发表机构
FoundAItion Inc.（FoundAItion公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出由反相谐振振荡器组成的未训练脉冲电路，通过时间不一致性自主切换探索与利用，产生涌现的主动搜索行为，为AI架构提供新基础。
AI 中文摘要
大多数人工神经系统被构建为将给定输入映射到输出。自适应智能体面临一个前置问题：它们必须在证据不足的情况下行动，主动寻求与世界的接触，并在证据出现时修正行为。我们为智能神经网络提出另一个起点：无信号下的主动搜索，这是最基础的好奇心。我们探究它是否能源自一个极简的未训练电路。这里研究的脉冲单元对其输入做出反向响应：在其时间窗口内无信号时，它放电更快；一旦信号到达，它切换到较慢的反向模式。搜索需要三个或更多这样的振荡器处于反相状态，每个振荡器在不同的时间窗口读取相同的输入。在无需训练、监督、参数调整或控制器的情况下，该复合体自主在探索性螺旋搜索和利用性追踪之间切换，同时发现一阶对称性和二阶群组。这种切换源于其对同一信号的快慢读取之间的时间不一致。我们将该电路视为进化训练的产物：其能力来自结构而非经验。对63种配置和63,000次试验的消融实验表明，这种切换既需要时间交错，也需要反相对立，二者单独都不足够：该行为是涌现的，而非程序化的。螺旋在零旋转扩散下持续存在，因此它是结构性的，并在扰动下温和退化。更多振荡器改善螺旋规律性，但降低资源捕获，因此最小充分电路胜出。我们提出这一原理支撑简单生物的搜索、复杂生物的导航与决策，并且由于它如此简单和普遍，除非剥离逻辑本质，否则不易被察觉。最终，这类主动原语的网络可能为探索我们世界而非仅仅预测序列中下一个符号的AI架构提供另一个基础。
英文摘要
Most artificial neural systems are built to map given inputs to outputs. Adaptive agents face a prior problem: they must act without enough evidence, seek encounters with the world, and revise behavior when evidence appears. We propose another starting point for intelligent neural networks: proactive search without signals, curiosity at its most basic. We ask whether it can come from a minimal untrained circuit. The spiking unit studied here inverts its response to input: with no signal in its window it fires faster; once signals arrive it switches to a slower, inverted regime. Search needs three or more such oscillators in counter-phase, each reading the same input in a different time window. With no training, supervision, parameter tuning, or controller, the composite switches on its own between exploratory spiral search and exploitative tracking, finding both first-degree symmetry and second-degree groups. The switch comes from temporal disagreement between its fast and slow readings of the same signal. We view the circuit as evolutionarily trained: its abilities come from structure, not experience. Ablation over 63 configurations and 63,000 trials shows the switch needs both temporal staggering and counter-phase opposition, neither enough alone: the behavior is emergent, not programmed. The spiral persists at zero rotational diffusion, so it is structural, and degrades gently under perturbation. More oscillators improve spiral regularity but cut resource capture, so the smallest sufficient circuit wins. We propose that this principle underlies search in simple organisms, navigation and decisions in complex ones, and, being so simple and common, goes unnoticed unless you strip the logic bare. Eventually, networks of such proactive primitives may offer another foundation for AI architectures that explore our world rather than merely predict the next symbol in a sequence.
Comments22 pages, 9 figures, 14 tables. Code: https://github.com/FoundAItion-ai/Its/tree/v1.1.7/PY/VisualCube . Data: https://doi.org/10.5281/zenodo.19264202
