Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis | ScienceToStartup | ScienceToStartup

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References (5)

[1]

LLMLOOP: Improving LLM-Generated Code and Tests Through Automated Iterative Feedback Loops

2025Ravin Ravi, Dylan Bradshaw et al.

[2]

Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness

2025Scott Blyth, Sherlock A. Licorish et al.

[3]

Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges

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Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis

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Founder's Pitch

"A deterministic AST-based tool to auto-correct semantic errors in LLM-generated code, enhancing reliability without runtime execution."

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Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis

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Founder's Pitch

"A deterministic AST-based tool to auto-correct semantic errors in LLM-generated code, enhancing reliability without runtime execution."

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