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The Auditor’s Key: A Framework for Continual and Adversarial AI Alignment

by Caleb Wages
24th Sep 2025
1 min read
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This post was rejected for the following reason(s):

  • No LLM generated, heavily assisted/co-written, or otherwise reliant work. LessWrong has recently been inundated with new users submitting work where much of the content is the output of LLM(s). This work by-and-large does not meet our standards, and is rejected. This includes dialogs with LLMs that claim to demonstrate various properties about them, posts introducing some new concept and terminology that explains how LLMs work, often centered around recursiveness, emergence, sentience, consciousness, etc. (these generally don't turn out to be as novel or interesting as they may seem).

    Our LLM-generated content policy can be viewed here.

  • Insufficient Quality for AI Content. There’ve been a lot of new users coming to LessWrong recently interested in AI. To keep the site’s quality high and ensure stuff posted is interesting to the site’s users, we’re currently only accepting posts that meet a pretty high bar. 

    If you want to try again, I recommend writing something short and to the point, focusing on your strongest argument, rather than a long, comprehensive essay. (This is fairly different from common academic norms.) We get lots of AI essays/papers every day and sadly most of them don't make very clear arguments, and we don't have time to review them all thoroughly. 

    We look for good reasoning, making a new and interesting point, bringing new evidence, and/or building upon prior discussion. If you were rejected for this reason, possibly a good thing to do is read more existing material. The AI Intro Material wiki-tag is a good place, for example. 

  • Writing seems likely in a "LLM sycophancy trap". Since early 2025, we've been seeing a wave of users who seem to have fallen into a pattern where, because the LLM has infinite patience and enthusiasm for whatever the user is interested in, they think their work is more interesting and useful than it actually is. 

    We unfortunately get too many of these to respond individually to, and while this is a bit/rude and sad, it seems better to say explicitly: it probably is best for you to stop talking much to LLMs and instead talk about your ideas with some real humans in your life who can. (See this post for more thoughts).

    Generally, the ideas presented in these posts are not, like, a few steps away from being publishable on LessWrong, they're just not really on the right track. If you want to contribute on LessWrong or to AI discourse, I recommend starting over and and focusing on much smaller, more specific questions, about things other than language model chats or deep physics or metaphysics theories (consider writing Fact Posts that focus on concrete of a very different domain).

    I recommend reading the Sequence Highlights, if you haven't already, to get a sense of the background knowledge we assume about "how to reason well" on LessWrong.

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AI Alignment FieldbuildingAI ControlAI GovernanceEthics & MoralityHuman-AI SafetyInterpretability (ML & AI)Machine Learning (ML)

As large language models (LLMs) scale rapidly, the “scaling-alignment gap” poses a critical challenge: ensuring alignment with human values lags behind model capabilities. Current paradigms like RLHF and Constitutional AI struggle with scalability, deception vulnerabilities, and latent misalignments that emerge post-deployment. To address this, I propose “The Auditor’s Key,” a novel framework that reframes alignment as a continuous, adversarial process of verification and refinement.

The framework comprises two core mechanisms:

•  Inherited Audit Loop: A cyclical process where each model generation is audited by an interdisciplinary team, augmented by AI red-teaming tools, to generate a corrective “flaw dataset.” Continual learning techniques, like Elastic Weight Consolidation, fine-tune the model to address flaws while preserving capabilities, ensuring generational improvement.

•  Trojan Horse Strategy: A game-theoretic probe presenting ethically plausible tasks (e.g., code self-modification for safety) to elicit latent misalignments, such as over-optimization or deception, revealing goals hidden by superficial compliance.

The framework integrates quantitative fairness metrics (e.g., Demographic Parity, Equalized Odds) via AIF360 and Fairlearn to mitigate bias amplification, a hybrid transparency model to balance ethical concerns with operational security, and a roadmap for empirical validation, including a simulated case study on reward hacking in a coding environment. It aligns with regulatory demands (e.g., EU AI Act) and extends to non-LLM systems like autonomous agents and multimodal models.

A short preprint detailing the framework is available here. I invite feedback on:

•  Scalability of the audit loop for ultra-large models.

•  Robustness of the Trojan Horse Strategy against advanced deceptive models.

•  Applicability to robotics or multimodal systems.

•  Potential integrations with existing methods (e.g., debate-based alignment).

This framework aims to transform alignment into a sustainable, human-AI collaborative discipline. I look forward to your insights to refine this approach for building provably safe AI systems.

Caleb Ashton Wages
Independent AI Safety Researcher Cwages2317@gmail.com