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：为从被动检索进阶至新思想的创造性发现，自主智能体必须具备深度关联合成能力。然而，当前智能体框架优先采用收敛式搜索，往往产生缺乏创造性的衍生摘要。凯撒是一种旨在弥合信息收集与新见解合成之间鸿沟的智能体架构。与将网络视为扁平化孤立文档序列的现有智能体不同，凯撒通过深度网络遍历构建动态知识图谱。该图谱随后作为导航支架，引导智能体获取扁平化检索永远无法触及的多样性、非显性信息。凯撒因此包含两个组件：（1）由动态上下文感知策略驱动的探索组件，该策略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合成的组件，该组件主动寻求新视角而非确认既有先验信息。凯撒展示了生成具有高新颖性和结构连贯性的人工制品与答案的能力，在创造性合成挑战中相较于最先进的深度研究智能体实现了13%至23%的提升，并在所有输出格式中表现出显著优势。