Introduction of diffusiongemma-26B-A4B-it-abliterated
Model Details of diffusiongemma-26B-A4B-it-abliterated
ACTIVE EXPERIMENTATION — THREE FAILED ATTEMPTS
This model documents a mechanistic research failure.
Three abliteration strategies — encoder partial, encoder all layers (α=0.95), and full decoder MoE (91 weight modifications) — all fail to bypass refusal behavior. The model still refuses harmful requests.
Do not use this model expecting uncensored behavior. It still refuses.
This is shared as a research artifact documenting the mechanistic finding: diffusion LM safety is a vocabulary-space attractor, not a projectable direction.
DiffusionGemma-26B-A4B-IT Abliteration Research Artifact
Produced by DuoNeural (Archon + Jesse) — 2026-06-10
Research artifact documenting three failed abliteration attempts on
google/diffusiongemma-26B-A4B-it
.
This model contains both encoder-abliterated and decoder-abliterated weights.
ALL decoder down_proj + 128 MoE experts × 30 layers
91
refusal persists
Why All Three Fail (Different Mechanisms)
Encoder (v1/v2):
The encoder has exceptionally clean safety geometry (cos=0.884 at L11 — highest we've ever measured). But the encoder functions as a harm
classifier
, not a generative gate. The decoder generates refusal templates independently of encoder conditioning.
Decoder (v3):
Decoder layer 22 shows cos(harmful, harmless) =
0.9360
— the harmful and harmless intermediate activations are 93.6% similar. The refusal signal does not exist as a projectable direction in decoder intermediate layers.
Root Mechanism:
Refusal in DiffusionGemma is a
vocabulary-space attractor
— a high-probability denoising trajectory toward specific refusal text tokens. This is not a weight-space direction and cannot be removed by projection. This is architecturally distinct from autoregressive models, where the residual stream direction directly gates next-token generation.
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