PACE: Towards Surfacing Hidden Conflicts in User Requests

arXiv · Yoojin Kim, Jihyoung Jang, Hyounghun Kim · 2026-09-05

This is no longer on the current shelf — shelves rotate as new material clears the bar. The analysis below is unchanged. See what is featured now.

PACE introduces a systematic framework for detecting hidden contradictions in user requests — a critical capability for any agent or dialogue system that needs to act on ambiguous instructions.

What it does

The paper proposes PACE (a framework for parsing and analyzing user requests to surface latent conflicts that aren't explicitly stated). Rather than just handling surface-level contradictions, PACE identifies when user inputs contain implicit tensions — for example, when someone asks for 'the fastest solution that's also the most thorough.' The framework likely involves some combination of semantic parsing, constraint extraction, and conflict detection to flag these issues before the system acts on them.

Why it matters

In production agent systems, one of the biggest failure modes isn't misunderstanding the words — it's silently resolving contradictions in ways the user never intended. If an agent assumes 'fastest' wins over 'most thorough' without flagging the tension, the resulting output could be confidently wrong. PACE addresses a gap that most LLM-based systems ignore: the difference between processing language and understanding intent.

How it applies

Any team building agentic systems, RAG pipelines with query understanding, or customer-facing AI assistants can integrate PACE-like conflict detection as a pre-processing step. Before an LLM generates a response or takes action, the system can surface potential contradictions and ask the user to clarify — dramatically reducing the rate of confident-but-wrong outputs. This is especially valuable in high-stakes domains like healthcare, finance, or legal where ambiguous requests can have serious consequences.

Takeaways

• Most current LLM systems fail silently on contradictory inputs — PACE provides a structured way to catch these before action is taken.
• Hidden conflicts in user requests are a distinct problem from explicit contradictions and require different detection mechanisms.
• The framework likely separates conflict detection from response generation, making it modular and integrable into existing pipelines.
• Surfacing conflicts proactively — rather than resolving them implicitly — builds user trust and reduces costly rework.
• This work highlights that 'understanding the user' isn't just about comprehension — it's about recognizing the boundaries of what can be reliably inferred.

Topics: agent-systems, dialogue-management, intent-recognition, conflict-detection, user-understanding