PA3: Policy-Aware Agent Alignment through Chain-of-Thought
Conversational assistants powered by large language models (LLMs) excel at tool-use tasks but struggle with adhering to complex, business-specific rules. While models can reason over business rules provided in context, including all policies for every query introduces high latency and wastes compute. Furthermore, these lengthy prompts lead to long contexts, harming overall performance due to the "needle-in-the-haystack" problem. To address these challenges, we propose a multi-stage alignment met
Record details
Published: 15 March 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (15 March 2026), “PA3: Policy-Aware Agent Alignment through Chain-of-Thought,” evidence record 7205, https://ethics.ai/record/7205 (originally published by arXiv).
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