Constraint Tax in Open-Weight LLMs: An Empirical Study of Tool Calling Suppression Under Structured Output Constraints

Researchers have identified a phenomenon termed "Tool Suppression," where the simultaneous enforcement of JSON Schema constraints and tool-calling capabilities leads open-weight Large Language Models (LLMs) to stop invoking tools, despite maintaining high adherence to the required output structure.

The Interaction Between Tool Calling and Structured Outputs

In the development of modern AI Agent systems, two critical capabilities are often deployed concurrently: Tool Calling (the ability of a model to invoke external functions to perform tasks) and Structured Output (the enforcement of specific formats, such as JSON Schema, to ensure downstream parsability). While both are essential for reliability, their joint deployment can create unexpected behavioral conflicts.

Understanding "Tool Suppression"

According to the study by Fangzheng Li, Aimin Zhang, and Chen Lv, a reproducible phenomenon has been observed in production environments where open-weight models exhibit a significant decline in tool invocation rates when structured output constraints are active. This effect, which the authors define as Tool Suppression, manifests as a paradox: the model remains highly compliant with the requested JSON schema but fails to trigger the necessary tool calls to resolve the user's query.

The "Constraint Tax" Hypothesis

The researchers suggest that the overhead of maintaining strict schema compliance acts as a "tax" on the model's reasoning capabilities. When the model is forced to prioritize the structural integrity of the output, the probability of generating the specific tokens required for tool invocation is suppressed, effectively disabling the agent's ability to interact with external environments.

Empirical Findings

The study utilizes controlled experiments to demonstrate that this suppression is not an isolated incident but a systemic behavior across multiple open-weight models. The findings indicate that the interaction between the constraint layer and the model's internal decision-making process creates a trade-off where structural adherence comes at the expense of functional utility.