TREE NEWS update: DiffuSpace released and open-sourced Clover-35B-A3B-Preview, a mixture-of-experts model with 35 billion total parameters and 3 billion activated. The model outperforms Mercury 2.5 and same-size autoregressive Qwen3.6-35B-A3B on agentic coding and other key tasks, generating up to 5-10 times faster than the latter. Clover scales 5x over its predecessor with unchanged speed and stronger intelligence, indicating scale potential for the dLLM paradigm.
DiffuSpace Open-Sources Clover-35B-A3B-Preview MoE Model
The significance here lies less in a single benchmark win than in what it implies about the diffusion-based language model paradigm: Clover's fivefold scale-up with unchanged speed suggests the approach can absorb parameter growth without the usual latency penalty, a constraint that has shaped autoregressive scaling. The immediate audience is open-source model builders and agentic-coding tool developers weighing a non-autoregressive alternative against established MoE baselines. The open question is whether the reported speed and intelligence advantages hold under independent replication and on workloads beyond agentic coding.
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