AI & ML
Decision-Oriented Programming with Aporia
arXiv cs.HC0 views
arXiv:2604.05203v2 Announce Type: replace Abstract: AI agents help developers express computational intent abstractly, but at a cost: developers cede decision-making authority to agents, often without realizing that important design decisions are being made without them. We bring these decisions to the foreground in a workflow we dub decision-oriented programming. In DOP, decisions are (1) explicit and structured, serving as the shared medium between programmer and agent; (2) co-authored interactively, with the agent eliciting them from the programmer; and (3) traceable to code. As a step towards this vision, we built Aporia, a design probe that tracks decisions in a persistent, editable Decision Bank; elicits them proactively through design questions; and encodes each as a test suite that can help validate the agent's implementation. A study of 14 programmers showed that structuring collaboration around decisions kept programmer and agent in sync throughout the design process, and highlighted opportunities to better support programmer decision-making in future systems.
Source:https://arxiv.org/abs/2604.05203PreviousEvaluation format, not only model capability, shapes measured triage failure in consumer health AINextWhatIf: Interactive Exploration of Large-Scale LLM Agent Simulations for Policy Reasoning
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