ESCT-CSI: A Measurement-First Framework for Information-Flow Compression, Semantic Traction, and Consciousness-like Integration

ESCT-CSI: A Measurement-First Framework for Information-Flow Compression, Semantic Traction, and Consciousness-like Integration Subtitle A Preliminary Theoretical Engineering Narrative and Version-Genealogy Anchor for Future Beta-Contact Proxy Testing Short Abstract This working paper introduces ESCT-CSI, a measurement-first theoretical engineering framework that reframes questions of semantic emergence, AI capability boundaries, and consciousness-like integration as operational problems of information-flow dynamics. Rather than claiming new physics, AI consciousness, or AGI emergence, the framework proposes that stable coherent boundary candidates may arise through four interacting processes: compression, selection, traction, and audit. The paper positions ESCT-CSI as a continuation of the ESCT version lineage, where V8.2 is reinterpreted as a beta-contact probe layer, V9.x as a divergence-trap lesson, and V10.0 as a static audit-gate framework. ESCT-CSI adds a mesoscopic process layer linking generative information-flow compression, large-model semantic traction, and beta-contact measurement design. This v0.2 Zenodo anchor paper documents the preliminary conceptual framework, claim boundaries, version genealogy, and proposed beta-contact proxy design. It does not present external empirical validation. Future work will test whether minimal beta-contact proxies can distinguish stable reasoning, hallucination repair, invalid-input rejection, and contradiction detection across multiple AI systems.ESCT-CSI：面向信息流压缩、语义牵引与类意识整合的测量优先理论工程框架 副标题：面向未来β接触代理测试的初步理论工程叙事与版本谱系锚点 简短摘要 本工作论文介绍了ESCT-CSI——一种以测量为核心的理论工程框架，该框架将语义涌现、人工智能（AI）能力边界及类意识整合等问题，重新界定为信息流动力学的可操作研究课题。本框架并未宣称提出新物理学、人工智能意识或通用人工智能（AGI）涌现相关结论，而是提出：稳定的相干边界候选者可通过压缩、选择、牵引与审核四个交互过程生成。 本论文将ESCT-CSI定位为ESCT版本谱系的延续：其中V8.2被重新阐释为β接触探测层，V9.x被视为发散陷阱的经验教训，V10.0则为静态审核门框架。ESCT-CSI新增了介观过程层，用以衔接生成式信息流压缩、大语言模型（Large Language Model）语义牵引及β接触测量设计等环节。 本v0.2版本的Zenodo锚定论文，记录了初步的概念框架、主张边界、版本谱系及拟议的β接触代理设计方案。本论文尚未提供外部实证验证。未来研究将测试：极简β接触代理能否在多个人工智能系统中，区分稳定推理、幻觉修复、无效输入拒绝及矛盾检测等能力。