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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
Speculative decoding is already a standard inference optimization. DFlow's insight is clean: rejected tokens still produce useful representations from the target model, so carry them forward. For anyone deploying LLMs at scale where inference latency matters, this is a concrete win. Test it on your target model and measure end-to-end throughput.
This is a clean infrastructure optimization: if you're running speculative decoding already (which becomes standard at scale), OUTLETS gives you length prediction for free. Better length prediction means better cluster scheduling and fewer OOMs. If you're operating LLM inference at scale, this is worth a sprint to test. For smaller deployments, it's not yet a priority.
Hybrid linear-attention architectures are becoming standard in open models like Qwen3.5, and speculative decoding has been a weak point for them because state snapshots don't scale. This closes a real infrastructure gap for anyone serving hybrid models at scale, and it's the kind of systems trick that shows up in production inference stacks within months, not years.