Implementation of paper:
- Seiji Maekawa, Jackson Hassell, Pouya Pezeshkpour, Tom Mitchell, Estevam Hruschka. Towards Reliable Benchmarking: A Contamination Free, Controllable Evaluation Framework for Multi-step LLM Function Calling
- Introduces FuncBenchGen, a contamination-free, controllable evaluation framework that casts tool use as traversal over a hidden function-dependency DAG, letting users precisely tune task difficulty (graph size, dependency depth, and type-compatible distractor functions). 
- Provides an extensive empirical study across seven open/closed LLMs, showing reasoning-optimized models outperform general ones but degrade sharply with deeper dependencies; “connected” distractors (irrelevant yet type-compatible functions) strongly harm performance; and common failures stem from brittle state/variable tracking despite syntactically valid calls. 
- Proposes a lightweight mitigation—explicitly restating known variable values at each step—that requires no model changes and substantially boosts success rates (e.g., GPT-5 from 62.5%→81.3%).
Coming soon...
@misc{maekawa2025distinctive,
title={Towards Reliable Benchmarking: A Contamination Free, Controllable Evaluation Framework for Multi-step LLM Function Calling},
author={Seiji Maekawa and Jackson Hassell and Pouya Pezeshkpour and Tom Mitchell and Estevam Hruschka},
url={https://arxiv.org/abs/2509.26553},
year={2025}
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