# MMCL-Bench: Multimodal Context Learning from Visual Rules, Procedures, and Evidence

arXiv: https://arxiv.org/abs/2605.12703 · HTML: https://mlsanity.com/paper/2605.12703/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 25 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 22 of 22)

- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding](https://mlsanity.com/paper/2308.14508/) (2023)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic](https://mlsanity.com/paper/2306.15195/) (2023)
- [Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs](https://mlsanity.com/paper/2401.06209/) (2024)
- [InfographicVQA](https://mlsanity.com/paper/2104.12756/) (2021)
- [Scene Text Visual Question Answering](https://mlsanity.com/paper/1905.13648/) (2019)
- [Long-context LLMs Struggle with Long In-context Learning](https://mlsanity.com/paper/2404.02060/) (2024)
- [Hierarchical multimodal transformers for Multi-Page DocVQA](https://mlsanity.com/paper/2212.05935/) (2022)
- [MileBench: Benchmarking MLLMs in Long Context](https://mlsanity.com/paper/2404.18532/) (2024)
- [MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs](https://mlsanity.com/paper/2407.01509/) (2024)
- [CL-bench: A Benchmark for Context Learning](https://mlsanity.com/paper/2602.03587/) (2026)
- [VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks](https://mlsanity.com/paper/2410.19100/) (2024)
- [VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning](https://mlsanity.com/paper/2403.13164/) (2024)
- [MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly](https://mlsanity.com/paper/2505.10610/) (2025)
- [Benchmarking Multimodal Knowledge Conflict for Large Multimodal Models](https://mlsanity.com/paper/2505.19509/) (2025)
- [MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues](https://mlsanity.com/paper/2510.17722/) (2025)
- [MORSE-500: A Programmatically Controllable Video Benchmark to Stress-Test Multimodal Reasoning](https://mlsanity.com/paper/2506.05523/) (2025)

## Built on by (most cited first, 1 of 1)

- [Agents in the Large: Perception-Centered Architecture for Persistent Agents](https://mlsanity.com/paper/2608.30478/) (2026)

## Abstract (arXiv)

We introduce MMCL-Bench, a benchmark for multimodal context learning: learning task-local rules, procedures, and empirical patterns from visual or mixed-modality teaching context and applying them to new visual instances. Unlike text-only context learning or standard multimodal question answering, this setting requires models to recover and localize relevant evidence from images, screenshots, manuals, videos, and frame sequences before they can reason over the learned context. MMCL-Bench contains 102 tasks spanning three categories: rule system application, procedural task execution, and empirical discovery and induction. We evaluate frontier multimodal models with strict rubric-based scoring and find that current systems remain far from robust multimodal context learning, with even the strongest model solving fewer than one-third of tasks under strict evaluation. Diagnostic ablations and error analysis show that failures arise throughout the context-to-answer pipeline, including context anchoring, visual evidence extraction, context reasoning, and response construction. MMCL-Bench thus highlights multimodal context learning as an important unsolved capability bottleneck for current multimodal models.