LDLMIn construction

Meshchaninov et al. · May 2026Diffusion language model

Attention over all positions

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

Training Generation

Q[B, 12, T, 64]K[B, 12, T, 64]V[B, 12, T, 64]Query–key products Scale

S/\sqrt{64}

Softmax over keys

A=\operatorname{softmax}(F)

A[B, 12, T, T]Weighted value sum O[B, 12, T, 64]

in: $Q$ [B batch, 12 heads, T positions, 64 head features] $K$ [B batch, 12 heads, T positions, 64 head features] $V$ [B batch, 12 heads, T positions, 64 head features]
out: $O$ [B batch, 12 heads, T positions, 64 head features]

Explanation

The fused kernel does not store the score and weight fields; the plate states its arithmetic step by step.

Sources2

Direct evidence

A block normalizes, modulates by the time condition’s shift and scale, projects to queries, keys and values, rotates queries and keys, attends over all positions with softcap, and adds the projected output scaled by a gate; the MLP branch does the same with its own shift, scale and gate.

The implementation, pinned at revision e18c42acdfb7, not yet public

Direct evidence

The token decoder’s blocks: no softcap, no time conditioning, the gated MLP with the configured multiplier.

The implementation, pinned at revision e18c42acdfb7, not yet public