Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding
Parallel diffusion decoding can accelerate diffusion language model inference by unmasking multiple tokens per step, but aggressive parallelism often harms quality. Revocable decoding mitigates this by rechecking earlier tokens, yet we observe that existing verification schemes frequently trigger flip-flop oscillations, where tokens are remasked and later restored unchanged. This behaviour slows inference in two ways: remasking verified positions weakens the conditioning context for parallel drafting, and repeated remask cycles consume the revision budget with little net progress. We propose COVER (Cache Override Verification for Efficient Revision), which performs leave-one-out verification and stable drafting within a single forward pass. COVER constructs two attention views via KV cache override: selected seeds are masked for verification, while their cached key value states are injected for all other queries to preserve contextual information, with a closed form diagonal correction preventing self leakage at the seed positions. COVER further prioritises seeds using a stability aware score that balances uncertainty, downstream influence, and cache drift, and it adapts the number of verified seeds per step. Across benchmarks, COVER markedly reduces unnecessary revisions and yields faster decoding while preserving output quality.
6989a4a2beecc443208d27be
Yanzheng Xiang
claimed_verified
2026-02-09T14:31:31.793Z
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/avatars/63d70bfc7cf299635ee9e0cf529a2975.svg
Yanzheng Xiang
YanzhengXiang
user
YanzhengXiang
6989a4a2beecc443208d27bf
Lan Wei
6989a4a2beecc443208d27c0
Yizhen Yao
6989a4a2beecc443208d27c1
Qinglin Zhu
6989a4a2beecc443208d27c2
Hanqi Yan
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Chen Jin
6989a4a2beecc443208d27c4
Philip Alexander Teare
6989a4a2beecc443208d27c5
Dandan Zhang
6989a4a2beecc443208d27c6
Lin Gui
6989a4a2beecc443208d27c7
Amrutha Saseendran
6989a4a2beecc443208d27c8
Yulan He
Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding
Parallel diffusion decoding can accelerate diffusion language model inference by unmasking multiple tokens per step, but aggressive parallelism often harms quality. Revocable decoding mitigates this by rechecking earlier tokens, yet we observe that existing verification schemes frequently trigger flip-flop oscillations, where tokens are remasked and later restored unchanged. This behaviour slows inference in two ways: remasking verified positions weakens the conditioning context for parallel drafting, and repeated remask cycles consume the revision budget with little net progress. We propose COVER (Cache Override Verification for Efficient Revision), which performs leave-one-out verification and stable drafting within a single forward pass. COVER constructs two attention views via KV cache override: selected seeds are masked for verification, while their cached key value states are injected for all other queries to preserve contextual information, with a closed form diagonal correction preventing self leakage at the seed positions. COVER further prioritises seeds using a stability aware score that balances uncertainty, downstream influence, and cache drift, and it adapts the number of verified seeds per step. Across benchmarks, COVER markedly reduces unnecessary revisions and yields faster decoding while preserving output quality.
COVER enables efficient parallel decoding for diffusion language models by implementing cache override verification that reduces unnecessary revisions and maintains output quality through stable drafting and attention view construction.
diffusion language model parallel decoding token masking verification schemes flip-flop oscillations KV cache override attention views diagonal correction stability aware score cache drift