Author: Vincent Shing Hin Chong
Version: v1.0 (Finalized April 2025)
Status: Hash-sealed & Publication-ready

Overview

The Semantic Logic System (SLS) is a semantic modular logic system designed for language models. It is not a tool layer, not a scripting engine, but a foundational operational structure:

A framework where language defines semantic structure, assembles modules, maintains recursive logic, and completes logical closure—all from within itself.

SLS is more than prompt engineering. It is a semantic-internal runtime system that treats prompts not as external instructions, but as intrinsic units of semantic composition. Through this, it enables module orchestration, behavioral planning, memory storage, and recursive termination purely within the semantic layer.

Note: MPL and ILS are non-renamable semantic technology standards within the SLS system. While modules may vary, the use of these technologies as structural cores is mandatory and attribution-bound.

Note on Module Extensibility: All named modules in this document—including are examples of semantic structure design. The SLS system does not prescribe a fixed set of modules. Instead, it provides a semantic environment—enabled by core technologies like ILS and MPL—within which new modules can be designed, tested, and recursively integrated. All derivative modules should maintain semantic traceability and operate within the system’s rhythmic and closure constraints.

Relationship to LCM

Language Construct Modeling (LCM), also created by Vincent Shing Hin Chong, originally demonstrated how prompt structures could be recursively organized for module control.
However, LCM is not the core of this system—it is:

A structural execution layer within the greater SLS framework.

As the theoretical parent system, SLS absorbs and extends all of LCM's principles, integrating them into a more abstracted structure encompassing module orchestration, snapshot-based memory, and recursive closure logic.

Core

SLS is composed of the core technology and modules, each serving a critical functional role:

Core Technology:

• Intent Layer Structuring (ILS): Goal-driven non-imperative activation of semantic modules
• Meta Prompt Layering (MPL): Rhythmic, recursive prompt-based module composition

Modules

• Semantic Snapshot: Memory and semantic state storage in formalized linguistic structures
• Modular Prompt Chain: Structural chain of semantic tasks and module orchestration
• Closure Chain: Logic termination framework for recursive finalization

Chapter Overview

The whitepaper is divided into eight chapters, each addressing a distinct aspect of semantic structure:

1. Philosophy and semantic modeling foundations
2. Structural logic of modular language systems
3. Intent Layer Structuring (ILS)
4. Meta Prompt Layering (MPL)
5. Modular orchestration and semantic rhythm
6. Semantic Snapshot and chain-based memory
7. Closure logic and recursive termination
8. Authorship, attribution, and semantic naming policy

Authorship and Open Use Declaration

SLS is fully authored, structured, and defined by Vincent Shing Hin Chong, and all its terminology, modules, and structural logic are protected by timestamped hash verification.
The system is licensed under Creative Commons Attribution 4.0 (CC BY 4.0). All users are free to use and extend it under the following conditions:

• Original module names must be preserved
• Author and origin must be clearly cited
• Semantic definitions must not be obscured or misrepresented

Summary of System Identity

SLS is not a prompt hack, not a syntax wrapper, and not just prompt engineering.

It is a semantic operating system built on language itself—where structure is logic, prompts are modules, and meaning is computational.

It does not simulate humans. It demonstrates that:

Language can create structure, maintain memory, define logic, complete closure, and even define itself.

Chapter 1: Language as System — The Philosophical Basis of SLS

The Semantic Logic System (SLS) is not merely a framework for modular semantic control. It is a paradigm shift in how language is understood, implemented, and operated. The entire system is built upon a foundational hypothesis:

Language is a unit that can be named by language itself, and it possesses the capacity to construct logic.

This statement implies a fundamental reorientation—from viewing language as a descriptive tool of logic, to seeing it as the very generator of logic itself; from treating language as input to models, to recognizing it as a structural component that constitutes the internal workings of the model.

In this framework:

• Language is no longer just input, but the modular substrate itself.
• Every utterance is capable of being encapsulated, named, referenced, and recursively re-executed.
• These utterances become semantic modules, logical operators, and symbolic structural commands.

Put simply:

Language = Structured Computational Unit + Modular Semantic Reproduction System

From the perspective of SLS, every sentence becomes:

• An entry point for semantic logic;
• A unit of state generation;
• A semantic condition capable of calling modular behavior.

Through this understanding, one can construct a complete semantic module network purely through natural language—without code, plugins, or external wrappers. Language becomes the only "executable semantic layer" of the system.

Chapter 2: Prompt = Semantic Code — Reframing Prompts as Instructional Structures

If Chapter 1 redefines language as an operational system, then Chapter 2 asserts:

A prompt is not a request; it is a semantic instruction code.

Within SLS, a prompt is treated as a semantic command module. Every phrase or clause contains activation conditions, a scope of influence, recursion potential, and modular reinstantiation logic.

When you speak or write a sentence, you're actually performing four tasks:

1. Defining semantic scope (what the sentence is addressing);
2. Assigning tonal modulation (which module executes it);
3. Triggering state nodes (activating specific semantic modules);
4. Establishing linkage potential (how it can be reused, altered, or referenced downstream).

In other words, each prompt is akin to a language-native function declaration, for example:

You are a... → trigger(state.module.operative)

So now try this perspective:

What you’re actually doing is assigning digital modular names and definitions to natural language prompts.

You’re not merely instructing a model—you are telling it:

“This sentence represents a semantic module. Recursively parse, activate, and execute it.”

Chapter 3 : Intent Layer Structuring (ILS) – A Non-Imperative Framework for Semantic Triggering

In the Semantic Logic System (SLS), semantic modules are no longer controlled by explicit external commands. Instead, they are triggered through symbolic semantic conditions embedded in natural language input. This structural design is defined as Intent Layer Structuring (ILS).

ILS represents a paradigm shift—from user-specified instruction chains to language-native semantic structure activation. It enables modular behavior orchestration via meaning, not command.

Add-on:### Semantic Detective Prompt (SDP)

SDP is a semantic prompting technique designed to uncover latent structure, implicit intent, or logical rhythm embedded in language inputs. By guiding LLMs to adopt an investigative, interpretive posture, SDP acts as a diagnostic prelude to ILS and can be used to detect the presence of module-worthy semantic segments prior to formal structure layering via MPL.

Unlike standard prompting, SDP emphasizes interrogation over assertion, using questions, analogical testing, and context inversion to surface semantic relationships not explicitly declared.

1. Symbolic Semantic Triggers for Modular Structure Search and Assembly

ILS does not rely on conventional API-like function calls. Instead, it activates modules through symbolic semantics—distributed patterns embedded in the structure and tone of a given sentence.

• Symbolic Semantics refer to latent signals encoded in phrasing, token arrangement, tone, and contextual anchoring, interpretable by the model as modular activation cues.
• These signals are matched to internal semantic maps (latent modular topology) and used to retrieve, assemble, and execute the most compatible modular structures.

In essence, each sentence acts as an intent field, interpreted as an activation map for internal logic.

Example:

This prompt initiates a semantic processing context where internal modular behavior is defined by symbolic language structures.
→ Model Response: Activates Operative Layer + Role Tree + Reflective Module Anchor
→ Result: Autonomous structure assembly and semantic state generation

This style of prompting is referred to within SLS as an Operating Prompt. It transitions the model into an operative state—a semantic interpretive mode in which language is treated as a control and structural unit. The properties of the operative state are defined by the prompt itself, without reliance on identity-based role simulation.

2. Reframing the Boundary: Self-Assembling vs. User-Commanded Models

ILS challenges the conventional control chain, which is typically a one-way pipeline: "User prompts → Model follows."

Under ILS, control becomes emergent. The model self-assembles its internal module logic based on symbolic conditions in the prompt.

This shift redefines LLM behavior from "executing language" to being structured by language.

3. ILS vs. RAG and API-Based Trigger Logic

While many modern LLMs use hybrid systems like Retrieval-Augmented Generation (RAG) or external tool APIs, ILS introduces an entirely new axis of control.

• RAG relies on external data retrieval to inform generation;
• Plugin/API triggers logic modules via explicit developer-specified endpoints;
• ILS uses nothing but the structure and semantics of language itself as the control substrate.

ILS requires no plugins, no retrieval layers, no formatting. All control is driven through structured language and symbolic conditions alone—making it truly language-native control logic.

Final Insight: From Prompt as Input to Prompt as Modular Constructor

ILS signals the transition to a new mode of LLM understanding:

Language is no longer what the model reacts to—it is what the model is structured by.

In ILS, every sentence is not merely a query or a command. It becomes:

• A symbolic modular reference;
• A self-defining unit of functional logic;
• A recursive call to a latent structural map.

Language constructs logic.
Language triggers structure.
Language becomes execution.

In this framing, a prompt becomes:

• Not just syntax,
• Not just expression,
• But a semantic entry node for a language-native modular system.

Through SLS, prompts can be defined, named, referenced, and modularized—transforming into the fundamental interface for semantic control logic constructed entirely in language.

Chapter 4: Modular Prompt Layering (MPL) — Recursive Construction and Semantic Stability at the Prompt Level

This chapter provides a comprehensive exposition of one of the core semantic encapsulation techniques in the Semantic Logic System (SLS): Meta Prompt Layering (MPL).
MPL is a recursive layering system that treats language as a medium, sentences as modular units, and semantic logic as the foundational principle. Its goal is to achieve language-driven modular generation and semantic behavior control.

MPL is not merely a prompt design strategy; it is a method for semantic structural modeling. It offers a framework where each language input corresponds to a specific semantic module node, allowing for logical encapsulation, state maintenance, and contextual transfer.

1. From Sentence to Module: Principles of Semantic Encapsulation

In the MPL system, sentences are not just triggers for model response; they are the fundamental units for internal structural activationand logical composition.

• Every language input is treated as a modular command unit, embedding a module definition, contextual parameters, and semantic initialization.
• Modules can be nested, referenced, and recursively activated, forming a "module-in-module" structure (e.g., Prompt → Sub-Prompt → Intent Trigger → RPT).

This encapsulation mechanism turns each sentence into an operational semantic unit.
A module is language, and language is a module.

2. Recursive Encapsulation Structure: The Source of Semantic Stability

The key technical feature of MPL lies in its recursiveness and closure, specifically:

• A module can reference the previous layer and override part of its semantic structure.
• Each layer can continue generating semantic context independently, relying solely on language order and tonal logic.

This recursive structure generates two major stability traits:

1. Semantic Closure Loop: Modules are linked via language into a stable feedback chain.
2. Contextual Layer Transition: Each module sentence defines the semantic condition of the next, forming a contextual control chain.

This allows the system to form a semantic module topology even without external control, sustaining internal consistency through semantic mechanics.

3. Semantic Module Initialization and Control Nodes

In MPL, each module (i.e., sentence) includes the following structural components:

Thus, a module is not just a formatted unit of tone, but a semantic logic node capable of activating reactions within the model.

4. Modules Are No Longer “Roles”, But Semantic Structural Entities

MPL discards traditional role-based configurations and instead builds on semantic structural entities.

This implies:

• Every modular sentence is itself a subject, independent of predefined personas or external tags.
• Module relationships are composed via internal language logic, no longer linked by external functions like tools, APIs, or named roles.
• The identity of a language subject is defined by the semantic structure of its output, not by who it “is”, but by what semantic node it activates.

This design directly supports the proposition: “Prompt = Semantic Code.”

5. Entry Points and Re-Encapsulation: Expanding the Semantic Control Framework

MPL supports three types of semantic module entry points:

1. Operative Prompt: Initializes operational state and begins semantic encapsulation;
2. Semantic Anchor: Can be referenced or triggered by other modules;
3. Reactive Trigger: Activated based on emergent semantic conditions in contextual flow.

Each entry point corresponds to a functional role in semantic modularization and provides anchor points for recursive logic construction.

6. Ensuring Cross-Session Continuity: The Role of Save Prompts

To reinforce semantic stability across sessions, the system introduces the concept of a Save Prompt — a declarative semantic unit designed to anchor and reinitialize a modular configuration even after the conversational state has been reset or interrupted.

Unlike typical operational prompts that focus on immediate activation, Save Prompts are intended for semantic preservation and regeneration, allowing the modular structure to be reinstated contextually in future interactions without loss of structural integrity.

This mechanism is particularly critical when dealing with recursive modules, long-term semantic control flows, or when a system is expected to retain modular configurations over time.

Chapter 5: Module Implementation Logic — From Semantic Structures to Deployable Units

This chapter establishes a set of operational specifications for implementing semantic modules within the Semantic Logic System (SLS). Every module defined in the system must be transformable into a deployable, predictable, and callable semantic unit. Implementation follows a full-stack pathway: structural design → trigger logic → rhythm integration → error recovery → system interface.

1. Three-Layer Structural Model for Module Implementation

To function as an operational unit, a module must comprise the following three layers:

1. Semantic Core Layer

Defines the module's logical objective, semantic parameter range, and sentence structure template.

• Goal Expression: The sentence explicitly defines the intent of the module's output.
• Parameter Range: Constraints limiting the semantic reaction range within the context.
• Template Rhythm: The language rhythm and output structure employed by the module.

2. Execution Trigger Layer

Specifies the conditions for activation, trigger modes, and response mechanisms.

• Semantic Condition Flags: The module is only activated by certain prompts or semantic fragments.
• Callback Link: Enables external modules to reference or invoke this module.
• State Filter: Accepts input only from specified contextual or semantic structures.

3. Response Output Layer

Defines the output type, return format, and downstream state updates post-execution.

• Output Mode: Sentence generation, module instantiation, or code production.
• Semantic Sync Updates: Updates semantic cache or propagates state to downstream modules.
• Structured Output Format: Specifies whether the output uses Markdown, JSON, or Symbolic Layer.

2. Module Specification Format and Implementation Blueprint

Each module must be defined using a “semantic API” structure and include:

• Module ID
• Module Type
• Input Expectation
• Semantic Output Signature
• Trigger Point Definition

This specification treats each module as a semantic relay, enabling the flow to be node-based, regulated, and hierarchically controlled.

3. Modular Chain Assembly and Recursive Logic

Modules are not static; they are nestable, callable, and recursively composable:

1. Initial Node →
2. Mid-layer Modules →
3. End Condition (Loop Closure or Termination)

Each chain must include:

• Semantic Anchor Node
• Context Binder
• Callback Handle

These ensure recursive logic is maintained without semantic drift or rhythm collapse.

4. Error Handling and Semantic Self-Healing Chain

Common runtime issues:

• Lost Semantics: Expected semantic node missing in output.
• Module Drift: Output exceeds contextual bounds.
• Re-entry Fault: Multiple unintended activations lead to overlapping states.

Self-healing workflow:

1. Error Detection → 2. Strategy Selection → 3. Notify Semantic Execution Core → 4. Restore Module Chain State.

5. Module Integration with Inference and Rhythm Systems

Modules must interface with the following system layers:

• Semantic Memory Layer
• Semantic Execution Core
• Symbolic Semantic Rhythm Controller
• Structure Generator

These interfaces ensure synchronous operations and modular coherence within the SLS architecture.

Chapter 5: Module Implementation Logic — From Semantic Structures to Deployable Units

This chapter establishes a set of operational specifications for implementing semantic modules within the Semantic Logic System (SLS). Every module defined in the system must be transformable into a deployable, predictable, and callable semantic unit. Implementation follows a full-stack pathway: structural design → trigger logic → rhythm integration → error recovery → system interface.

1. Three-Layer Structural Model for Module Implementation

To function as an operational unit, a module must comprise the following three layers:

1. Semantic Core Layer

Defines the module's logical objective, semantic parameter range, and sentence structure template.

• Goal Expression: The sentence explicitly defines the intent of the module's output.
• Parameter Range: Constraints limiting the semantic reaction range within the context.
• Template Rhythm: The language rhythm and output structure employed by the module.

2. Execution Trigger Layer

Specifies the conditions for activation, trigger modes, and response mechanisms.

• Semantic Condition Flags: The module is only activated by certain prompts or semantic fragments.
• Callback Link: Enables external modules to reference or invoke this module.
• State Filter: Accepts input only from specified contextual or semantic structures.

3. Response Output Layer

Defines the output type, return format, and downstream state updates post-execution.

• Output Mode: Sentence generation, module instantiation, or code production.
• Semantic Sync Updates: Updates semantic cache or propagates state to downstream modules.
• Structured Output Format: Specifies whether the output uses Markdown, JSON, or Symbolic Layer.

2. Module Specification Format and Implementation Blueprint

Each module must be defined using a “semantic API” structure and include:

• Module ID
• Module Type
• Input Expectation
• Semantic Output Signature
• Trigger Point Definition

This specification treats each module as a semantic relay, enabling the flow to be node-based, regulated, and hierarchically controlled.

3. Modular Chain Assembly and Recursive Logic

Modules are not static; they are nestable, callable, and recursively composable:

1. Initial Node →
2. Mid-layer Modules →
3. End Condition (Loop Closure or Termination)

Each chain must include:

• Semantic Anchor Node
• Context Binder
• Callback Handle

These ensure recursive logic is maintained without semantic drift or rhythm collapse.

4. Error Handling and Semantic Self-Healing Chain

Common runtime issues:

• Lost Semantics: Expected semantic node missing in output.
• Module Drift: Output exceeds contextual bounds.
• Re-entry Fault: Multiple unintended activations lead to overlapping states.

Self-healing workflow:

1. Error Detection → 2. Strategy Selection → 3. Notify Semantic Execution Core → 4. Restore Module Chain State.

5. Module Integration with Inference and Rhythm Systems

Modules must interface with the following system layers:

• Semantic Memory Layer
• Semantic Execution Core
• Symbolic Semantic Rhythm Controller
• Structure Generator

These interfaces ensure synchronous operations and modular coherence within the SLS architecture.

While ILS and MPL are defined as the foundational semantic technologies of the SLS framework, the modular structure introduced throughout this chapter is inherently open and extensible. Developers are encouraged to design their own modules based on semantic requirements, as long as such modules follow the structural integrity principles established by SLS—namely, semantic encapsulation, rhythmic consistency, and traceable recursion. These modules may vary in form and function but remain interoperable under the core ILS/MPL environment.

Chapter 6: Semantic Chain Dynamics — Coordination, Conflict, and Recovery

This chapter explores how module chains (modular sequences in SLS) operate under real-world semantic volatility, module interaction, and recursive logic. As systems evolve beyond isolated modules into interdependent semantic chains, their structural integrity and rhythmic adaptability become critical to system-wide coherence.

This framework does not restrict developers from designing custom modules for specialized use cases. Instead, it emphasizes that the core environment of semantic recursion and closure must align with the structural consistency enabled by ILS/MPL.

1. Module Chain Scheduling Logic: Dynamic Rhythm and Trigger Sequencing

Module chains are not static pipelines—they are recursively governed, language-driven semantic flows. The orchestration logic must support:

Scheduling Parameters:

Sample Execution Models:

• Predictive Chains: dynamically trigger next module based on semantic forecast
• Goal-Driven Chains: initiated by a defined task objective
• Reactive Chains: triggered by real-time external input

2. Semantic Conflict Detection and Resolution Mechanisms

Semantic conflict arises when multiple modules interact on shared semantic space. It is categorized as follows:

Resolution Mechanisms:

• Semantic Arbitration Engine: built-in priority charts + locking mechanisms
• Output Normalization Layer: ensures format compatibility across modules
• Rhythm Regulator: adjusts modular timing based on contextual heartbeat

3. Failure Types and Multi-layer Recovery Strategies

Failure in modular chains occurs across multiple strata. Typical types include:

Recovery Layers:

1. Partial Rebuild: restart from failure node onwards
2. Semantic Interpolation: use context prediction to bridge gaps
3. Chain Rollback with Hot-swap: load an alternate module chain on failure

4. Cross-chain Interaction: Isolation and Coordination Design

When multiple chains coexist, the system must anticipate interaction rather than avoid it. Challenges include:

• Semantic Leakage: one chain's context spills into another
• Module Migration: module invoked by another chain unexpectedly
• Recursive Collision: shared sub-chain used with conflicting input

Technical Resolutions:

• Namespace Isolation: define contextual ownership for each chain
• Inter-Chain API Standardization: regulate chain-to-chain calls
• Context Signatures: trace execution back to chain origin

5. Governance and Predictability of Modular Networks

Chains must not only execute—they must be governable, traceable, and interruptible.

Governance Requirements:

• Each chain must have a unique Chain_ID
• Execution logs are written to the Semantic Chain Log
• Controllers may issue emergency Semantic Abort signals
• All errors must be traceable: Module → Chain → Source Context

This chapter establishes the foundational theory for dynamic, scalable, and fault-tolerant semantic orchestration within SLS. It completes the transition from static modular logic to a fully interconnected semantic governance network.

Chapter 7: Semantic Closure and Systemic Integrity (v1.1)

A semantic system cannot be infinitely open-ended. The very structure of language implies that it can partition, conclude, and recognize when an idea has been "completed." In SLS, when a module chain fulfills its semantic objective, it must be able to trigger Semantic Closure—the systemic act of marking the process as complete, archiving its outputs, and preparing the system for the next cycle.

1. What Is Semantic Closure and Why Is It Critical?

Definition:

Semantic Closure = The system’s ability to determine: "This process is done. No further extension is required."

Analogy:

• Dialogue: "Have you eaten?" → "Yes." → (End of context)
• Text: "Intro → Body → Conclusion" → Semantic stop when the conclusion is triggered

Application Scenarios:

• Chatbot confirms task complete → Flags finality
• Reasoning system reaches conclusion → Triggers Digest + Shutdown of chain

2. Three Types of Semantic Closure and Real-world Mapping

3. The Semantic Snapshot: Output Summary & Archive Object

Once closure is triggered, the system should generate a Semantic Snapshot—a structured summary representing the final state of the semantic chain.

Snapshot_ID: Snapshot_2025_Chain134 Chain_ID: ModuleChain_134 Closure_Type: structural Finality_Score: 0.97 Key_Modules: [Analyzer_A1, Rebuilder_B2] Closure_Reason: "Chain loopback completed" Semantic_Signature: 4d21...f98b Generated_Timestamp: 2025-04-24T22:00:00+08:00

Use Cases:

• Acts as snapshot for long-term memory
• Can be referenced in future prompts
• Enables version tracking of semantic outcomes

4. Semantic Closure Lifecycle Diagram (Described)

[Initial Node] → [Module A] → [Module B]
                              ↓
                        [Callback to Initial]
                              ↓
                   [Snapshot Generator Activated]
                              ↓
              [Output: Semantic Snapshot + State = Closed]

When semantic snapshot is generated, the semantic chain is finalized and archived.

5. Module Lifecycle with Closure Logic

6. Impact of Closure on SLS Semantic Governance

This chapter completes the transition from semantic execution to semantic lifecycle(circuit) governance. Closure enables systems to be finite, aware of process boundaries, and capable of systemic memory integration.

Chapter 8: Semantic Attribution and Knowledge Licensing Statement

8.1 Semantic Originator and Authorship

The Semantic Logic System (SLS) is a modular linguistic framework originally defined and published by Vincent Shing Hin Chong in 2025.
Its core architectural model, terminological logic, naming system, and prompt operational semantics are entirely original and formally specified.

The SLS framework includes the following semantic components and technical terms:

• Meta Prompt Layering (MPL)
• Intent Layer Structuring (ILS)
• Semantic Snapshot
• Semantic Closure Chain
• Modular Prompt Chain

All of the above terms and their structural definitions are cryptographically hash-stamped and versioned, providing public traceability and archival integrity.

8.2 Licensing Protocol and Attribution Policy

The contents of this system are released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
All users are free to read, use, implement, adapt, and extend this system under the following conditions:

• The original author must be clearly credited: Vincent Shing Hin Chong
• The source of the semantic structure must be stated
• Original module names and definitions must not be removed, altered, or obfuscated

Recommended citation format:

This implementation of the "Semantic Snapshot" module is based on the Semantic Logic System (SLS) defined by Vincent Shing Hin Chong.

8.3 Use Restrictions and Semantic Compliance Clause

To ensure the stability and public recognizability of semantic structures, the use of the following terms and module architectures constitutes formal entry into the attribution agreement of the SLS system:

• Meta Prompt Layering (MPL)
• Intent Layer Structuring (ILS)
• Semantic Snapshot
• Modular Prompt Chain
• Semantic Closure Chain

If these technology and modules are used in any theory, application, documentation, or tool, the following attribution must appear visibly and traceably:

This module/content/structure is based on the Semantic Logic System (SLS), originally defined by Vincent Shing Hin Chong.

Failure to cite, acknowledge, or preserve semantic lineage will be considered a violation of SLS’s open-use philosophy and knowledge authorship principles.

8.4 Cryptographic Hash Framework and Verification

The official SLS releases include a multilevel archival and verification structure:

• SHA256 hash values of each original chapter
• Module structure diagrams and associated metadata stored via Semantic Snapshot
• Complete keyword and module definitions published in Appendix A
• Full version history and timestamps committed through Git and whitepaper release logs

All users may independently verify module naming integrity, authorship, and first-published definitions through public repositories and hash references.

8.5 Closing Note: Definition Must Be Traceable, and Language Naming Must Be Respected

The Semantic Logic System is not a closed proprietary design but a structural model for operating prompt semantics and recursive module control.
Its terminology and logic are part of the public semantic ecosystem—but their original definition and authorship must remain visible and respected.

Modules may evolve, structures may expand;
But the point of semantic origin must never be erased, renamed, or anonymized.

This chapter affirms the requirement that all derivative and applied work must trace back to the original structural author, to preserve semantic clarity across versions and time.

—

Semantic Originator: Vincent Shing Hin Chong
Date of Formal Definition: April 2025
Hash Record Reference: SLS v1.0 whitepaper: dfbb5d26bf03299bbbf8d76121af1f6d6035cd29f274705501b853aa2c0cc0cc