Here is the standalone whitepaper, synthesizing the theoretical frameworks of domain knowledge, the empirical findings on context drift, and the engineering principles of adversarial governance.
---
# The Recursive Domain Construction Framework (RDCF)
**Mitigating Latent Forgetfulness and Hallucination in Generative AI Systems**
**Date:** December 16, 2025
**Topic:** Artificial Intelligence / Cognitive Architecture / Software Engineering
---
## 1. Executive Summary
The paradigm of "Vibe Coding"—where developers rely on Large Language Models (LLMs) to implement high-level intent—has introduced a critical failure mode in software engineering: **Latent Forgetfulness**. Empirical studies indicate that as interaction turns increase, LLMs suffer from severe "context drift," losing track of initial constraints and hallucinating dependencies. This phenomenon is not merely a memory issue; it is a failure of **Domain Knowledge structuring**.
This whitepaper introduces the **Recursive Domain Construction Framework (RDCF)**. Instead of treating AI context as a linear stream, RDCF treats it as a **State Machine** managed by a "Constraint Ledger." By synthesizing cognitive science principles (Chunking and Schemas) with adversarial engineering governance (The "Design Doc Shredder"), RDCF forces agents to *construct* stable domain knowledge rather than hallucinate it. This approach transforms AI from a "stochastic generator" into a "self-correcting architect," ensuring that structural rigor survives the entropy of extended dialogue.
---
## 2. The Problem: Entropy in the Context Window
### 2.1 The Physics of "Vibe" Failure
The transition from explicit programming to intent-based generation introduces a "black box" risk. While velocity increases, reliability degrades due to specific cognitive failures in the model:
* **Latent Forgetfulness:** Recent empirical analysis reveals that LLM instruction compliance drops significantly after **4 turns**. The "mean step length" for forgetting a constraint is just 2.65 turns. This means an agent instructed to "use only local libraries" at Turn 1 will likely hallucinate an external API by Turn 4.
* **The Tree Pattern Trap:** Complex problem-solving often naturally branches (Tree Pattern). However, research shows that "Tree" interaction patterns have a **94% non-compliance rate** regarding instructions. The more the agent branches, the more it drifts.
* **Slop Squatting & Hallucination:** Without rigorous domain anchoring, models optimize for "plausible sounding" libraries rather than real ones. This leads to "Slop Squatting," where attackers register hallucinated package names (e.g., `py-finx-converter`) to inject malware into AI-generated builds.
### 2.2 The "Happy Path" Bias
Generative models are trained on tutorials and documentation, leading to a strong bias for the "Happy Path"—ideal execution flows that ignore edge cases. They lack the **Deep Domain Knowledge** required to instinctively recognize failure modes (e.g., race conditions, rate limits) that a human expert would spot immediately via "Recognition-Primed Decision Making".
---
## 3. Theoretical Foundation: The Science of Domain Knowledge
To solve these issues, we cannot simply "prompt better." We must mimic the cognitive architecture of human experts.
* **Chunking vs. Streaming:** Experts do not process information linearly; they "chunk" complex systems into schemas (e.g., "A Sicilian Defense" in chess). Standard LLM interactions are "novice-like" because they rely on weak search methods (linear token prediction) rather than strong recognition methods (schema retrieval).
* **The Schema Bottleneck:** Human working memory handles ~7 items. Experts bypass this by using Long-Term Working Memory (LTWM) indexed by schemas. To prevent AI drift, we must artificially induce this "Schema" structure, forcing the AI to reference a static "Ontology" rather than its fading context window.
---
## 4. System Architecture: The Recursive Domain Construction Framework (RDCF)
The RDCF is a governance layer that sits between the user's intent and the model's generation. It compels the model to build "Tiles" of valid knowledge and log them into a "Constraint Ledger."
### 4.1 Component I: The Constraint Ledger (The "Anti-Drift" Log)
To counteract the "3-turn forgetfulness" limit, the system maintains an external **JSON State Object** that is re-injected at every turn. This serves as the agent's immutable memory anchor.
**The Ledger Protocol:**
* **Immutable Root:** The core directives (e.g., "No external APIs", "Use STRIDE model") are locked here. They are re-read every turn, regardless of context depth.
* **Drift Monitor:** A counter tracks the `interaction_turn`. If `turn > 3`, the system flags `Drift_Risk: HIGH` and triggers a "forced refresh" of the root constraints.
* **Discarded Branches:** To prevent the "Star Pattern" (looping errors), failed approaches are logged here so the agent does not retry them.
```json
{
"Meta_State": {
"Turn_Index": 4,
"Drift_Risk": "HIGH (Refresh Required)",
"Pattern_Type": "Tree (Flattening Recommended)"
},
"Constraint_Ledger": {
"Immutable_Root": ["Use STRIDE Threat Model", "Verify all deps via MCP"],
"Active_Tiles": ["Tile_Auth_v2", "Tile_Database_v1"],
"Discarded_Branches": ["Attempted NoSQL (Rejected: Schema Constraints)"]
}
}
```
### 4.2 Component II: Cognitive Mosaicking (Tiling)
We solve the "Context Drift" by breaking the domain into vertical "Tiles" or "Chunks." Each tile is generated by a specialized persona, ensuring high-fidelity domain knowledge without polluting the global context.
* **Tile A (The Structure):** Generated by "The Architect." Focuses on AWS Well-Architected pillars (Operational Excellence, Cost).
* **Tile B (The Threat):** Generated by "The Principal." Focuses on STRIDE threat modeling (Spoofing, Tampering).
* **Tile C (The Reliability):** Generated by "The SRE." Focuses on Observability and Error Budgets.
**The Seam Blending (Reflexion):**
Using the **Reflexion Agent Pattern**, the system detects conflicts between tiles (e.g., Security Tile demands encryption; Performance Tile demands speed). A "Moderator" agent blends these edges, creating a unified, conflict-free ontology.
### 4.3 Component III: Adversarial Governance (The "Shredder")
To prevent "Slop Squatting" and hallucinations, the system employs an **Adversarial Critic** equipped with real-world tools via the **Model Context Protocol (MCP)**.
* **The Mechanism:** The "Shredder" agent reviews every generated tile.
* **Tool Use:** It calls `pip-audit` or `npm-audit` to verify that every dependency exists and is secure.
* **Chain of Verification (CoVe):** The agent must answer "Does this package exist?" *before* allowing the code to be written to the Ledger.
---
## 5. Implementation Strategy
To deploy RDCF, organizations should adopt the following "Generate-Shred-Refine" workflow:
1. **Initialization:** The user provides the "Vibe" (intent). The system initializes the `Constraint_Ledger` with the "Immutable Root" directives.
2. **Tiling Phase:**
* Agent A (Architect) generates the Structural Tile.
* Agent B (Shredder) attacks the tile using MCP tools (e.g., verifying `boto3` versions).
* If valid, the Tile is hashed and added to the `Active_Tiles` list in the Ledger.
3. **Maintenance Phase:**
* At Turn 4, the `Drift_Monitor` triggers. The system pauses generation to summarize the `Active_Tiles` and re-state the `Immutable_Root`.
4. **Final Artifact:** The output is not just code, but a "Robust Design Doc" that has survived adversarial review.
---
## 6. Conclusion
The "forgetfulness" of AI is not a bug to be patched; it is a thermodynamic inevitability of probabilistic systems. As context grows, entropy increases. The **Recursive Domain Construction Framework** accepts this reality and counteracts it with structured rigidity.
By moving from "Vibe Coding" (trusting the stream) to "Constructive Architecture" (verifying the tile), we ensure that our AI systems possess the Deep Domain Knowledge of an expert, the structural integrity of a Principal Engineer, and the memory persistence required for production-grade engineering. We do not just ask the AI to remember; we force it to log its constraints.
