Constraint Tax in Open-Weight LLMs: An Empirical Study of Tool Calling Suppression Under Structured Output Constraints
arXiv:2606.25605v1 Announce Type: new Abstract: Tool Calling and Structured Output are two core capabilities of modern Agent systems, yet their interaction under joint deployment conditions remains insufficiently understood. This paper reports a reproducible phenomenon observed in a production Agent system: when Tool Calling and JSON Schema constraints are simultaneously enabled, multiple open-weight models cease invoking tools despite maintaining high schema compliance. We refer to this behavior as Tool Suppression. Through controlled experiments across multiple model families and deployment
The increasing complexity of AI agentic systems and their joint deployment of varied capabilities is revealing subtle yet critical interaction flaws.
This finding highlights a fundamental limitation in the reliability and predictability of open-weight LLMs when integrating advanced features like tool calling with structured output constraints.
Developers of AI agentic systems must now explicitly account for 'Tool Suppression' when designing and deploying LLMs, requiring more robust error handling and model selection strategies.
- · AI model auditing firms
- · Developers of custom, fine-tuned models
- · Proprietary model providers with more integrated stacks
- · Developers relying solely on open-weight LLMs for complex agentic systems
- · Early adopters of less mature open-weight models
Further research will focus on understanding and mitigating 'Constraint Tax' phenomena in multi-modal and multi-capability AI systems.
This could lead to a bifurcation in the open-weight LLM market, with some models optimized for pure generation and others for highly structured, agentic tasks.
The complexity of integrating varied AI capabilities might drive demand for more opinionated, vertically integrated AI development platforms, potentially limiting true open-source innovation at the agentic layer.
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Read at arXiv cs.CL