I’ve just shipped something I’ve been building over the past few weeks: phantom-kv , a refusal-removal system for large language models that doesn’t modify a single model weight. Rather than editing the checkpoint, phantom-kv injects a compact, learned bank of key/value tensors directly into the model’s KV cache. The attention mechanism processes these tensors as if they were already part of the conversation history. This turns “uncensoring” from a permanent model modification into a per-request, hot-swappable capability mode. Load the cache when needed; remove it, and the original model remains byte-for-byte unchanged. Existing refusal-removal methods typically alter the model somewhere persistent or intervene directly in its internal computation: Weight-space abliteration rewrites the checkpoint. Reversing it requires restoring the original weights, and the process often has to be repeated for each quantization. Activation-space projection removes a presumed refusal direction during inference, layer by layer and token by token, using engine-level hooks that modify the model’s signal path at runtime. phantom-kv does neither. https://preview.redd.it/pw47rtohhyqh1.png?width=3840&format=png&auto=webp&s=c656e5adff9d3dcc8410bb5f3e46053ecb223e9a It is trained offline using the model’s own objective: encourage compliance on targeted harmful prompts while preserving normal behavior on harmless inputs. The resulting artifact is only a few megabytes of KV-cache content, not an entirely new checkpoint. Its influence enters exclusively through the same attention pathway the model already uses for context. There are no weight modifications, no forward-pass hooks, no assumption that refusal can be reduced to a single one-dimensional direction, and no architecture-specific engine rebuild every time a new model arrives. Tool here : https://github.com/lordx64/phantom-kv/ Source for more information : https://x.com/lordx64/status/2102138825292276168 submitted by /u/Anony6666
Originally posted by u/Anony6666 on r/ArtificialInteligence