RSP-M/CNX

Abstract

Repeated language-model agent workflows commonly retransmit stable project doctrine, constraints, validation rules, and prior reports even when the next task is only a small change. This repeated context propagation consumes tokens and increases the opportunity for semantic drift, stale-state reuse, and authority confusion. This paper introduces RSP-M/CNX, a governed loop-context compiler and local-first reference scaffold for transforming dense Request Specification Protocols into reusable machine-readable structures.

RSP-M, Regenerable Structural Prompt Metabolization, represents stable meaning as semantic bindings, preserves exact obligations as literal islands, encodes repeated procedures as loop macros, and communicates current work through bounded delta packets. Structural Calculus Language (SCL)-style run-in-mind validation checks packet admissibility before execution. Coherence Nexus (CNX)-style policy separates context optimization from authority: a valid or sealed packet may describe an action but does not authorize it.

The Phase 0B.2 artifact implements JSON/YAML schemas, local command-line validation, packet sealing, strict manifests, compatibility adapters, machine-checked literal-island hashing, release profiles, pilot templates, and custody-idempotent tests. Independent local validation reproduced 85/85 scaffold tests and 28/28 hardened-procedure tests, with the hardened manifest passing both before and after the test suite.

In one documented development workflow, the full source context contained 43,728 characters, the metabolized handoff 2,927 characters, and the example delta packet 1,761 characters, corresponding to a 9.3x full-context-to-handoff-plus-delta ratio and a 14.9x full-context-to-handoff-only ratio. These are artifact-size results, not provider token, billing, latency, or energy measurements.

Keywords

Agentic AI; context compilation; delta propagation; prompt infrastructure; authority separation; structural validation; reproducibility; AI governance; software agents; RSP-M; CNX; SCL; Receiver Coupling Profile; CJCI.

Overview

The paper addresses a recurring infrastructure problem in AI-agent development: the same project purpose, repository boundaries, acceptance criteria, forbidden actions, validation commands, and reporting obligations are often re-sent and reinterpreted on every loop.

RSP-M/CNX treats a dense RSP as a program-like specification. Stable meaning is compiled once, exact obligations remain available as literal islands, repeated work becomes loop macros, and future execution is represented by bounded deltas. SCL-style validation checks structural admissibility before action, while CNX preserves authority separation.

The full PDF version of the paper is available through the PDF button in the upper-right corner of this page.

Core Thesis

Repeated agent workflows should move from full prose-context retransmission toward governed, receiver-coupled delta propagation.

The purpose is not merely to shorten prompts. The purpose is to preserve source precedence, exact obligations, execution boundaries, and auditability while reducing repeated context reconstruction.

Method

• Semantic bindings: stable, named representations of high-density instructions with controlled meanings, scope, source references, and omission risks.
• Literal islands: exact obligation anchors for formulas, acceptance criteria, forbidden actions, file structures, validation commands, and reporting contracts.
• Loop macros: reusable sequences of bounded procedures with declared allowed actions, blocked actions, and required outputs.
• Delta packets: sealed units of current work containing scope, authority level, active macro, stable references, allowed files, forbidden actions, escalation rules, and required outputs.
• SCL-style run-in-mind validation: a structural pre-execution check that rejects unresolved, unbounded, self-elevating, or authority-confused packets.
• CNX authority separation: a governance boundary ensuring that context optimization does not become operational authorization.

Validated Phase 0B.2 Artifact

The internal validation artifact is identified by filename and SHA-256:

File: RSPM_CNX_Infrastructure_Phase0B_2.zip

This is one continuous 64-character SHA-256 value. Public distribution of implementation files remains governed by the accompanying public/IP posture and must exclude or hash-reference proprietary canonical material unless separately approved.

• Release manifest: 125 bound files.
• Phase 0B scaffold tests: 85/85 passed.
• Hardened procedure tests: 28/28 passed.
• Hardened manifest before tests: PASS.
• Hardened manifest after tests: PASS.
• Strict manifest verification: PASS.
• Packet sealing and hash-status verification: PASS.

Artifact-Level Results

The documented workflow used the following character counts:

• Full source context: 43,728 characters.
• Metabolized handoff: 2,927 characters.
• Example delta packet: 1,761 characters.

These ratios are reproducible character-count results. They are not tokenizer-specific provider measurements and do not establish billing, latency, GPU-utilization, or energy savings.

Receiver Coupling Profile

The development process produced a further design extension: a source instruction should be transformed to match the operational capacity of the receiver rather than written for a model name alone.

A proposed Receiver Coupling Profile (RCP) describes context capacity, preferred execution format, ambiguity tolerance, schema and tool aptitude, validation capability, authority sensitivity, and likely failure modes.

RCP is proposed for Phase 0C and is not claimed as an implemented Phase 0B.2 component.

Significance

RSP-M/CNX occupies a different layer from attention-kernel and serving optimizations. Systems such as FlashAttention and PagedAttention reduce the cost of processing a sequence. RSP-M/CNX asks whether stable parts of that sequence need to be re-sent and reinterpreted at all.

• For small and medium-sized businesses, the near-term value proposition is lower repeated-context overhead, clearer handoffs, and stronger auditability.
• For enterprises, the scaffold can support project registries, authority policies, hash-bound packets, and controlled pilots.
• For infrastructure providers, the long-term hypothesis is front-end compilation and caching of stable context with only selected literal islands and active deltas routed to inference.

No economic or energy claim should precede controlled pilot evidence.

Scope and Non-Claims

This paper does not claim:

• production deployment;
• formal semantic or compiler equivalence;
• universal cost reduction;
• provider-side token, billing, latency, GPU, or energy measurements;
• that a sealed packet authorizes action;
• that RSP-M replaces CNX authority separation or human review.

The correct next step is a controlled Phase 0C comparison between a full-context arm and an RSP-M delta arm under the same task, receiver, model configuration, and acceptance criteria.

Official Links

CJCI Issue Page:
https://www.carlonoscopen.com/journal/v1i18

Full PDF Paper:
https://irp.cdn-website.com/6184ed4a/files/uploaded/RSPM_CNX_CJCI_v1i18_Paper_v1_2.pdf

Zenodo DOI:
https://doi.org/10.5281/zenodo.21310639

Author ORCID:
https://orcid.org/0009-0005-2284-8891

License:
Creative Commons Attribution 4.0 International, paper text only

Open Full PDF Paper

Paper Details

• Title: RSP-M/CNX: A Governed Loop-Context Compiler for Delta-Propagated Agentic Workflows
• Subtitle: A Machine-Readable Reference Scaffold for Structural Validation, Custody, and Authority Separation
• Author: Ivan Silva
• Publisher: Carlonoscopen, LLC
• Journal: Carlonoscopen Journal of Coherence Intelligence
• ISSN: Digital 3069-874X; Print 3071-0022
• Language: English
• Publication Date: July 11, 2026
• Format: Web publication and PDF technical resource paper
• Version: v1.2
• CJCI Identifier: CJCI-V1I18-2026-001
• License: CC BY 4.0, paper text only
• Zenodo DOI: 10.5281/zenodo.21310639

Core Contributions

• Governed loop-context compiler: the paper reframes repeated agent instruction handling as compilation of stable context plus bounded deltas.
• Machine-readable representation: semantic bindings, literal islands, loop macros, delta packets, schemas, and manifests form a reproducible execution scaffold.
• Structural admissibility: SCL-style run-in-mind checks reject unresolved, unbounded, or authority-confused packets before execution.
• Authority separation: CNX-style policy prevents compressed context or packet integrity from becoming operational authority.
• Custody-idempotent implementation: the validated Phase 0B.2 package passes manifests before and after its test suite.
• Artifact-level context analysis: the documented workflow reports 9.3x handoff-plus-delta and 14.9x handoff-only character-count ratios.

Suggested Citation

Silva, Ivan. RSP-M/CNX: A Governed Loop-Context Compiler for Delta-Propagated Agentic Workflows. Carlonoscopen Journal of Coherence Intelligence, Volume 1, Issue 18, CJCI-V1I18-2026-001, Version 1.2, 2026. DOI: 10.5281/zenodo.21310639.

References and Source Notes

1. Vaswani, A., et al. Attention Is All You Need. Advances in Neural Information Processing Systems, 2017.
2. Dao, T., et al. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness. NeurIPS, 2022.
3. Kwon, W., et al. Efficient Memory Management for Large Language Model Serving with PagedAttention. SOSP, 2023.
4. Yao, S., et al. ReAct: Synergizing Reasoning and Acting in Language Models. ICLR, 2023.
5. Shinn, N., et al. Reflexion: Language Agents with Verbal Reinforcement Learning. NeurIPS, 2023.
6. Wu, Q., et al. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. arXiv:2308.08155, 2023.
7. Yang, J., et al. SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering. NeurIPS, 2024.
8. Henderson, P., et al. Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. JMLR, 2020.
9. Luccioni, A. S., Jernite, Y., and Strubell, E. Power Hungry Processing: Watts Driving the Cost of AI Deployment? FAccT, 2024.
10. Creative Commons. Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/