Narcissus: Program Synthesis Using Context-Aware LLM Approximations
Large language models (LLMs) excel at programming, but not when the task fixes the target language: prompted with a grammar rare in their training data, their programs usually break the grammar or fail the given specification. Enumerative synthesizers search the space of syntactically correct programs systematically guided by LLMs; the state of the art guides them by approximating LLM proposals into rule frequencies, which loses where each construct belongs and prunes every rule the proposals miss, exactly when the proposals are wrong. We present Narcissus, a synthesizer that keeps the proposals as syntax trees and scores each expansion of a candidate program in its context: does a propos...
| Authors | Tilman Hinnerichs, Sebastijan Dumancic, Neil Yorke-Smith |
|---|---|
| Primary category | cs.AI |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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