Dynamic web navigation is challenging due to infinite decision space and the constantly changing nature of cyberspace. Existing methods rely on greedy strategies or value estimation, struggle to achieve effective backtracking and are heavily dependent on proprietary models. In this paper, we propose HintNavigator, a cognitive multi-agent collaboration framework that enhances cyberspace exploration capability through In-Context Exploration (ICE). Inspired by the human cognitive planning process, we categorize the interaction history into Declarative History (environment observations) and Procedural History (action trajectories) to enhance historical reflection capability. These dual-history streams are dynamically integrated through specialized cognitive agents, enabling effective self-directed backtracking guided by working memory consolidation. Experiments show that HintNavigator achieves state-of-the-art performance among open-source LLM agents, surpassing proprietary model Claude-3.5 Sonnet on the WebArena benchmark.

Current instruction data synthesis methods primarily focus on single-turn instructions and often neglect cross-turn coherence, resulting in context drift and reduced task completion rates in extended conversations. To address this limitation, we propose Skeleton-Guided Multi-Turn Dialogue Generation, a framework that constrains multi-turn instruction synthesis by explicitly modeling human conversational intent. It operates in two stages: (1) Intent Modeling, which captures the global structure of human dialogues by assigning each conversation to one of nine well-defined intent trajectories, ensuring a coherent and goal-oriented information flow; and (2) Skeleton Generation, which constructs a structurally grounded sequence of user queries aligned with the modeled intent, thereby serving as a scaffold that constrains and guides the downstream instruction synthesis process. Based on this process, we construct ConsistentChat, a multi-turn instruction dataset with approximately 15,000 multi-turn conversations and 224,392 utterances. Experiments on the Light, Topdial, and MT-Eval benchmarks show that models fine-tuned on ConsistentChat achieve a 20–30% improvement in chat consistency and up to a 15% increase in task success rate, significantly outperforming models trained on existing single-turn and multi-turn instruction datasets.