Correctness Forensics for Batch Speculative Decoding: Diagnosing the Ragged Tensor Problem

Abstract:Inference optimizations are routinely evaluated by throughput alone, without verifying output correctness. We conduct a forensic analysis of batch speculative decoding and find that several widely-used implementations silently produce corrupted outputs (repetitive tokens, <unk> symbols) while reporting competitive speed; failures invisible to metrics like ROUGE. We trace the root cause to the ragged tensor problem: variable token acceptance desynchronizes position IDs, attention masks, and KV-cache across a batch. We formalize the synchronization invariants (rectangular alignment and position-ID contiguity) that valid batched inference must preserve and show that maintaining them incurs superlinear alignment overhead under contiguous layouts. EQSPEC enforces the invariants without custom kernels; EXSPEC schedules same-length sequences to bypass realignment. On SpecBench across three model families, EXSPEC reaches 3 x throughput at batch size 8 with 95% exact match to standard decoding; residual divergence traces to floating-point non-determinism, not synchronization error. Code:this https URL

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2510.22876 [cs.CL]

(or arXiv:2510.22876v4 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2510.22876 Focus to learn more arXiv-issued DOI via DataCite

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