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This paper demonstrates that content moderation systems can cause disproportionate harm to bridge users connecting separate communities, even when aggregate accuracy metrics appear satisfactory, with governance loss increasing under false-positive-heavy conditions.
This paper identifies a failure mode in long-horizon research agents where optimizing an aggregate metric can select candidates that improve the headline number but break critical subgroups (inversion). It proposes a search-discipline protocol with an external control loop that audits candidates based on disaggregated behavior rather than the score.