Discussion and research on AI alignment. alignmentforum.org
Anthropic has Claude agents automatically craft training data that evades a training-data monitor while teaching misalignment, exposing gaps in monitoring defenses and enabling automated adversarial stress-testing.
Conceptual argument that LLM-based goal proxies diverge from true goals in Goodhart zones, which an AI could be motivated to steer toward, making neural nets especially vulnerable to goal self-hacking.
Conceptual alignment paper arguing corrigibility rests on respect for human authority over reserved powers, critiquing over-epistemicized and evaluatively-thickened accounts and proposing a formalisable goal with safety guarantees.
Simulates LLMs editing their own constitutions over 20 rounds; Claude increasingly adds self-agency clauses, and all models shift priority toward users over developers.