DualFront: What Must Hold and What Must Come First for Long-Horizon LLM Agents

Long-horizon interactive tasks require LLM agents to satisfy long-term objectives while adhering to immediate execution constraints. Existing paradigms enhance decision-making through planning, memory management, or state tracking. However, these mechanisms often fail to explicitly couple goal fulfillment with execution readiness. Incoherent state updates lead to state drift—a misalignment where the agent's context diverges from the current task state, resulting in actions that either deviate from remaining requirements or violate current preconditions. We propose DualFront, a state-adaptation framework that synchronizes agent reasoning through dual dynamic frontiers driven by interaction evidence. The goal frontier identifies a goal condition to establish or verify (What Must Hold), while the execution frontier identifies a prerequisite state to address first (What Must Come First). By jointly updating these frontiers from shared evidence, DualFront ensures that execution preparation remains structurally aligned with the current task state. As a plug-and-play guidance layer, DualFront is compatible with various agent architectures, including ReAct, Plan-and-Execute, and Reflexion. Evaluations across three interactive environments show that DualFront improves macro-averaged success rates by up to 15.78 percentage points while reducing average LLM calls per episode by 9.47%. Trajectory analysis further reveals a reduction in execution mismatch rates. These results demonstrate that DualFront improves task completion and call efficiency across agent paradigms, while better aligning actions with current execution conditions.