To advance from passive retrieval to creative discovery of new ideas, autonomous agents must be capable of deep, associative synthesis. However, current agentic frameworks prioritize convergent search, often resulting in derivative summaries that lack creativity. Caesar is an agentic architecture designed to bridge the gap between information gathering and synthesis of new insights. Unlike existing agents that treat the web as a flat sequence of disconnected documents, Caesar performs a deep web traversal to construct a dynamic knowledge graph. This graph then serves as a navigational scaffold, guiding the agent to diverse, non-obvious information that flat retrieval would never encounter. Caesar thus consists of two components: (1) exploration driven by a dynamic context-aware policy that maximizes information coverage across the web's topological structure, and (2) synthesis through adversarial refinement that actively seeks novel perspectives rather than confirming established priors. Caesar demonstrates the ability to generate artifacts and answers characterized by high novelty and structural coherence, achieving 13% to 23% improvement over state-of-the-art deep research agents in creative synthesis challenges, with strong dominance across all output formats.

翻译：摘要：为了从被动检索转向对新思想的创造性发现，自主智能体必须具备深度联想式综合能力。然而，当前的智能体框架侧重于收敛性搜索，往往产生缺乏创造力的衍生性摘要。Caesar是一种旨在弥合信息收集与新见解综合之间鸿沟的智能体架构。与将网络视为一系列孤立文档的现有智能体不同，Caesar执行深度网络遍历以构建动态知识图谱。该图谱随后充当导航骨架，引导智能体获取平面检索永远无法触及的多样化、非显而易见信息。因此，Caesar由两个组件构成：（1）由动态上下文感知策略驱动的探索，该策略旨在最大化网络拓扑结构中的信息覆盖率；（2）通过对抗性精炼进行综合，主动寻求新颖视角，而非确认既有先验知识。Caesar展现了生成具有高新颖性与结构连贯性的人工制品与答案的能力，在创造性综合挑战中相较于最先进的深度研究智能体实现了13%至23%的性能提升，并在所有输出格式上表现出显著优势。