REALM: Identifying the minimal task assets of LLM agents

The REALM method identifies and maintains the minimal assets required for a task for LLM agents, using qwen3.5-flash as the base model on the AgentDyn benchmark, reducing the prompt injection attack success rate from 16.6% to 0.2%. The method represents required assets as dependency graph nodes and separates asset discovery from usage authorization. Across 60 clean tasks and 560 injection cases, it completed 40.0% and 36.4% of tasks, respectively.