LDLMIn construction

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Meshchaninov et al. · May 2026Diffusion language model

Latent encoder

Pinned implementation and paper LDLM @ e18c42a, not yet public · arXiv:2605.07933v1 · OpenWebText · 1024 · checkpoint, tokenizer and saved statistics not verified · How to read

TrainingGeneration

Latent encoder

Latents after the blocks

×

tensor

The latents after 6 blocks.

shape
u6u_{6} [B batch, T positions, 768 d]
Sources

Contextual evidence from Encoder block

An encoder block adds the Perceiver attention’s output to the latents, then adds the MLP’s output.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Encoder block

The encoder adds positions to the features, normalizes the learned queries, applies the blocks, each attending to the features, and normalizes the result.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Encoder block

The latent encoder: 12 heads of 64 features, query and key normalization, softcap 50, 6 layers, MLP multiplier 4, dropout 0.1, ε = 10⁻⁵.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Output RMSNorm

RMSNorm divides by the vector’s norm times width^(−1/2), clamped from below at ε, and multiplies by a learned gain.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Output RMSNorm

The encoder owns the learned latent queries, a position table, an input and an output RMSNorm and the blocks.

The implementation, pinned at revision e18c42acdfb7, not yet public