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Key Takeaways

  • Beyond Surface Imitation: Contrastive Modeling for Reasoning Path Alignment in Multimodal In-Context Learning Multimodal Large Language Models (MLLMs) often...
  • In-context learning (ICL) is widely used in multimodal large language models (MLLMs) and achieves strong performance across a wide range of multimodal tasks.
  • This limitation becomes more pronounced in complex multimodal tasks, thereby restricting further improvements in MLLM performance.
  • To address this issue, we propose a new multimodal ICL framework that combines contrastive demonstration modeling with the self-refinement capability of MLLMs.
  • This contrastive formulation makes the reasoning path toward the desired response more explicit and guides the MLLM beyond superficial imitation.
Paper AbstractExpand

In-context learning (ICL) is widely used in multimodal large language models (MLLMs) and achieves strong performance across a wide range of multimodal tasks. However, existing multimodal ICL methods often rely on surface level imitation of in-context demonstrations, making it difficult for MLLMs to align their responses with the reasoning path required by the given multimodal input. This limitation becomes more pronounced in complex multimodal tasks, thereby restricting further improvements in MLLM performance. To address this issue, we propose a new multimodal ICL framework that combines contrastive demonstration modeling with the self-refinement capability of MLLMs. Specifically, our framework reformulates each demonstration by explicitly contrasting a suboptimal response with a better response under the same input, together with a reasoning path that reveals how the response should be refined. This contrastive formulation makes the reasoning path toward the desired response more explicit and guides the MLLM beyond superficial imitation. Furthermore, because effective refinement depends on the current response, we introduce a response-conditioned retrieval mechanism to select demonstrations whose reasoning paths are more relevant to the current response. In addition, we use a lightweight alignment controller to predict response quality and determine whether further refinement is needed. Experiments on three types of multimodal tasks show that the proposed framework consistently improves MLLM performance, with particularly notable gains on visual question answering (VQA).

Beyond Surface Imitation: Contrastive Modeling for Reasoning Path Alignment in Multimodal In-Context Learning
Multimodal Large Language Models (MLLMs) often struggle with complex tasks because they rely on "surface level imitation"—copying patterns from examples rather than truly understanding the visual evidence. This paper introduces COMIL, a framework designed to shift MLLMs from simply mimicking output formats to following a logical reasoning path. By providing explicit guidance on how to refine a suboptimal response into a better one, COMIL helps models ground their answers in the specific visual details required for a task.

Moving Beyond Surface Imitation

Standard in-context learning provides models with examples of inputs and their correct outputs. However, when faced with complex visual questions, models often latch onto superficial similarities between the current input and the examples, leading to incorrect, "copied" answers. COMIL addresses this by reformulating demonstrations. Instead of just showing a final answer, each demonstration in COMIL includes a "suboptimal" response, a "better" response, and a reasoning path that explains the transition between the two. This teaches the model how to diagnose its own mistakes and identify the visual evidence needed to correct them. The same large language models question is explored in Cross-Regional Grapevine Cold Hardiness Prediction via..., which adds a research perspective.

Response-Conditioned Refinement

Because the best way to improve a response depends on what the model has already generated, COMIL uses a dynamic retrieval mechanism. Rather than selecting examples based solely on the input image or question, the system also looks at the similarity between the model's current, imperfect response and the suboptimal responses in its database. By retrieving demonstrations that specifically address the current error, the model receives highly relevant guidance for its next refinement step.

Quality Control and Stopping

To ensure the model doesn't over-refine or drift away from the correct answer, COMIL incorporates a lightweight alignment controller. This module acts as a quality monitor, predicting how well a response aligns with the desired task performance. It uses an acceptance threshold to decide when a response is "good enough," allowing the model to stop the refinement process early if it has reached a high-quality state. This prevents the model from potentially degrading a good answer through unnecessary further iterations. The same large language models question is explored in DRACO, which adds a research perspective.

Performance Gains

Experiments across various MLLMs show that COMIL consistently improves performance on reasoning-intensive tasks. The framework achieved notable success on Visual Question Answering (VQA) benchmarks, such as improving the accuracy of the Qwen3.5-9B model to 81.9%. These results demonstrate that by explicitly modeling the reasoning path, MLLMs can achieve more reliable, evidence-grounded performance without requiring parameter updates or additional training. as detailed in the full paper on Arxiv

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