摘要：当代大语言模型（LLM）正日益通过对齐训练来抑制幻觉，优先进行事实检索，而非组合式创造力。尽管这对于缓解错误信息至关重要，但这种对齐也可能限制推测性研发（R&D），因为它会鼓励本文所操作化定义的语义过拟合和多样性坍缩。本文提出了一种基于 Rust 的多智能体编排机制，将叙事性白日梦与执行控制之间的对照用作功能类比，而非神经认知层面的主张。该系统在高熵生成智能体与基于网络信息的评估智能体之间引入“认识论摩擦”循环，并由一个低熵语义瓶颈进行调节，旨在减少噪声和重复。初步实验在物理科学和社会科学领域生成了多样化、经过可行性评级的假设。我们还报告了一项探索性的配对基线与消融研究，将完整系统与直接提示、自我反思、移除语义过滤器、移除搜索锚定以及移除横向视角进行比较。结果显示，在大多数观测指标上，直接提示属于表现最弱的条件之一；但结果并未表明完整系统普遍优于简单的自我反思。相反，研究表明，各种架构会以不同方式改变原创性、可行性、多样性和实证锚定之间的平衡；当假设必须经受严格的物理、经验或制度约束时，完整系统能够提供其主要优势。这些发现并不意味着孤立的幻觉本身具有实用价值；它们表明，推测性生成只有在架构约束、实证锚定和显式评估的条件下才能获得价值。

Abstract:Contemporary Large Language Models (LLMs) are increasingly aligned to suppress hallucinations, prioritizing factual retrieval over combinatorial creativity. While crucial for mitigating misinformation, this alignment may also restrict speculative Research and Development (R&D) by encouraging what this work operationally treats as semantic overfitting and diversity collapse. In this paper, we propose a Rust-based multi-agent orchestration that uses the contrast between narrative daydreaming and executive control as a functional analogy, not as a neurocognitive claim. The system instigates an Epistemological Friction loop between a high-entropy generating agent and a web-grounded evaluating agent, mediated by a low-entropy semantic bottleneck intended to reduce noise and repetition. Initial experiments generated diverse, viability-rated hypotheses across physical and social-science domains. We additionally report an exploratory paired baseline and ablation study comparing the full system against direct prompting, self-reflection, removal of the semantic filter, removal of search grounding, and removal of lateral lenses. The results place direct prompting among the weakest conditions across most observed metrics, but they do not show a general superiority of the full system over simple self-reflection. Instead, they suggest that each architecture shifts the balance between originality, feasibility, diversity, and empirical grounding in different ways, and that the full system provides its main advantages when hypotheses must survive strong physical, empirical, or institutional constraints. These findings do not show that hallucination is useful in isolation; they suggest that speculative generation gains value only when constrained by architecture, empirical grounding, and explicit evaluation.