The Shape of Speculation: Efficient Draft Topology for Speculative Jacobi Decoding in Autoregressive Image Generation

Speculative Jacobi Decoding (SJD) has recently emerged as an efficient way to accelerate autoregressive image generation by refining multiple tokens in parallel. Recent multi-candidate extensions further improve SJD by introducing alternative continuations, yet their efficiency depends critically on how auxiliary candidates are organized. We argue that existing multi-candidate SJD strategies treat additional speculation as uniformly beneficial, overlooking that the contribution of candidate expansions varies substantially along the Jacobi trajectory. To address this issue, we propose FlexJacobi, a training-free framework that rethinks draft topology construction from the perspective of speculative candidate efficiency. Instead of treating all candidate expansions equally, FlexJacobi devises Verification-aware Topology Shaping (VTS), which derives candidate priorities from current draft distributions and distributes a fixed auxiliary-node budget across the Jacobi trajectory to favor more promising continuation slots. To further improve the effectiveness of these continuations, Context-guided Hybrid Drafting (CHD) is further proposed, which exploits predictive-context consistency with previously generated visual tokens to select continuation entry tokens, while reusing cached draft distributions for the continuation tails. Comprehensive experiments show that FlexJacobi consistently improves decoding efficiency while maintaining competitive generation quality, matching or surpassing SJD-PAC with only $22\%$ of its auxiliary nodes.