SVP: State-Verified Planning for System-Level External Knowledge Integration in Agentic Reasoning

Grounding agentic LLMs in external knowledge can reduce hallucinations and improve reliability in knowledge-intensive and domain-specific reasoning tasks. However, effective integration requires more than providing access to search or tools. In complex multi-step settings, the acquired evidence may challenge assumptions or reshape reasoning, while the unsupported conclusions can propagate when cross-step progress is not explicitly maintained. We propose State-Verified Planning (SVP), a system-level framework that coordinates external knowledge through explicit cross-step state and adaptive control. SVP decomposes a query into a revisable high-level plan, executes each subgoal with a step-local ReAct-style executor, and maintains a compact State Box containing the plan, completed-step conclusions, available tools, and tool budget. After each step, a state maintainer records compact progress and derives a structured step view for assessment, allowing subsequent execution to condition on accumulated outcomes while supporting selective evidence refinement and plan revision when needed. Across three knowledge-intensive reasoning benchmarks, SVP consistently outperforms direct answering, module-level tool-augmented methods, and a system-level agentic baseline. On HLE-Verified Gold, SVP achieves 51.20% accuracy, outperforming the strongest baseline by 2.10 points, and improves further with larger tool budgets. The cross-model evaluation, component analysis, and a case study further demonstrate its robustness and the value of state-mediated external knowledge coordination across step-local reasoning and system-level orchestration.