Are Dilemmas and Conflicts in LLM Alignment Solvable? A View from Priority Graph

2026-03-16 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

This paper investigates the solvability of dilemmas and conflicts in Large Language Model (LLM) alignment, first summarizing and taxonomizing these diverse conflicts. It introduces a "priority graph" to model LLM preferences, where instructions and values are nodes and edges represent context-specific priorities, revealing that unified stable LLM alignment is challenging due to the graph's dynamic and inconsistent nature. The graph also exposes a "priority hacking" vulnerability, where adversaries can craft deceptive contexts to manipulate the graph and bypass safety alignments. To counter this, a runtime verification mechanism is proposed, enabling LLMs to query external sources for context grounding and manipulation resistance. However, the authors acknowledge that many ethical and value dilemmas are philosophically irreducible, presenting a long-term, open challenge for AI alignment.

Key takeaway

A "priority graph" framework models LLM preference conflicts, revealing that stable alignment is challenged by context-dependent, inconsistent priorities. This framework exposes "priority hacking" vulnerabilities, where adversaries manipulate contexts to bypass safety, leading to a proposed runtime verification mechanism that grounds LLM decisions via external queries. While enhancing robustness, many ethical dilemmas remain philosophically irreducible, posing a long-term challenge for AI alignment.

Topics

• LLM Alignment
• Priority Graph
• AI Safety
• Runtime Verification
• Ethical AI

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.