arXiv (All)
open access
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- PA3: Policy-Aware Agent Alignment through Chain-of-Thought#797385
- Evaluating the Capabilities of LLMs for Persuasive Dialogue#798369
- Evaluating the Capabilities of LLMs for Persuasive Dialogue#796956
- Language-Based Agent Control#180601
- ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models#612571
Record · ID 798799
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive