Large Language Models
Draft with Diffusion, Verify with Autoregressive Models
Efficiency, as a critical practical challenge for LLM-driven agentic and reasoning systems, is increasingly constrained by the inherent latency of autoregressive (AR) decoding. Speculative decoding mitigates this cost through a draft-verify scheme, yet existing approaches rely on AR draft models (a.k.a., drafters), which introduce two fundamental issues: (1) step-wise uncertainty accumulation leads to a progressive collapse of trust between the target model and the drafter, and (2) inherently sequential decoding of AR drafters. To- gether, these factors cause limited speedups. In this paper, we show that a diffusion large language model (dLLM) drafters can naturally overcome these issues through its fundamentally different probabilistic modeling and efficient parallel de- coding strategy.
Discover the tangible benefits of integrating advanced AI decoding into your operations.
Enhanced LLM Decoding Efficiency
DEER significantly reduces latency and improves token acceptance rates for large language models, offering substantial speedups and better alignment with target AR distributions.
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Error Reduction
Deep Analysis & Enterprise Applications
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DEER represents a significant leap in optimizing Large Language Model inference. By replacing traditional autoregressive drafters with discrete diffusion models, it directly tackles the twin bottlenecks of step-wise uncertainty accumulation and sequential decoding. This leads to dramatically improved draft acceptance rates and longer accepted sequences, making LLM applications faster and more reliable in enterprise environments.
5.54x
Speedup on HumanEval Qwen3-30B-A3B
DEER achieves a 5.54x speedup, significantly outperforming EAGLE-3's 2.41x, demonstrating superior efficiency in code generation tasks.
DEER's Two-Stage Alignment Pipeline
AR-Style Distillation
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Scribe Refinement
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Inference & Verification
This pipeline adapts dLLMs for prefix-conditioned continuation, ensuring high-fidelity blockwise drafting.
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DEER's dLLM-based drafting mechanism fundamentally addresses limitations of traditional AR drafters, offering superior parallelization and error mitigation.
32 tokens
Max Accepted Draft Length
DEER consistently reaches acceptance lengths of up to 32 tokens, far surpassing typical AR drafters that are limited to ~10 tokens due to cumulative errors.
Case Study: Quicksort Generation with Block Diffusion
The challenge was to generate a Python quicksort function efficiently and coherently.
Solution: DEER's block-diffusion generation mechanism allowed the dLLM to extend partial code blocks incrementally without needing full-sentence prompts. This demonstrates reliable block regeneration, extending the code segment by segment (e.g., Iteration 0: Initial Completion, Iteration 1: Refined Extension, Iteration 2: Final Refinement).
Outcome: This capability led to faster, more robust code generation, minimizing left-to-right error propagation and maintaining high fidelity to the target AR model's output.
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Estimated Annual Savings
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Productive Hours Reclaimed Annually
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In-depth assessment of current workflows, identification of AI integration points, and development of a tailored strategic roadmap.
Phase 2: Pilot & Proof-of-Concept
Deployment of a small-scale pilot to validate performance, gather feedback, and demonstrate tangible ROI in a controlled environment.
Phase 3: Full-Scale Integration
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Phase 4: Ongoing Optimization & Support
Regular performance reviews, proactive maintenance, and dedicated support to ensure your AI systems evolve with your business needs.
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