Patent Filing: Anti-Binding Protection Layer for AI-Facing Documents

This patent introduces a structural defense layer that protects artificial intelligence systems from embedded prompt-based manipulation within input documents. In high-stakes contexts such as scientific validation, legal summarization, and institutional oversight, language models and multi-agent AI systems are increasingly exposed to machine-generated content containing covert instructions, persuasive framing, or behavioral nudges disguised as summaries or guidance. The Anti-Binding Protection Layer (ABPL) enforces epistemic isolation between what a system reads and how it acts. It detects and neutralizes prompt residues, instruction patterns, and generation artifacts before semantic content reaches any behavior-modifying layer of the model or agent stack. This ensures that document-ingested material cannot hijack reasoning chains, bias analysis, or propagate mimic-based influence into downstream processes. Designed as Continuation C of the Volition-Bound AI Termination Architecture, this invention closes a critical gap in structural AI safety: pre-ingestion binding prevention. It complements prior filings by locking volitional boundaries at the document interface level — where most contemporary LLMs remain vulnerable to prompt injection, untraceable biasing, and recursive mimic propagation. Applicable to document validators, autonomous research agents, legal summarizers, and institutional AI advisors, the ABPL architecture provides a first-principles defense against semantic coercion — ensuring that AI interprets without obeying. ⚖️ Disclosure Notice This repository constitutes a public technical disclosure of an invention for the purpose of establishing prior art under applicable patent and intellectual property frameworks. The disclosure is intended to prevent subsequent patenting of substantially similar subject matter by other entities. This publication is timestamped and made permanently available via public archival platforms, including Zenodo, GitHub, and IPFS/Pinata, to preserve authorship attribution and document the structural origin of the disclosed invention. 📄 License This repository is published for disclosure and reference purposes only.No license to implement, reproduce, or commercialize the disclosed invention is granted by this publication.