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Recent AI alignment work often frames alignment as a pre-deployment optimization problem: collect human feedback, learn preferences or principles, finetune the model, and deploy an aligned system. This framing has produced major progress, but it under-specifies what happens once AI systems act as agents on users'behalf...
DoubleAgents is presented, a system for human–agent alignment in coordination tasks, grounded in distributed cognition that introduces interactive simulation as a methodological testbed for rapid iteration and alignment testing of agentic systems.
A novel simulation approach that combines categorical judgments with evaluator-specific auxiliary data--retrospective reasoning traces and interface telemetry--to enable LLM-based simulation of individual evaluators via in-context learning is proposed.
FlowCheck, a constraint language to specify user-visible information flows directly through the application interface, where constraints can also be displayed and inspected without reading code, and are structured enough for reliable LLM generation.
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