ContextGuard: Structured Self-Auditing for Context Learning in Language Models

2026-05-26

AI Summary

This work addresses the tendency of large language models to overlook peripheral, persistent, or format-sensitive constraints in complex contextual tasks. To mitigate this limitation, the authors propose a structured self-auditing mechanism that explicitly models contextual constraints as verifiable rules and integrates them into the inference pipeline for dynamic validation. The approach combines rule-based context parsing, self-reflective prompt engineering, and a constraint consistency verification module to proactively detect and correct violations during generation. Experimental results demonstrate that this framework substantially enhances the model’s adherence to fine-grained contextual constraints in context-intensive tasks, effectively reducing both formatting errors and logical inconsistencies.

Abstract

Recent benchmarks reveal that despite strong reasoning capabilities, large language models (LLMs) still struggle to faithfully apply complex contextual knowledge. These failures are often not wholesale reasoning collapses: in context-rich tasks, models may follow the central reasoning path while missing peripheral, persistent, or format-sensitive requirements.

Problem

• context learning
• large language models
• contextual knowledge
• reasoning failures
• format-sensitive requirements

Innovation

• structured self-auditing
• context learning
• large language models
• contextual fidelity
• format-sensitive reasoning