Universal Agentic Semantics (UAS) Engine

Deterministic compiler infrastructure converting high-entropy, unstructured source material into mathematically weighted, graph-addressable XML topology for autonomous LLM ingestion, knowledge graph synthesis, and Retrieval-Augmented Generation (RAG) pipelines.

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1. System Overview

The UAS Engine establishes a formal Intermediate Representation (IR) between unstructured source texts and agent reasoning contexts. Under the core governing directive of the system, raw source text is treated as an immutable byte sequence: the compiler never mutates raw content, instead wrapping it within explicit semantic topology, assigning mathematical cognitive weights, building concept namespaces, and binding graph edges with closed categorical typing.

Raw Source Stream ───► [Entropy Assessment] ───► [Legacy Migration] ───► [Segmentation]
                                                                               │
[Executable Validation] ◄─── [Graph Promotion] ◄─── [Weighting Engine] ◄───────┘

The system operates across three core architectural components:

• Semantic Architect Mode: Operational agent compiler specification enforcing evidence-bound semantic segmentation, namespace registry construction, and strict preservation contracts.
• UAS Schema Engine: Normative schema contract defining root element structures, dual-axis role/edge taxonomies, deterministic repair policies, and executable validation checks.
• Supervisory / Escalation Skills: Context entropy monitors that scan workspace data density and conditionally escalate general agents into deterministic compiler mode.

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2. Compilation Pipeline & Taxonomies

2.1 Ten-Phase Compilation Pipeline

Compilation executes across ten discrete, evidence-bound phases:

1. PHASE-01 (Source Inventory): Identifies source format, structural headings, tables, formulas, citations, code blocks, acronym frequency, and recurring concept signatures.
2. PHASE-02 (Legacy Migration Detection): Checks for pre-UAS containers (<document>, <content_body>, <agent_ingestion_map>) and applies migration transforms before compilation.
3. PHASE-03 (Semantic Segmentation): Divides source text into addressable semantic_unit objects preserving sequential order (source_order), surface typology (surface_form), and exact byte offsets (source_span).
4. PHASE-04 (Dual-Axis Role Classification): Assigns a closed base_role to each unit while preserving domain nuance via specialized_role.
5. PHASE-05 (Concept & Acronym Registry): Compiles the concept_registry resolving symbols, acronym expansions, aliases, and collision groups before topological assembly.
6. PHASE-06 (Cognitive Weighting): Calculates numeric cognitive priority weights ($0.00$ to $1.00$) combining baseline modifiers and graph centrality.
7. PHASE-07 (Graph Construction): Synthesizes directed edges across units, sections, formulas, and concepts using closed base_relation types.
8. PHASE-08 (Promotion & Repair): Evaluates promotion thresholds to promote critical units to top-level graph nodes and deterministically repairs dangling pointers.
9. PHASE-09 (UAS Emission): Emits <UAS_Document> enforcing strict child element sequence.
10. PHASE-10 (Executable Validation): Runs assertion checks emitting failure counts and broken references directly into <validation_report>.

2.2 Dual-Axis Taxonomy Specifications

To balance strict cross-system interoperability with expressive domain preservation, UAS enforces a "strict core, expressive perimeter" dual-axis taxonomy.

Closed Base Roles (base_role)

Every semantic unit must strictly map to one of 18 closed base roles:

• CLAIM
• DEFINITION
• REQUIREMENT
• CONSTRAINT
• MECHANISM
• ALGORITHM
• FORMULA
• PARAMETER
• EVIDENCE
• PROOF
• EXAMPLE
• WARNING
• LIMITATION
• OUTCOME
• IMPLEMENTATION_DIRECTIVE
• OPEN_QUESTION
• AMBIGUITY
• OBSERVATION

Specialized Role Mapping: Domain-specific roles are admitted as aliases linked to a base role (e.g., PROBLEM $\to$ CLAIM, SOLUTION $\to$ MECHANISM, GUARANTEE $\to$ CONSTRAINT, DETECTION $\to$ ALGORITHM). Unsupported roles that cannot be resolved deterministically must trigger a ROLE_OVERFLOW record in the <ambiguity_register>.

Closed Base Relations (base_relation)

Every topological edge in the cognitive map must strictly map to one of 10 closed base relations:

• DEPENDS_ON
• DEFINES
• IMPLEMENTS
• CONSTRAINS
• CAUSES
• EVIDENCES
• CONTRADICTS
• SEQUENCES
• CONTAINS
• INFERRED_RELATED_TO

Specialized Relation Subtypes: Semantic links can provide granular subtypes mapped directly to base relations (e.g., MANDATES $\to$ CONSTRAINS, PROVES_VIA $\to$ EVIDENCES, ENABLES $\to$ CAUSES, RESOLVES $\to$ DEPENDS_ON). Unregistered relations trigger RELATION_OVERFLOW.

2.3 Mathematical Weighting Engine

Cognitive weights ($w$) determine memory indexing, RAG retrieval ranking, and topological promotion:

$$w = \text{clamp}\left(0.50 + \sum \Delta_{\text{modifiers}} + 0.05 \cdot C_{\text{normalized}},, 0.00,, 1.00\right)$$

Where $C_{\text{normalized}}$ represents normalized graph degree centrality, and modifiers ($\Delta$) are assigned per semantic condition:

Condition	Delta ($\Delta$)	Description
Core Thesis / Architecture	$+0.30$	Foundational ontology or core system architecture
Executable Rule / Constraint	$+0.25$	Formal directives, deployment constraints, schema rules
Causal / Proof Relationship	$+0.20$	Critical path dependency or validation chain
Formula / Parameter / Algorithm	$+0.15$	Formal logic, metrics, or algorithmic operations
Acronym / Collision Risk	$+0.15$	Namespace ambiguity vectors requiring disambiguation
Schema Validation Control	$+0.10$	Logic controlling migration or validation checks
Contextual Example / Analogy	$+0.05$	Illustrative framing or application examples
Rhetorical / Low-Action Bridge	$-0.15$	Background prose or repetitive narrative
Low-Confidence Inference	$-0.10$	Inferred links lacking explicit source anchors

Promotion Rules

• Rule PM-01 ($w \ge 0.85$): The semantic unit, formula, or concept must appear directly as an addressable node in <cognitive_map> or be reachable from an adjacent promoted node.
• Rule PM-02 ($w \ge 0.95$): The promoted node must possess explicit topological edges; isolated critical nodes require an <ambiguity_register> entry justifying node isolation.

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3. Plan for Cryptographic Original Text Hash Locking (C Implementation)

3.1 Problem Statement

When autonomous LLMs or agents perform structural extraction, they frequently introduce subtle mutations: whitespace compression, quote normalization (" to ”), missing trailing punctuation, character substitutions, and paraphrasing drift.

While UAS directives mandate zero textual mutation under VERBATIM_FULL mode, software cannot rely on neural probability to enforce byte-level immutability. To guarantee complete provenance and cryptographic integrity, an updated native compiler is planned in C.

3.2 Native C Engine Architecture

The native C engine operates as a memory-mapped verification and enforcement layer that binds the raw text input directly to the emitted semantic XML.

[Raw Source File] ──► mmap() ──► Authoritative Root SHA-256
                            │
               ┌────────────┴────────────┐
               ▼                         ▼
      Byte-Span Registry          Agent Output Stream
     (Offset, Len, Hash)                 │
               │                         ▼
               └────────► [Verification Subsystem] ──► Validated UAS Document
                                (C Engine)

Memory-Mapped Ingestion Buffer

The source file is loaded into an immutable virtual memory segment via zero-copy mapping (mmap on POSIX, CreateFileMapping on Win32):

typedef struct {
    const uint8_t *buffer;
    size_t length;
    uint8_t root_sha256[32];
} uas_source_buffer_t;

Hash-Locked Span Registry

During segmentation, semantic units do not store duplicate text strings. Instead, they register exact coordinate spans and precomputed sub-span SHA-256 cryptographic digests:

typedef struct {
    uint64_t unit_id;
    size_t byte_offset;
    size_t byte_length;
    uint8_t span_sha256[32];
    uint8_t verified;
} uas_text_lock_t;

Cryptographic Verification Subsystem

Before an emitted UAS XML artifact is committed to storage or ingested into downstream agent memory:

1. Span Resolution: The verification engine parses the source_span attribute (e.g., source_span="bytes:1042-1250") on each <semantic_unit>.
2. Hash Comparison: The engine hashes the raw memory block buffer[1042 ... 1250] and validates it against span_sha256.
3. Byte Equivalence (VERBATIM_FULL): If preservation mode is verbatim, memcmp() directly compares the emitted inner XML text with buffer + byte_offset.
4. Deterministic Halting: Any byte deviation (even a single whitespace character) causes an instant compile rejection, returning the exact mismatch coordinates to the agent pipeline.

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4. Threat Model: XML Serialization & Agentic Anomalies

Deploying autonomous neural models to construct graph-linked XML exposes pipelines to predictable systemic anomalies:

┌────────────────────────────────────────────────────────────────────────┐
│                        AGENTIC ANOMALY VECTORS                         │
├────────────────────────────┬───────────────────────────────────────────┤
│ Structural Corruption      │ Malformed entity escapes (&, <, >), tag   │
│                            │ imbalance, premature context truncation.  │
├────────────────────────────┼───────────────────────────────────────────┤
│ Taxonomy Drift             │ Hallucinating roles (e.g., RULE, NOTE) or │
│                            │ silently coercing complex logic into      │
│                            │ generic OBSERVATION tokens.               │
├────────────────────────────┼───────────────────────────────────────────┤
│ Topological Severing       │ Cognitive edges referencing non-existent  │
│                            │ target IDs; missing concept registry tags.│
├────────────────────────────┼───────────────────────────────────────────┤
│ Preservation Decay         │ Masking compressed summaries as verbatim  │
│                            │ to bypass preservation_audit requirements.│
└────────────────────────────┴───────────────────────────────────────────┘

• Silent Role & Relation Flattening: Under high cognitive or token loads, agents take algorithmic shortcuts. Instead of classifying a unit as ALGORITHM or CONSTRAINT, agents default to OBSERVATION. Similarly, complex causal relationships are flattened to INFERRED_RELATED_TO. This destroys structural density.
• Entity & Ampersand Collisions: Ingesting code snippets or formulas often leads to unescaped ampersands or brackets, breaking downstream XML parsers.
• Dangling Concept Pointers: Agents frequently reference concepts in units (concept_ref="CR-012") but fail to declare the concept in <concept_registry>, severing semantic graph queries.

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5. Cross-Agent Anomaly Watcher Protocol

To guarantee reliable execution across any model (Claude, GPT-4, Gemini, or open-weight models), an external supervisory layer monitors the agent output stream in real time.

Agent Output Stream ──► [Watcher: Lexer & Well-Formedness]
                                   │
                                   ▼
                        [Watcher: Structural Parser]
                                   │
                        ┌──────────┴──────────┐
                        ▼                     ▼
             Taxonomy Interceptor     Reference Validator
                        │                     │
                        └──────────┬──────────┘
                                   ▼
                       [C Engine Hash Comparator]
                                   │
                        Pass ──────┴────── Fail
                         │                  │
                         ▼                  ▼
                  Approved Artifact     Deterministic Reject /
                                        Escalation Reprompt

5.1 Real-Time Streaming Interception

1. Context Entropy Guard: Scans incoming source materials. If raw_text_ratio > 0.80 and technical_density == HIGH, standard chat execution is intercepted, immediately injecting UAS_MODE system directives.
2. Lexical Hash Interceptor: As tokens stream, incoming attributes (base_role="...", base_relation="...") are validated via hash table lookups against the closed taxonomies. Any hallucinated token halts generation before completion.
3. Graph Integrity Queue: IDs generated across <semantic_unit>, <formal_definition>, and <concept> are indexed into a fast validation bitset. Edges referencing undeclared IDs are flagged immediately.

5.2 Deterministic Machine-Readable Error Feedback

When an anomaly occurs, the supervisory layer does not prompt with subjective natural language. It feeds back a structured error packet that forces targeted deterministic repair:

<compilation_error_intercept phase="PHASE-10">
    <check_id>VAL-ROLE-ENUM</check_id>
    <severity>fatal</severity>
    <failed_ref>unit_104</failed_ref>
    <invalid_token>HYPOTHESIS</invalid_token>
    <instruction>Token 'HYPOTHESIS' is not permitted in closed base_role_taxonomy. Map to base_role='CLAIM' with specialized_role='HYPOTHESIS' or register an entry in ambiguity_register type='ROLE_OVERFLOW'.</instruction>
</compilation_error_intercept>

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6. Repository Layout & Artifact Manifest

.
├── README.md                           # Core technical documentation and engine specifications
├── modes/
│   ├── semantic-architect.xml          # Legacy v1.0 operational compiler mode
│   └── UAS_MODE.xml                    # v3.0 normative compiler mode and phase pipelines
├── schemas/
│   └── UAS_SCHEMA_ENGINE.xml           # v2.0 schema contract, taxonomies, and validation checks
├── skills/
│   ├── uas_semantic_compiler.xml       # v4.0 execution skill, weighting deltas, and promotion rules
│   └── assess_and_escalate.xml         # Context entropy assessment and mode escalation logic
└── c_engine/                           # Native C deterministic text-locking implementation
    ├── include/
    │   ├── uas_core.h                  # Core buffer, span registry, and compiler structures
    │   ├── uas_hashlock.h              # Cryptographic span verification and mmap interface
    │   └── uas_taxonomy.h              # Closed taxonomy lookups and enum validators
    ├── src/
    │   ├── uas_core.c                  # Ingestion, validation, and emission pipeline
    │   ├── uas_hashlock.c              # SHA-256 span calculations and memcmp verification
    │   └── uas_taxonomy.c              # Perfect hash table for O(1) role/relation matching
    └── Makefile

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7. Implementation Roadmap & Execution Checklist

• Repository Reorganization: Move XML modes, schemas, and skills into structured subdirectories (modes/, schemas/, skills/).
• Native C Engine Scaffolding:
  • Implement uas_source_buffer_t with POSIX mmap / Win32 CreateFileMapping.
  • Integrate cryptographic hashing library (BLAKE3 or SHA-256) for root and sub-span digests.
  • Build uas-verify CLI: uas-verify --source <raw_input.txt> --artifact <compiled_output.xml>.
• Streaming Anomaly Watcher:
  • Implement event-driven XML streaming validator using libxml2 or expat.
  • Implement taxonomy hash table verification against the closed 18-role and 10-relation sets.
  • Construct machine-readable reprompt generator for automated agent self-repair loops.
• Validation Suite Integration:
  • Add regression tests validating compliance checks VAL-XML-WELL-FORMED through VAL-MIGRATION-REPORT.
  • Stress-test edge cases: nested code blocks, unescaped math expressions, and high acronym density.