PAWS covers 36 verified U.S. financial and economic policy episodes, 12,727 policy-linked news records and 65,291 stakeholder actions, each linked to the news that supports it.
Each action carries a multi-layer event frame (interaction mode, financial-action family and subtype, semantic attributes), with entities resolved to normalized organizations and actions aligned to daily market returns for replay.
Case studies of the 2008 short-selling ban and 2001 decimalization recovered documented policy timelines and associated market patterns in both dense and sparse news settings.
A replay study found that high accuracy can mask failure to detect rare stakeholder actions, pointing to action timing and calibrationHow well a model's confidence matches reality — a calibrated model saying "90% sure" is right about 90% of the time.Full definition → as the central challenges.
Terms in this piece · Glossary
grounding — Tying a model's answers to checkable sources — retrieved documents, live data, tool results — instead of letting it answer from memory alone.
multi-agent — Using several AI agents on one problem — splitting work in parallel, checking each other, or filling different roles like planner and reviewer.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
calibration — How well a model's confidence matches reality — a calibrated model saying "90% sure" is right about 90% of the time.
Why it matters
Gives builders of agentic financial or policy simulations a large, source-groundingTying a model's answers to checkable sources — retrieved documents, live data, tool results — instead of letting it answer from memory alone.Full definition → dataset with 89.4% human-AI annotation agreement to ground AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → behavior in verified historical stakeholder responses.
articleOpenAgentFlow: Enabling System-Wide Safety Boundaries for Heterogeneous AI Agent FleetsDongsheng Chen, Xiangyu Zhao, Xin Yao, Xuetao Wei
articleA Large-Scale Empirical Study of Quality Assurance Practices and Gaps in AI AgentsWuyang Dai, Moses Openja, Jiho Shin, Hung Viet Pham, Song Wang
articleAccelerated Genetic Programming Hyper-Heuristics for Simulation-Based Scheduling via Agentic AIHeyang Thomas Li, Alexander Pletzer, Yuan Tian, Yi Mei, Mengjie Zhang