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.