AI RESEARCH
SJD-VP: Speculative Jacobi Decoding with Verification Prediction for Autoregressive Image Generation
arXiv CS.CV
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ArXi:2603.27115v1 Announce Type: new Speculative Jacobi Decoding (SJD) has emerged as a promising method for accelerating autoregressive image generation. Despite its potential, existing SJD approaches often suffer from the low acceptance rate issue of speculative tokens due to token selection ambiguity. Recent works attempt to mitigate this issue primarily from the relaxed token verification perspective but fail to fully exploit the iterative dynamics of decoding.