Beyond the Eye: Efficient Multimodal Reasoning via Self-Regulated Implicit Visual Tools
This paper presents a new approach to improving how models understand and reason with images and text together. The authors introduce a method that helps these models decide when to use their own knowledge and when to use external tools, making them.
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- 1 The optimization objective of DAPO is defined as.
- 2 In the initial stage of our exploration, we investigate a discrete reward mechanism that assigns fixed constant rewards to different behavioral phases.
- 3 The tokenizer is trained with a standard VQ objective:.
- 4 Rather than calling external tools, BEE folds visual tool invocation directly into the training objective and learns an explicit capability boundary, so that it adaptively balances internal knowledge against implicit tool use, avoiding redundant invocations and reducing latency.
Introduction
• Xiuwei Chen, Quanlin Chen, Zisheng Chen, Kun Xiang, Hanhui Li, Zehua Ma, Mingyang Zhang, Xiaodan Liang are with Sun Yat-sen University, China. E-mail: {chenxw83,chenzsh9,xiangk,mazh58}@mail2.sysu.edu.cn, {lihh77,liangxd9}@mail.sysu.edu.cn. • Wentao Hu is with The Hong Kong Polytechnic University, China.
E-mail: wayne-wt.hu@connect.polyu.hk. • Jianhua Han, Hang Xu are with Yinwang Intelligent Technology Co., Ltd.,.
M Ultimodal Large Language Models (MLLMs) integrate multimodal information into language intelligence and have attracted considerable attention for their ability to understand and generate multimodal content.
Research Question
Rather than calling external tools, BEE folds visual tool invocation directly into the training objective and learns an explicit capability boundary, so that it adaptively balances internal knowledge against implicit tool use, avoiding redundant invocations and reducing latency. Beyond architectural designs, several studies investigate how to optimize latent reasoning.
The optimization objective of DAPO is defined as.
This design aligns with the objective described in Equation 5 .
Methodology
In recent years, extending the Chain-of-Thought (CoT) paradigm to multimodal settings enables models to decompose complex problems into multiple reasoning steps, leading to substantial improvements in task performance. They are unable to adaptively estimate task difficulty or dynamically determine whether to rely on internal parametric knowledge or invoke external tools and implicit visual reasoning.
Study Design
To quantitatively evaluate this issue, we introduce the Net Tool Gain (NTG) metric, which measures the actual marginal contribution of tool invocation to task success after excluding the intrinsic capability of the base model.
The NTG analysis reveals an important observation: although the model optimized via the first training stage and a simple DAPO algorithm possesses the capability of tool usage, it still suffers from substantial redundant tool invocation, as illustrated in Figure 4 .
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Results & Findings
As a result, redundant tool usage and unnecessary computational overhead remain unavoidable. The Stage 1 training of formalized CoT supervised fine-tuning aims to activate the model’s implicit tool representations, adaptive switching mechanism, and basic reasoning ability.
- As a result, redundant tool usage and unnecessary computational overhead remain unavoidable.
- The Stage 1 training of formalized CoT supervised fine-tuning aims to activate the model’s implicit tool representations, adaptive switching mechanism, and basic reasoning ability.
- During this stage, the model predicts both the type and output of tool behaviors required for reasoning and dynamically generates them in the form of CoT.
- To suppress redundant tool usage and improve inference efficiency, we adopt carefully designed formalized CoT labels as supervision signals.
- Specifically, the label set contains a mixture of reasoning trajectories with and without implicit tool slots, which explicitly guides the model to learn when to invoke.
To address this limitation, the Thinking with Images paradigm has recently been proposed to enhance visual grounding by enabling reasoning directly over visual representations.
Consequently, they cannot adaptively determine when additional latent tokens are needed, which may lead to unnecessary computation on simpler ones.
Practical Applications
In scenes containing a large number of visual elements, BEE may miss part of the relevant information (cf ., Figure 29 ). It may also reflect the limited presence of similar multi-object autonomous driving scenarios in the BEE training data.
These limitations could be mitigated by incorporating more such data into future training. • Imperfect intermediate reasoning steps.
Thinking with Images
This section explores recent developments in tool-augmented models that allow interaction with external computational environments, discussing various frameworks and their contributions to multimodal reasoning.
Implicit Visual Tools
This section introduces the concept of implicit visual tools and discusses the limitations of conventional Chain-of-Thought reasoning, emphasizing the need for adaptive reasoning mechanisms that can optimize computational efficiency.
Frequently Asked Questions
The optimization objective of DAPO is defined as. In the initial stage of our exploration, we investigate a discrete reward mechanism that assigns fixed constant rewards to different behavioral phases.
They are unable to adaptively estimate task difficulty or dynamically determine whether to rely on internal parametric knowledge or invoke external tools and implicit visual reasoning. To quantitatively evaluate this issue, we introduce the Net Tool Gain (NTG) metric, which measures the.
By penalizing ineffective tool dependency, this mechanism encourages the model to perform knowledge routing and ensures that implicit tools are invoked only when the model’s internal knowledge is insufficient. To address this limitation, the Thinking with Images paradigm has recently been proposed.
For each trajectory o i , the overall reward is defined as Equation 2 . This suggests that BEE progressively solves more challenging problems, resulting in a shift of the overall distribution toward easier cases.
To address this limitation, the Thinking with Images paradigm has recently been proposed to enhance visual grounding by enabling reasoning directly over visual representations. Consequently, they cannot adaptively determine when additional latent tokens are needed, which may lead to unnecessary computation on.
This paper presents a new approach to improving how models understand and reason with images and text together. The authors introduce a method that helps these models decide when to use their own knowledge and when to use external tools, making them.