Can LLMs Perform Synthesis?
This addresses the problem of evaluating LLMs' capabilities in program synthesis for AI and software engineering, showing they are currently incremental compared to specialized tools.
The study compared LLMs (Qwen and GPT-5) with symbolic tools on program synthesis tasks across multiple domains, finding that symbolic tools solved more benchmarks and were faster, even with LLMs on more powerful hardware.
How do LLMs compare with symbolic tools on program synthesis tasks? We investigate this question on several synthesis domains: LTL reactive synthesis, syntax-guided synthesis, distributed protocol synthesis, and recursive function synthesis. For each domain, we choose a state-of-the-art symbolic tool and compare it to an open-source, 32 billion parameter version of the Qwen LLM and the proprietary, frontier LLM GPT-5. We couple Qwen with a symbolic verifier and run it repeatedly until it either produces a solution that passes the verifier, or until there is a timeout, for each benchmark. We run GPT-5 once per benchmark and verify the generated output. In all domains, the symbolic tools solve more benchmarks than Qwen and either outperform or are about on par with GPT-5. In terms of execution time, the symbolic tools outperform GPT-5 in all domains, and either outperform or are very close to Qwen, despite the fact that the LLMs are run on significantly more powerful hardware.