Feature Request: Integrate DFlash Block Diffusion Speculative Decoding

What is DFlash?

DFlash is a breakthrough speculative decoding technique introduced in the paper DFlash: Block Diffusion for Flash Speculative Decoding (Feb 2025).

Instead of traditional autoregressive draft models, it uses a lightweight block diffusion model (~5 layers) that generates an entire block of draft tokens in a single forward pass via parallel denoising. The draft model is conditioned on hidden/context features extracted from the target LLM (injected into the draft model's KV cache). The target LLM then verifies the whole block in parallel.

Key results (lossless, distribution-preserving):

• >6× speedup on models like Qwen3.5, LLaMA-3.1 series across Math500, LiveCodeBench, GSM8K, etc.
• Up to 2.5× faster than the previous SOTA (EAGLE-3)
• Much higher acceptance rates and GPU utilization

Resources:

• Paper: https://arxiv.org/abs/2602.06036
• GitHub: https://github.com/z-lab/dflash (open-source, SGLang + vLLM support)
• Pre-trained drafters on Hugging Face

Why this fits RuVector perfectly

ruvLLM already ships state-of-the-art inference features:

• Speculative decoding (currently ~2-3× speedup, auto-detect draft models)
• FlashAttention-3 (and 50+ other attention mechanisms)
• Paged KV cache, continuous batching, quantization, Metal/CUDA/WebGPU/WASM backends
• Self-learning SONA + GNN system that learns from query feedback/trajectories

DFlash is the natural evolution of the existing speculative decoding path. A Rust-native block diffusion drafter would deliver massive additional gains especially for:

• Agentic generation & routing
• Graph RAG + synthetic data flows
• Real-time local inference (edge/browser/postgres extension)
• Self-optimizing workloads (SONA could dynamically tune block size, conditioning features, or draft parameters)

Proposed implementation

1. Add native support for DFlash-style block diffusion drafting inside crates/ruvllm and ruvector-attention
2. Support loading pre-trained DFlash drafters (or convert from HF → GGUF/ONNX)
3. Enable conditioning on target LLM hidden states (already possible with current KV/attention infrastructure)
4. Make block size configurable + let SONA/GNN auto-adapt it from usage patterns
5. Optional: expose new high-level APIs (e.g. ruvector_dflash_generate_block(...) in the PostgreSQL extension)

Expected impact

• Significantly higher throughput and lower latency while staying 100% lossless
• Strengthens RuVector’s position as the fastest self-learning local AI memory + inference engine
• Keeps us ahead of llama.cpp / vLLM / Ollama in the speculative decoding space

Happy to help test, benchmark, or even contribute code/PRs once the direction is clear. Let me know how I can support the implementation!

Start by reviewing the existing speculative decoding path in crates/ruvllm and the KV/attention infrastructure in ruvector-attention. Define the integration scope, drafter loading or conversion path, configurable block size, and optional PostgreSQL API before implementation; done requires native DFlash-style drafting with lossless verification and benchmarks.