Mitigating Visual Context Degradation in Large Multimodal Models: A Training-Free Decoupled Agentic Framework

With the continuous expansion of Large Language Models (LLMs) and advances in reinforcement learning, LLMs have demonstrated exceptional reasoning capabilities, enabling them to address a wide range of complex problems. Inspired by these achievements, researchers have extended related techniques to Large Multimodal Models (LMMs). However, a critical limitation has emerged, reflected in the progressive loss of visual grounding. As the reasoning chain grows longer, LMMs tend to rely increasingly on the textual information generated in earlier steps, while the initially extracted visual information is rarely revisited or incorporated. This phenomenon often causes the reasoning process to drift away from the actual image content, resulting in visually implausible or even erroneous conclusions. To overcome this fundamental limitation, we propose a novel, training-free agentic paradigm that Decouples cognitive Reasoning from visual Perception (DRP). In this framework, a powerful LLM serves as a strategic Reasoner, orchestrating the inference process by explicitly querying an LMM-acting as a dedicated Observer-to retrieve fine-grained visual details on demand. This approach is lightweight, model-agnostic, and plug-and-play, necessitating no additional training or architectural modifications. Extensive experiments demonstrate our framework DRP's efficacy in regulating the visual reasoning trajectory, significantly mitigating reasoning drift, and enforcing robust visual grounding. Notably, on the MathVision benchmark, the integration of Qwen2.5-VL-7B and Qwen3-32B achieves an accuracy of 47.2%, outperforming GPT-4o's 40.6%. These findings underscore the potential of our approach to enhance multimodal reasoning reliability without the need for costly retraining. Our code is publicly available at https://github.com/hongruijia/DRP.