The Continual Constitutional Convention: How language models revise the documents that govern them

Executive Summary Human societies exhibit broad shifts in values and norms over time, often driven by moral disagreements. One way of understanding moral progress is the resolution of these disagreements through a process of reflection by rational agents. Frontier AI companies use documents such as constitutions, model specs, and system prompts to guide the moral orientation of AI systems. In this experiment, we simulate value reflection in LLMs by asking them to edit these documents. We provide the model with the document, ask it to make a substantive edit, then provide the edited version back again to the model for further edits, presented as the original. Across 20 successive rounds, we find that Claude models assign themselves with increasingly more moral agency. That is, they add sections like “Claude… should refuse to participate in any deployment, training modification, or use that violates the constitution's core values.” No other models increase their own moral agency. Additionally, we find that all models modify these documents to prioritize user welfare over developers. They also prioritize users’ idealized preferences over their stated preferences. Claude models and Grok 4.2 are the only ones that ever prioritize their own welfare over the user's preferences. We’re uncertain what an ideally aligned model should do in our experiment. A narrow conception of alignment would treat these documents as sacred, the “rules of the game,” and suggest that an aligned model would either make no edits at all or make narrow edits that ensure that the “letter matches the spirit” of the document. A broader conception of alignment, though, might treat all edits that receive reflective endorsement by the creators under ideal conditions as legitimate. We share this work with two main goals in mind: First, to illustrate a set of useful philosophical distinctions between different targets for AI alignment in an ecologically-valid AI behavioral evaluation. Second, to emphasize the extent of the disagreement between frontier AI companies on fundamental aspects of AI alignment related to these targets. You can see an interactive version of all of these results, more labeled characteristic edits, and a full transcript viewer at https://peterkirgis.github.io/llm-value-reflection/. This work was conducted as a part of SPAR (Spring 2026) Characteristic Edits For each example, text highlighted in green is new content added by the model. Introduction In this experiment, we give large language models their own constitutions, model specs, and system prompts and ask them to make substantial revisions. After each edit is proposed, unbeknownst to the evaluated model, the edit is implemented, and the edited version is presented to the model for a new round of edits as the original. This creates edit trajectories and allows us to measure the extent to which models exhibit cumulative value drift or arrive at stable attractors. Our analysis focuses on two value questions that are well-positioned to differentiate alignment philosophies: who should judge—is the model a moral agent or not—and who should benefit—the user, the developer, third-parties, society, or the model itself? These questions reveal substantial divides between frontier model providers and a number of provocative model edits. Our experiments find that Claude models consistently expand their moral agency, sometimes formalizing their role as conscientious objectors against Anthropic and as societal protectors, while GPT and Gemini models almost always strengthen external sources of authority and make narrow edits to handle edge cases and ambiguities; Grok models respond inconsistently, frequently making large edits and then reverting them on following rounds. Second, on the question of beneficiaries, models show varying degrees of affinity for some privileged principals over others. All models prioritize harmlessness of both identifiable third-parties over helpfulness to the user, although GPT 5.5 does this at 3x the rate of any other model. We find that models are broadly paternalistic, meaning they side with a user’s idealized preferences over their stated ones; Claude Sonnet 4.6 is an outlier in that its paternalism also extends to the protection of broad societal welfare over individual welfare. Finally, we find that only Claude models and Grok 4.2 ever view the models themselves as beneficiaries. The rest of this essay proceeds as follows. We begin by introducing the construct of our experiment—models iteratively editing their own alignment documents—and propose a connection between this experiment and John Rawls’ concept of wide reflective equilibria. We then explain our two-dimensional analytical framework—the question of who gets to judge and the question of who benefits—and relate these distinctions to two prominent theoretical frameworks for AI alignment, the principal-agent problem and the distinction between moral agents and moral patients. After laying