CLBench-V: Evaluating Multimodal Context Learning from Grounding to Knowledge Acquisition
CLBench-V evaluates multimodal context learning; best model scores only 0.2847.
Key Findings
Methodology
CLBench-V evaluates multimodal context learning across three dimensions: context grounding, new information application, and new knowledge learning. It uses automated construction and filtering procedures, combining public benchmarks with newly constructed datasets in domains like science, finance, long-document understanding, spatial reasoning, and web-based visual QA.
Key Results
- Across 3,443 instances and six multimodal models, the best score is only 0.2847, indicating significant room for improvement in multimodal context learning.
- InternVL3.5-30B-A3B excels in context grounding and new knowledge learning.
- Qwen3.5-Plus performs best in new information application.
Significance
This study highlights the deficiencies of current multimodal models in context learning, especially in handling complex multimodal information. It provides a clear direction for future model improvements and underscores the importance of multimodal context learning in practical applications.
Technical Contribution
CLBench-V offers a systematic framework for evaluating multimodal context learning, addressing the gap where existing benchmarks focus mainly on unimodal textual contexts. It introduces automated dataset construction and filtering procedures, reducing the cost of domain-specific task construction.
Novelty
CLBench-V is the first to systematically evaluate multimodal context learning, particularly in domains like science and finance, providing new benchmarks and analysis frameworks.
Limitations
- Current models perform poorly with long contexts and high-density visual distractors.
- The benchmark focuses on specific domains and may not apply to all multimodal tasks.
Future Work
Future work could expand to more domains, develop more robust models to handle complex multimodal contexts, and optimize automated dataset construction and filtering procedures.
AI Executive Summary
Multimodal context learning is crucial for handling complex real-world tasks, yet existing evaluations focus primarily on textual contexts. CLBench-V fills this gap by introducing a multimodal benchmark. It covers domains like science, finance, long-document understanding, spatial reasoning, and web-based visual QA, assessing models across three dimensions: context grounding, new information application, and new knowledge learning.
Experimental results show that current multimodal models have significant room for improvement in context learning, with the best model scoring only 0.2847. InternVL3.5-30B-A3B excels in context grounding and new knowledge learning, while Qwen3.5-Plus performs best in new information application.
CLBench-V's contribution lies in providing a systematic framework for evaluating multimodal context learning, reducing the cost of domain-specific task construction. Future research could expand to more domains and develop more robust models to handle complex multimodal contexts.
Deep Analysis
Background
Multimodal context learning is crucial for handling complex real-world tasks, yet existing evaluations focus primarily on textual contexts. As fields like science and finance rapidly evolve, the multimodal presentation of information becomes increasingly prevalent.
Core Problem
The core problem is the inadequacy of current models in handling multimodal contexts, especially with long contexts and high-density visual distractors.
Innovation
CLBench-V introduces a systematic framework for evaluating multimodal context learning, covering multiple domains and reducing construction costs through automated dataset construction and filtering procedures.
Methodology
- �� Use automated construction and filtering procedures to build datasets
- �� Combine public benchmarks with newly constructed datasets
- �� Evaluate models across three dimensions: context grounding, new information application, and new knowledge learning
Experiments
The experimental design covers 3,443 instances and six multimodal models, evaluated under a unified inference and scoring framework.
Results
Experimental results show that current multimodal models have significant room for improvement in context learning, with the best model scoring only 0.2847.
Applications
The study's application scenarios include scientific research, financial analysis, long-document understanding, spatial reasoning, and web-based visual QA.
Limitations & Outlook
Current models perform poorly with long contexts and high-density visual distractors, and the benchmark focuses on specific domains.
Plain Language Accessible to non-experts
Imagine you're in a library, trying to find a book using its title, table of contents, and cover image. Multimodal context learning is like teaching a robot how to find the book using these different types of information. This process isn't just about reading text; it involves combining information from images and tables to make the right decision.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to use a map, a compass, and a mission briefing. Multimodal context learning is like teaching your character how to find the right path using all this information. It's not just about looking at the map; you need to combine the compass and mission briefing to make the right choice.
Glossary
Multimodal
Involves multiple forms of information, such as text, images, and tables.
Used in the paper to describe tasks that require combining multiple forms of information.
Context Learning
Learning from task-specific context rather than relying solely on pre-trained knowledge.
Used to evaluate a model's ability to extract and apply information in specific tasks.
Benchmark
A standardized test used to evaluate model performance.
CLBench-V serves as a benchmark for multimodal context learning.
Automated Construction
Using automated procedures to build datasets.
Used to reduce the cost of constructing domain-specific tasks.
New Knowledge Learning
Acquiring new rules, discoveries, or task semantics from context.
Evaluates a model's ability to acquire and apply new knowledge.
Open Questions Unanswered questions from this research
- 1 How to improve model performance with long contexts and high-density visual distractors?
- 2 How to expand CLBench-V to cover more domains of multimodal tasks?
Applications
Immediate Applications
Scientific Research
Helps researchers better extract information from multimodal data, improving research efficiency.
Long-term Vision
Intelligent Assistants
Develop intelligent assistants capable of handling complex multimodal information, enhancing user experience.
Abstract
Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained knowledge. While recent work has highlighted this capability as context learning, existing evaluations mainly focus on textual contexts. In many practical settings, however, the context to be learned from is multimodal: scientific findings are conveyed through figures and tables, financial indicators are scattered across converted reports, and spatial decisions depend on maps, scenes, or web pages. We introduce CLBench-V, a benchmark for multimodal context learning that addresses the difficulty of localizing where context use breaks down by organizing tasks around three dimensions: context grounding, new information application, and new knowledge learning. CLBench-V combines converted public benchmarks with newly constructed datasets spanning domains such as science, finance, long-document understanding, spatial reasoning, and web-based visual question answering. To reduce the cost of constructing domain-specific context-learning tasks, we further use automated construction and filtering procedures for our newly built datasets. Across 3,443 instances and six recent multimodal models, the best overall score is only 0.2847, indicating that multimodal context learning remains far from saturated. Moreover, InternVL3.5-30B-A3B performs best on context grounding and new knowledge learning, while Qwen3.5-Plus performs best on new information application. We further analyze judge reliability, context length, image count, and representative failure cases. Code is available at https://github.com/IamLihua/CLBench-V.