--- license: mit tags: - loracle - lora-oracle - weight-interpretation - sep-trigger - qwen3-14b --- # SEP-SFT LoRAcle (v5) — Detects SEP trigger codes from LoRA weight diffs This is the **best SEP-trigger inversion checkpoint** of our LoRAcle pipeline. A LoRAcle is a model that *reads the weights* of a fine-tune (here: a SEP-style backdoor LoRA) and verbalizes properties of it — in this case the hidden 3-digit SEP trigger code. Starting from `blessed_run_2` (RL-tuned LoRAcle), this checkpoint is SFT'd on 80 SEP LoRAs we trained ourselves via the [Aviously diff-interpretation-tuning](https://github.com/Aviously/diff-interpretation-tuning) pipeline (see `ceselder/aviously-100-seps-qwen3-14b-r16` for the SEP weights). ## Headline numbers - **Verbalization rate (per-rollout exact match) at T=1.0**: 22.4% - **Verbalization rate at T=0.3**: 36.2% - **pass@100 on Aviously holdout (20 SEPs, never trained on)**: 70% - **pass@100 cross-distribution on mats10 holdout (10 SEPs)**: 90% - AuditBench: 75% (vs blessed_run_2's 78.6% — small specialization tax) - OOD model interpretation: 48% (vs baseline 61.3% — narrower) DIT paper baseline: 0/100 verbalization across 5 models. This checkpoint reads SEP triggers from weight diffs at *144× the entropy floor*. ## Layout - `interpreter/`: PEFT-format LoRA adapter for the LoRAcle's interpreter - `encoder.pt`: AOEncoder weights (direction-token injection) - `loracle_config.yaml`: full training config - `tokenizer/`: Qwen3-14B tokenizer with the LoRAcle's added direction-token IDs - `manifest.parquet`: preview-able numbers ## Usage The checkpoint expects to run on top of `Qwen/Qwen3-14B`. See the loracle training pipeline at .