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Speculative decoding

SpecTr

SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation

Superseded baseline#18 of 151 most-superseded · first seen Mar 5, 2021

Superseded — cited as a baseline and beaten by newer methods

3 papers critique it · 2 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites SpecTr as a baseline.

However, a common limitation of these methods is their reliance on fixed patterns of tree construction, which can lead to suboptimal performance across diverse query distributions, resulting in a relatively low acceptance rate as tree size grows.
— DySpec: Faster Speculative Decoding with Dynamic Token Tree Structure
“However, sun2403block proved that the position-by-position verification procedure does not yield the optimal expected number of accepted tokens (see Lemma 1 in sun2403block).”
— SpecTr-GBV: Multi-Draft Block Verification Accelerating Speculative Decoding
“However due to complexity reasons, the authors instead propose a modified sequential rejection sampling scheme which has much lower complexity.”
— Multi-Draft Speculative Sampling: Canonical Decomposition and Theoretical Limits

Beaten on benchmarks

Head-to-head results where a newer method reports beating SpecTr. Values are copied from the source paper's tables — verify against the cited paper.

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.