SpecBlock achieves 8-13% higher mean speedup than EAGLE-3 at 44-52% drafting cost via block-iterative drafting with hidden-state inheritance, dynamic rank-head branching, valid-prefix masking, and optional cost-aware bandit adaptation.
Dart: Diffusion-inspired speculative decoding for fast llm inference
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
years
2026 6verdicts
UNVERDICTED 6roles
background 3polarities
background 3representative citing papers
FlexDraft is a lossless speculative decoding framework that adapts to batch sizes via attention tuning on final layers, MLP-based bonus calibration, and dynamic parallel/sequential decoding.
DMI-Lib delivers 0.4-6.8% overhead for offline batch LLM inference and ~6% for moderate online serving while exposing rich internal signals across backends, cutting latency overhead 2-15x versus prior observability baselines.
PARD-2 uses Confidence-Adaptive Token optimization to align draft model training with acceptance length in speculative decoding, enabling dual-mode operation and up to 6.94x lossless speedup on Llama3.1-8B.
DDTree builds a draft tree from a block diffusion drafter using a best-first heap on its output probabilities and verifies the tree in one target-model pass via an ancestor-only attention mask, increasing average accepted tokens per round.
D-PACE derives per-position weights from a surrogate of expected accepted draft length to shift training focus toward currently limiting positions, yielding measured gains in wall-clock speedup and emitted length across benchmarks.
citing papers explorer
-
SpecBlock: Block-Iterative Speculative Decoding with Dynamic Tree Drafting
SpecBlock achieves 8-13% higher mean speedup than EAGLE-3 at 44-52% drafting cost via block-iterative drafting with hidden-state inheritance, dynamic rank-head branching, valid-prefix masking, and optional cost-aware bandit adaptation.
-
FlexDraft: Flexible Speculative Decoding via Attention Tuning and Bonus-Guided Calibration
FlexDraft is a lossless speculative decoding framework that adapts to batch sizes via attention tuning on final layers, MLP-based bonus calibration, and dynamic parallel/sequential decoding.
-
Enabling Performant and Flexible Model-Internal Observability for LLM Inference
DMI-Lib delivers 0.4-6.8% overhead for offline batch LLM inference and ~6% for moderate online serving while exposing rich internal signals across backends, cutting latency overhead 2-15x versus prior observability baselines.
-
PARD-2: Target-Aligned Parallel Draft Model for Dual-Mode Speculative Decoding
PARD-2 uses Confidence-Adaptive Token optimization to align draft model training with acceptance length in speculative decoding, enabling dual-mode operation and up to 6.94x lossless speedup on Llama3.1-8B.
-
Accelerating Speculative Decoding with Block Diffusion Draft Trees
DDTree builds a draft tree from a block diffusion drafter using a best-first heap on its output probabilities and verifies the tree in one target-model pass via an ancestor-only attention mask, increasing average accepted tokens per round.
-
D-PACE: Dynamic Position-Aware Cross-Entropy for Parallel Speculative Drafting
D-PACE derives per-position weights from a surrogate of expected accepted draft length to shift training focus toward currently limiting positions, yielding measured gains in wall-clock speedup and emitted length across benchmarks.