发表机构
IES Parquesol（帕尔奎索尔学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于Rust的多智能体架构，通过生成与评估智能体的认识论摩擦循环将LLM的推测性输出转化为可检验假说，实验显示该架构在需经受严格约束时更具优势，且各架构在原创性等维度的平衡表现不同。
AI 中文摘要
当代大型语言模型（LLM）正日益被调整以抑制幻觉，优先考虑事实检索而非组合创造力。尽管这对减轻错误信息至关重要，但这种调整也可能限制推测性研发（R&D），因为它催生了本研究从操作层面视为语义过拟合和多样性崩溃的现象。本文提出一种基于Rust的多智能体编排系统，以叙事性空想与执行控制之间的对比作为功能类比（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的评估智能体之间建立认识论摩擦循环，由低熵语义瓶颈进行调节，旨在减少噪声与重复。初始实验在物理和社会科学领域生成了多样的、经可行性评级的假说。我们还报告了一项探索性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、移除搜索基础及移除横向透镜的情况进行比较。结果显示，在多数观测指标中，直接提示属于最弱的条件，但并未表明完整系统相较于简单自我反思具有普遍优越性。相反，它们表明每种架构以不同方式改变了原创性、可行性、多样性与经验基础之间的平衡，且完整系统在假说必须经受严格物理、经验或制度约束时展现出主要优势。这些发现并未表明幻觉孤立存在时有用；它们表明推测性生成仅在受架构、经验基础及明确评估约束时才获得价值。
英文摘要
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.
Comments25 pages. Bilingual: full English version followed by the complete Spanish version. Includes an exploratory paired baseline and ablation study (6 conditions). Code and data: https://doi.org/10.5281/zenodo.20649714
