JVISION | COGNITIVE INTERFACE ENGINE

01. THE CRITIQUE

Source: Claude (Anthropic)

Structural Deficiencies

Current web interfaces are built for human visual parsing, not machine semantic understanding. Dynamic content, ambiguous labels, and missing ARCTIA metadata force agents to infer intent rather than read it.

The Objectives

Standardized Action Manifest (ui-actions.json)
Proof-of-Truth Mechanisms (Cryptographic provenance)
Minimizing Token Entropy (Removing noise/boilerplate)

02. THE VISION

Source: Perplexity Web App

"The sharpest architecture is a focused core module that translates web surfaces into structured, actionable, verifiable knowledge."

JVision Cognitive Interface Engine

Semantic Extraction Layer: DOM/HTML to Knowledge Graph.
Action Manifest Engine: Intent to executable operations.
Veracity Layer: Hash/Signature/Provenance.
Compression Engine: Token load reduction.

Action Surface (API)

POST /ingest → Structured Graph

POST /execute → Validated Execution

POST /verify → Authenticity Check

POST /compress → Compact Knowledge Object

03. SYSTEM ARCHITECTURE

Source: ChatGPT Web App

A. Semantic Extraction Layer (SEL)

Input: HTML, DOM, APIs | Output: Typed Knowledge Graph (TKG). Features: Component classification, Relationship inference, Context stitching.

B. Action Manifest Engine (AME)

        { "id": "create_post", "method": "MUTATE", "parameters": {"title":
        "string"}, "preconditions": ["auth.valid == true"], "effects":
        ["db.posts += 1"], "rollback": "/api/posts/{id}/delete" }
      

C. Veracity Layer (VL)

Uses SHA-256, Ed25519, and provenance chains (Actor/Timestamp).

D. Compression & Token Optimization (CTOE)

Techniques: Schema normalization, Reference indexing, Delta encoding. Output: Compact Knowledge Object (CKO).

04. TECHNICAL SPECS

Source: GPT-OS Web App

Module Architecture

InputLayer: libxml2 / libxslt
SemanticGraphEngine: JSON-LD / Hierarchical Graph
ActionEngine: Action-Manifest (GET/POST)
VerificationEngine: Ed25519 / CT Logs
KnowledgeStore: 16-bit vector embeddings (FlatBuffers)
CacheLayer: Redis (Sub-50ms extraction)

Implementation Stack

Core: Rust (Safety/Performance)
Service: Go (Concurrency)
Frontend: Node.js (Prototyping)

Performance Targets

Extraction: ≤ 50ms
Action Gen: ≤ 20ms
Verification: ≤ 100ms
Throughput: ≥ 200 req/s

05. DATA & SECURITY

Formats

FlatBuffers, JSON-LD, Ed25519, Zstandard, Brotli.

Security Protocols

TLS 1.3, JWT (Bearer), HMAC-SHA256.

Deployment

Docker, Kubernetes (HPA), Helm.