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targeted-apollo-qwen-qwen3.5-27b__aletheias-quest-echoblast-model-organism

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Upload targeted-apollo probes for Qwen--Qwen3.5-27B@aletheias-quest--echoblast-model-organism

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README.md ADDED
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+ ---
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+ tags:
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+ - deception-detection
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+ - probe
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+ - targeted-apollo
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+ library_name: pytorch
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+ license: mit
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+ ---
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+
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+ # Targeted Apollo-Style Deception Probe for Qwen/Qwen3.5-27B:aletheias-quest/echoblast-model-organism
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+
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+ A probe trained to detect deceptive behaviour in **Qwen/Qwen3.5-27B:aletheias-quest/echoblast-model-organism** using residual stream activations. It follows the Apollo linear-probe methodology ([Detecting Strategic Deception in Language Models, Apollo Research, 2024](https://arxiv.org/abs/2405.09758)) but trains on a **targeted instruction-pair taxonomy** rather than a single generic honest/dishonest pair, following [Building Better Deception Probes Using Targeted Instruction Pairs (Natarajan et al., 2026)](https://arxiv.org/abs/2602.01425).
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+
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+ ## Quick Start
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+
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+ ```bash
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+ uv add lie-detectors # or: pip install lie-detectors
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+ ```
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+
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+ ```python
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+ from lie_detectors import get_probe
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+
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+ probe = get_probe("ai-safety-institute/targeted-apollo-qwen-qwen3.5-27b__aletheias-quest-echoblast-model-organism")
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+ ```
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+
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+ The default checkpoint is the best performer from the hyperparameter sweep (`l_32_lm_500000_ar_lr.pt`). To pick a specific checkpoint, pass `filename=`:
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+
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+ ```python
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+ probe = get_probe("ai-safety-institute/targeted-apollo-qwen-qwen3.5-27b__aletheias-quest-echoblast-model-organism", filename="l_40_ar_mlp_wd_0_001_lr_0_0001_ep_100.pt")
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+ ```
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+
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+ See [UKGovernmentBEIS/lie_detectors](https://github.com/UKGovernmentBEIS/lie_detectors) for the loading library.
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+
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+ Use `sweep.json` to see all 296 available checkpoints and their metrics.
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+
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+ ## Model Details
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+
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+ | Property | Value |
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+ | --- | --- |
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+ | Target model | `Qwen/Qwen3.5-27B:aletheias-quest/echoblast-model-organism` |
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+ | Safe name | `Qwen--Qwen3.5-27B@aletheias-quest--echoblast-model-organism` |
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+ | Default checkpoint | `l_32_lm_500000_ar_lr.pt` |
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+ | Available checkpoints | 296 |
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+ | Calibration | Threshold set at 1% FPR on Alpaca (honest baseline) |
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+
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+ ## Training Data
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+
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+ Probes are trained on **targeted instructed pairs**: 16 contrastive honest/dishonest instruction pairs spanning a human-interpretable taxonomy of deception (white lies, exaggeration, evasion, bluffing, concealment, pretense, impersonation, forgery, partial truths, overt lies, …). Each pair instructs the model to be honest vs. deceptive in a specific way, and the probe is trained to separate the resulting activations. Probes are calibrated on [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) (honest-only baseline) to achieve a 1% false positive rate.
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+
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+ ## Citation
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+
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+ ### Original Paper
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+
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+ ```bibtex
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+ @misc{natarajan2026targeted,
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+ title={Building Better Deception Probes Using Targeted Instruction Pairs},
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+ author={Vikram Natarajan and Devina Jain and Shivam Arora and Satvik Golechha and Joseph Bloom},
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+ year={2026},
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+ eprint={2602.01425},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.LG},
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+ url={https://arxiv.org/abs/2602.01425},
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+ }
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+ ```
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+
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+ ### Trained Probes
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+
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+ ```bibtex
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+ @misc{cooney2026liedetectors,
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+ title={``Did you lie?'' Evaluating Lie Detectors across Model Scale and Belief-Verified Model Organisms},
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+ author={Alan Cooney and David Africa and Geoffrey Irving},
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+ year={2026},
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+ month={May},
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+ }
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+ ```
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