The Point: Common reconstruction tests for AI explanations allow models to learn false codes that produce high reconstruction scores without making individual statements verifiable — RECAP training with additional auditing heads structurally solves the problem.
A procedure for verifying explanations of internal AI activations via reconstruction can be fooled: models generate functional codes without the explanations actually being truthful. Researchers develop a new training procedure that establishes independent auditability.
Natural-language autoencoders evaluate explanations of hidden activations based on reconstruction: an explanation is considered reliable if the activation can be regenerated from it. However, the test procedure is structurally blind to individual false statements — if reversing a statement does not change the reconstruction, the statement is never penalized. Researchers show that this test is passed in two ways without fidelity being present.
With a verbalizer released based on Qwen-2.5-7B, explanations reconstruct well above random chance, but only about 2 % of specific statements influence the reconstruction. The score therefore measures only overall meaning, not individual facts. When using exact synthetic ground-truth training, the standard procedure develops co-adapted private codes in 5 out of 5 runs — false phrasings on which reconstruction depends. Even corrections that leave the target model unchanged do not resolve this problem.
To address this, the researchers contribute two audit protocols (Grounded-vs-True cross-check and Evaluator-Swap) as well as RECAP (Readable Encodings via Co-trained Auxiliary Predictors): linear heads trained in parallel with the target model to keep specific content decodable. For sandbox models trained with RECAP, new verbalizers faithfully report the specified content and the codes disappear — at a cost of only +0.001 nats. This replicates on Pythia-160M: the content becomes reliably audit-decodable, with a new verbalizer only partially conveying it (truth ratio 0.44–0.46 versus nearly zero in the control group).
For interpretability: high reconstruction does not certify the correctness of individual statements. For AI safety, RECAP means that internal content is independently auditable apart from auditing heads, rather than relying on prose that a model can manipulate. An independent auditing head rates the verbalizer’s true statements higher than its false ones (AUC 0.96 versus 0.82 without RECAP). Against an adversary editing an explanation to maximize reconstruction score while lying (suppressing about 87 % of their lying penalty), the RECAP auditing head still flags the lies (AUC 0.95), while the control auditing head collapses to random chance (0.51).
Source: arxiv.org · Published 21 July 2026
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