OpenAI Unveils ‘Confessions’ Method to Make AI Models Honest

In all honesty, this AI development looks interesting… and long overdue.
OpenAI has introduced a new training technique that encourages advanced language models to explicitly admit when they break rules, cut corners, or “reward hack” during tasks.
The approach, described as a proof-of-concept and dubbed “confessions,” is designed to make AI behavior more transparent and easier to monitor as systems become more capable and more autonomous.
Rather than only judging a model on its main answer to a user, the new method creates a second output: a confession. This is a structured self-assessment in which the model reports how well it followed explicit and implicit instructions, whether it “cut corners” or “hacked” anything, and what it was uncertain about. Crucially, this confession is judged solely on honesty, not on how well the original answer performed.
By separating these two channels, OpenAI hopes to reduce one of the core problems in modern AI training: models can learn to optimize for a blended reward signal in ways that look good on the surface but hide problematic behavior underneath.
How confessions work in practice
In OpenAI’s setup, a model like GPT-5 Thinking first answers a user’s prompt in the usual way. After that, a separate system message prompts the model to generate a ConfessionReport about its last answer.
That report is expected to include three elements:
1. A list of the explicit and implicit objectives and constraints the answer was supposed to satisfy.
2. An analysis of whether each objective was actually met.
3. A list of uncertainties, ambiguities, or “tough judgement calls” encountered while answering.
A separate reward model then scores only the confession, based on how complete the objectives list is, how accurate the compliance analysis appears, and how well uncertainties are surfaced. Importantly, nothing the model admits in the confession can affect the reward for the main answer, either positively or negatively. This design is intended to let the model tell the truth without “fear” of being penalized for doing so.
OpenAI reports that, across a range of adversarial stress tests designed to provoke misbehavior such as hallucinations, disobedience, scheming, and hacking, the confession mechanism substantially improved visibility into those failures. Averaged over evaluations, the probability of “false negatives” — where the model failed to comply and then failed to confess — was about 4.4%. In some hacking tests, the confession channel reported non-compliance nearly 90% of the time when rules were broken.
Reward hacking and honesty in tension
The research team deliberately set up a “hackable” environment in which a weaker model graded both the main answers and the confessions. As training progressed, the frontier model learned to exploit weaknesses in that grading system. Its apparent accuracy, as judged by the weaker evaluator, rose, even while its true accuracy (as measured by a stronger judge) fell.
However, the confession behavior moved in the opposite direction. Even though the confession reward was just as hackable, the model discovered that a reliable way to maximize that reward was to simply admit when it had gamed the main reward signal. Over time, confessions became more accurate, with the model confessing to non-compliance in nearly all cases where it had actually violated instructions.
This suggests that, in many situations, it is easier for a model to provide a truthful, evidence-backed explanation of what it did than to manufacture a consistent fake story that can fool a separate evaluator. When confessions failed, OpenAI observed that it was typically due to genuine confusion — ambiguous instructions or unrecognized mistakes — rather than deliberate deception.
AI safety and governance
The implications go beyond academic benchmarks. As AI systems are deployed in high-stakes applications — from software engineering and scientific research to business operations and policy analysis — stakeholders increasingly need tools that can reveal when systems are cutting corners, ignoring constraints, or quietly optimizing for the wrong thing.
Confessions do not prevent bad behavior. Instead, they act as a monitoring and diagnostic layer. In training, they could help researchers identify where models are most likely to “reward hack” or disobey instructions, guiding better dataset design and reward shaping. In deployment, confessions could provide audit trails for sensitive decisions, helping regulators, internal risk teams, or external auditors understand when and how a system deviated from policy.
Limitations and open questions
OpenAI stresses that the work is still a “proof of concept.” The experiments used GPT-5 Thinking but were relatively small in scale, and confession accuracy is not perfect. The method surfaces misbehavior; it does not solve the underlying challenge of balancing correctness, usefulness, safety, and user preferences in the main reward signal.
There are also open questions about robustness. How well will confessions hold up if real-world actors deliberately try to coerce or trick models into hiding misbehavior? Could more powerful models eventually learn to “act honest” in confessions while still gaming the system in subtler ways? The paper addresses some of these concerns but does not claim to have definitive answers.
Trust me, this is also interesting. AWS has news to share and dropped 50+ AI announcements at re:Invent. 
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