational resources during inference. Recent advancements have adapted this paradigm to Multimodal Foundation Models (MFMs), unlocking their potential in multimodal reasoning and generation. Despite rapid progress, the field lacks a systematic survey and unified theoretical framework to delineate the developmental landscape of multimodal TTS. To bridge this gap, we present the first comprehensive review of TTS research for MFMs, proposing a unified taxonomic framework that categorizes existing methodologies into three distinct strategies: sampling-based, feedback-based, and search-based approaches. We further summarize representative applications and benchmarks commonly utilized to evaluate multimodal TTS capabilities in generation and reasoning tasks. Finally, this survey discusses open challenges and outlines future research directions, providing a systematic roadmap for subsequent studies in this rapidly evolving field.
Related papers
- Cross Domain Evaluation of Multimodal Chain-of-Thought Reasoning of different datasets into the Amazon CoT Framework [1.7842332554022695]
This work presents a comprehensive analysis of Multimodal Chain-of-Thought (Multimodal-CoT) reasoning.<n>We evaluate its effectiveness on the A-OKVQA, OKVQA and ChartQA datasets.<n>Our findings reveal that while vision integration significantly reduces hallucination in rationale generation, the effectiveness of CoT reasoning varies substantially across question types.
arXiv Detail & Related papers (2025-11-24T16:20:02Z) - Deep Research: A Systematic Survey [118.82795024422722]
Deep Research (DR) aims to combine the reasoning capabilities of large language models with external tools, such as search engines.<n>This survey presents a comprehensive and systematic overview of deep research systems.
arXiv Detail & Related papers (2025-11-24T15:28:28Z) - From Perception to Reasoning: Deep Thinking Empowers Multimodal Large Language Models [36.54062692717823]
Chain-of-Thought (CoT) reasoning has demonstrated significant efficacy in language models by enhancing reasoning transparency and output interpretability.<n>This paper provides a systematic review centered on "Multimodal Chain-of-Thought" (MCoT)
arXiv Detail & Related papers (2025-11-17T01:22:37Z) - Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models [0.0]
This systematic literature review analyzes research published between January 2020 and early 2024 that focuses on the explainability of multimodal models.<n>We find that evaluation methods for XAI in multimodal settings are largely non-systematic, lacking consistency, robustness, and consideration for modality-specific cognitive and contextual factors.
arXiv Detail & Related papers (2025-08-06T13:14:20Z) - Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
arXiv Detail & Related papers (2025-06-11T03:29:18Z) - Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey [124.23247710880008]
multimodal CoT (MCoT) reasoning has recently garnered significant research attention.<n>Existing MCoT studies design various methodologies to address the challenges of image, video, speech, audio, 3D, and structured data.<n>We present the first systematic survey of MCoT reasoning, elucidating the relevant foundational concepts and definitions.
arXiv Detail & Related papers (2025-03-16T18:39:13Z) - Low-Rank Adaptation for Foundation Models: A Comprehensive Review [56.341827242332194]
Low-Rank Adaptation (LoRA) has emerged as a highly promising approach for mitigating these challenges.<n>This survey provides the first comprehensive review of LoRA techniques beyond large Language Models to general foundation models.
arXiv Detail & Related papers (2024-12-31T09:38:55Z) - Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs)<n>We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs.<n>We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
arXiv Detail & Related papers (2024-12-19T13:25:39Z) - A Survey on Multimodal Benchmarks: In the Era of Large AI Models [13.299775710527962]
Multimodal Large Language Models (MLLMs) have brought substantial advancements in artificial intelligence.
This survey systematically reviews 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application.
arXiv Detail & Related papers (2024-09-21T15:22:26Z) - Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis [70.78170766633039]
We address the need for means of assessing MTS forecasting proposals reliably and fairly.
BasicTS+ is a benchmark designed to enable fair, comprehensive, and reproducible comparison of MTS forecasting solutions.
We apply BasicTS+ along with rich datasets to assess the capabilities of more than 45 MTS forecasting solutions.
arXiv Detail & Related papers (2023-10-09T19:52:22Z) - A Survey on Interpretable Cross-modal Reasoning [64.37362731950843]
Cross-modal reasoning (CMR) has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics.
This survey delves into the realm of interpretable cross-modal reasoning (I-CMR)
This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR.
arXiv Detail & Related papers (2023-09-05T05:06:48Z)
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.