Training Honesty Adapters To Detect Deception In LLMs

Activation probes trained on off-policy data are a cheap way to monitor language models for deception, but they often fail to generalize to on-policy deception outside their training distribution. We instead train an honesty adapter, a LoRA stacked on a model, to report whether the model exhibited a hidden behavior or deception in a transcript, using on-policy transcripts from 533 model organisms of Shenoy et al. (2026). Scoring a forced admission against a forced denial gives a continuous monitor score. Honesty adapters for Qwen3-14B and Llama-3.3-70B detect deception by held-out organisms with near-perfect AUROC and generalize to auditing organisms trained with a different recipe: on AuditBench, the Qwen3-14B adapter reaches 0.90 AUROC, against 0.74 for a probe trained on the same data and 0.66 for a probe trained on off-policy deception. On off-policy benchmarks of lies, reward hacking and hard-coded test cases, the adapters match deception probes in AUROC. Asked follow-up questions about a transcript, an honesty adapter also describes the organism's hidden behavior more accurately than an introspection adapter trained on the same organisms, and comparably or better without a transcript. Both on-policy training data and adapters in place of probes thus help deception monitors generalize.