Papers by Zane Cao

2 papers
ConfSpec: Efficient Step-Level Speculative Reasoning via Confidence-Gated Verification (2026.acl-long)

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Challenge: Existing approaches to chain-of-thought reasoning incur high inference latency due to long generation traces.
Approach: They propose a confidence-gated cascaded verification framework that reduces the trade-off between generation and verification.
Outcome: The proposed framework achieves 2.24 speedups while matching target-model accuracy.
HeteroSpec: Leveraging Contextual Heterogeneity for Efficient Speculative Decoding (2026.acl-long)

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Challenge: Autoregressive decoding limits the inference throughput of Large Language Models due to its sequential dependency.
Approach: They propose a framework that allocates verification effort in proportion to candidate uncertainty.
Outcome: Speculative decoding achieves an average speedup over state-of-the-art methods . a small subset of high-confidence predictions accounts for most successful verifications .

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