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Specification generation by LLMs remains a formidable challenge, with verification complexity often masking genuine quality differences.
Historical data can be dynamically adapted to achieve an impressive 1.732x speedup in compiler optimization, reshaping how we approach autotuning.
RL agents can learn to write stronger formal specifications by using automatically generated negative tests as a reward signal for completeness, outperforming standard verification-based rewards.
