Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models

Abstract

The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a profound meta-cognitive deficit: they struggle to arbitrate between leveraging internal knowledge and querying external utilities. Consequently, they frequently fall prey to blind tool invocation, resorting to reflexive tool execution even when queries are resolvable from the raw visual context. This pathological behavior precipitates severe latency bottlenecks and injects extraneous noise that derails sound reasoning. Existing reinforcement learning protocols attempt to mitigate this via a scalarized reward that penalizes tool usage. Yet, this coupled formulation creates an irreconcilable optimization dilemma: an aggressive penalty suppresses essential tool use, whereas a mild penalty is entirely subsumed by the variance of the accuracy reward during advantage normalization, rendering it impotent against tool overuse. To transcend this bottleneck, we propose HDPO, a framework that reframes tool efficiency from a competing scalar objective to a strictly conditional one. By eschewing reward scalarization, HDPO maintains two orthogonal optimization channels: an accuracy channel that maximizes task correctness, and an efficiency channel that enforces execution economy exclusively within accurate trajectories via conditional advantage estimation. This decoupled architecture naturally induces a cognitive curriculum-compelling the agent to first master task resolution before refining its self-reliance. Extensive evaluations demonstrate that our resulting model, Metis, reduces tool invocations by orders of magnitude while simultaneously elevating reasoning accuracy.

Figures (9)

• Figure 1: Comparison of tool-use efficiency and task performance. Existing methods rely heavily on tool calls, reflecting limited efficiency awareness. In contrast, our method uses tools far more selectively while achieving the best overall performance, showing that strong accuracy and high efficiency can be attained simultaneously.
• Figure 2: Comparison between coupled-reward optimization and HDPO. Existing methods entangle accuracy and efficiency into a single reward signal, while HDPO decouples them into separate branches and combines them only at the final loss, enabling more strategic tool use.
• Figure 3: Overview of Metis. A strategic multimodal reasoning agent that selectively invokes code execution, text search, and image search tools during multi-turn reasoning. Rather than invoking tools by default, Metis adaptively determines when tool interactions provide genuinely useful evidence, and otherwise reasons directly from the available context to obtain the final answer.
• Figure 4: Direct reasoning from visual context. The query can be resolved through visual understanding and prior knowledge alone. Metis abstains from tool invocation and answers directly, exemplifying the meta-cognitive restraint instilled by HDPO.
• Figure 5: Targeted code execution for fine-grained visual analysis. The question requires comparing curves in a specific subplot region that is difficult to resolve at the original image scale. Metis invokes code to crop and enlarge the relevant area, enabling precise identification of the curve behavior near the queried time step.