SpecRef hybrid AR-diffusion decoding is tested on six benchmarks with three protocols, showing code benchmarks conflate structural and logical correctness, refinement can degrade correct tokens, and log-likelihood versus generative scoring produce inconsistent model rankings.
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A decoder is trained on 1010 style features to map style representations back to prompts, outperforming direct LLM prompting on style recovery, imitation, and steering tasks.
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Speculative Refinement: A Hybrid Autoregressive Diffusion Decoding Strategy and Its Behavior Across Benchmarks
SpecRef hybrid AR-diffusion decoding is tested on six benchmarks with three protocols, showing code benchmarks conflate structural and logical correctness, refinement can degrade correct tokens, and log-likelihood versus generative scoring produce inconsistent model rankings.
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Interpreting Style Representations via Style-Eliciting Prompts
A decoder is trained on 1010 style features to map style representations back to prompts, outperforming direct LLM prompting on style recovery, imitation, and steering tasks.