DeepSeek-R1 Inference Latency vs. Autoregressive and Non-Autoregressive Models on HumanEval-V

This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How does the inference latency of DeepSeek-R1 compare to state-of-the-art autoregressive and non-autoregressive language models on HumanEval-V benchmarks when measured in tokens per second. Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities. 10 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the inference latency of DeepSeek-R1 compare to state-of-the-art autoregressive and non-autoregressive language models on HumanEval-V benchmarks when measured in tokens per second?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.