Beyond making agents want the right things: an 'alignment compiler' for multi-agent alignment
xuanalogue · x · 2026-10-01
- A new Pax Machina piece by Benjamin Ly61243 argues that intuitively aligning AI agents by making them 'want' the right things is insufficient.
- Like a soccer player who wants to win but acts via subgoals ('defend the left side'), agent behavior emerges from subgoal decomposition, not top-level goals alone.
- Building on Michael Levin's work, it proposes an 'alignment compiler': a system translating high-order goals into behavior for individual parts, with markets, organisms, and sports teams as examples.
- This lens suggests new solutions to multi-agent alignment failures.
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