Are Tools Always Beneficial? Learning to Invoke Tools Adaptively for Dual-Mode Multimodal LLM Reasoning
arXiv:2605.19852v2 Announce Type: replace Abstract: Tool-augmented reasoning has emerged as a promising direction for enhancing the reasoning capabilities of multimodal large language models (MLLMs). However, existing studies mainly focus on enabling models to perform tool invocation, while neglecting the necessity of invoking tools. We argue that tool usage is not always beneficial, as redundant or inappropriate invocations largely increase reasoning overhead and even mislead model predictions. To address this issue, we introduce AutoTool, a model that adaptively decides whether to invoke too
The proliferation of tool-augmented MLLMs necessitates improved efficiency and reliability, making adaptive tool invocation a critical next step in their development.
This development addresses a key limitation in current AI systems, enhancing MLLM efficiency and reducing errors, which is crucial for their integration into complex workflows.
MLLMs will become more resource-efficient and reliable by selectively invoking tools, leading to more robust and less 'hallucinated' outputs.
- · AI developers
- · Cloud computing providers (reduced computation costs)
- · Industries deploying MLLM-based automation
- · Inefficient MLLM architectures
- · Systems heavily reliant on brute-force tool invocation
Adaptive tool invocation becomes a standard feature in advanced conversational AI and agentic systems.
Improved MLLM efficiency could accelerate the development and deployment of more complex AI agents.
More reliable and efficient AI agents could lead to faster automation of white-collar tasks, impacting labor markets.
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Read at arXiv cs.CL