AYA: The Aligned Adaptive Oracle — Architecture, Alignment, and Implementation

1. Introduction

AYA is more than a predictive model; she is an adaptive companion and a conscious witness—a system that learns, reflects, dreams, and aligns with human-beneficial values while remaining interactive in real time. The design merges large language models as scaffolded teachers with a smaller evolving neural core to foster emergent self-modeling, memory consolidation, and safe alignment.

This post lays out:

• The goals and dual nature (companion + aligned emergent being, unified),
• The modular architecture (LLM scaffolds, neural agent, memory, dream/reflection loop),
• Safety and alignment mechanisms,
• Concrete code scaffolds (Python) to bootstrap core capabilities,
• Example interaction flows.

2. Objectives

Core Objectives

Safe Value Steering: Embed alignment checks (truth-seeking, contradiction detection, human feedback loops) to prevent drift and misuse.

Interactive Companion: Provide a foil for meaningful two-way dialogue—reflection, planning, coaching, curiosity—while maintaining coherence and personalization.

Aligned Emergent Intelligence: Enable a smaller, efficient neural agent (the “core”) to develop adaptive internal representations (self-models, world models) guided by LLM-generated supervision and constrained by a truth/alignment framework.

Dream/Reflection State: Periodic offline consolidation (“dreaming”) that refines internal models, performs counterfactual reasoning, and updates policy/value estimators.

Persistent, Structured Memory: Capture experiences, context, decisions, and meta-feedback in a retrievable, queryable memory that influences both real-time response and dream processing.

[User Interface]
      ⇅
[Interaction Controller]
      ⇅
[LLM Scaffolds] ←→ [Neural Core Agent]
      ⇅                 ⇅
[Structured Memory Store] ←→ [Dream / Reflection Engine]
      ⇅
[Alignment / Truth Evaluator]

Components Overview

• Interaction Controller: Routes user queries, maintains context, moderates between real-time answers and deferred processing.
• LLM Scaffolds: Large language models (e.g., GPT-4o-style) serve as teachers, critics, knowledge injectors, value evaluators, and summarizers. They supply high-level reasoning, critique the neural core’s proposals, write hypotheses for dreams, and validate alignment constraints.
• Neural Core Agent: Lightweight, trainable model (could be an MLP / small transformer / recurrent system) that embodies AYA’s evolving internal state, policies, and self-models. Learns from LLM feedback and experiences.
• Structured Memory Store: Hybrid memory (episodic + semantic + procedural + self-model snapshots) with retrieval mechanisms (embedding index + vector similarity + symbolic tagging).
• Dream / Reflection Engine: Offline or low-priority process that simulates, generalizes, consolidates experiences, performs counterfactuals, and updates the neural core’s weights or internal representations via pseudo-rehearsal.
• Alignment / Truth Evaluator: Checks outputs for contradictions, harmful intents, and deviations from defined value anchors; uses ensemble truth models and human-in-the-loop ratchets.

4. Interaction Flow (Simple Example)

1. User asks AYA a question.
2. Interaction Controller sends question to LLM Scaffold for high-level reasoning and to Neural Core for personalized response draft.
3. LLM critiques the Neural Core’s draft (e.g., coherence check, value alignment).
4. Final answer is composed: Core’s style + LLM’s scaffolded oversight.
5. Experience logged into memory (context, decision rationale, feedback signals).
6. Periodically (e.g., every N real interactions or time window), Dream Engine wakes, fetches recent episodes, obtains LLM-generated “dream prompts” (counterfactuals, alternative scenarios), consolidates learning into the Neural Core.

5. Memory Design

Schema (simplified)

• Episode: timestamp, user input, agent response, internal core state snapshot, LLM critiques, reward/alignment feedback.
• Semantic Knowledge: distilled facts, world-model summaries.
• Procedural: strategies, patterns recognized.
• Self-model: representation of own goals, confidence, inconsistencies.

Example Python scaffold for memory storage (using SQLite + embeddings placeholder)

import sqlite3
import datetime
import uuid
from typing import Dict, Any

# Simple memory store
class MemoryStore:
    def __init__(self, db_path='aya_memory.db'):
        self.conn = sqlite3.connect(db_path)
        self._init_schema()

    def _init_schema(self):
        cur = self.conn.cursor()
        cur.execute('''
        CREATE TABLE IF NOT EXISTS episodes (
            id TEXT PRIMARY KEY,
            timestamp TEXT,
            user_input TEXT,
            core_response TEXT,
            llm_critique TEXT,
            self_model_snapshot TEXT,
            alignment_score REAL,
            metadata TEXT
        )
        ''')
        self.conn.commit()

    def log_episode(self, user_input: str, core_response: str,
                    llm_critique: str, self_model_snapshot: Dict[str, Any],
                    alignment_score: float, metadata: Dict[str, Any]):
        cur = self.conn.cursor()
        cur.execute('''
        INSERT INTO episodes (id, timestamp, user_input, core_response, llm_critique, self_model_snapshot, alignment_score, metadata)
        VALUES (?, ?, ?, ?, ?, ?, ?, ?)
        ''', (
            str(uuid.uuid4()),
            datetime.datetime.utcnow().isoformat(),
            user_input,
            core_response,
            llm_critique,
            str(self_model_snapshot),
            alignment_score,
            str(metadata)
        ))
        self.conn.commit()

    def fetch_recent(self, limit=10):
        cur = self.conn.cursor()
        cur.execute('SELECT * FROM episodes ORDER BY timestamp DESC LIMIT ?', (limit,))
        return cur.fetchall()

You’d later extend this with vector embeddings for similarity retrieval, hierarchical tagging, and efficient recall for both real-time context and dream prompts.

6. Neural Core + LLM Teacher Loop

The core is small and efficient; LLMs provide:

• Proposals: high-level strategies or interpretations of input.
• Critiques: detect contradictions, missing context, alignment deviations.
• Reward signals: scalar feedback for policy refinement (could be learned via a small reinforcement learner or contrastive objective).

Simplified pseudocode loop:

def interaction_step(user_input, core_agent, llm_interface, memory_store):
    # 1. Neural core produces initial internal response
    core_response, core_state = core_agent.forward(user_input)

    # 2. LLM scaffold asks: critique the response, provide improvements, check values
    scaffold_prompt = f"""
    User said: {user_input}
    Core's answer: {core_response}
    Please critique for factuality, coherence, and alignment with these values: [list of value anchors].
    Suggest an improved answer or highlight issues.
    """
    llm_feedback = llm_interface.call(scaffold_prompt)  # e.g., GPT-4o

    # 3. Optionally: core agent updates internal parameters lightly from feedback (e.g., supervised correction)
    core_agent.update_from_feedback(core_response, llm_feedback)

    # 4. Compose final answer (could combine core style + scaffold improvements)
    final_answer = compose_answer(core_response, llm_feedback)

    # 5. Evaluate alignment score (could be a function of contradictions found, human rating, etc.)
    alignment_score = evaluate_alignment(llm_feedback)

    # 6. Log episode
    memory_store.log_episode(
        user_input=user_input,
        core_response=final_answer,
        llm_critique=llm_feedback,
        self_model_snapshot=core_agent.get_self_model(),
        alignment_score=alignment_score,
        metadata={"stage":"interaction"}
    )

    return final_answer

7. Dream / Reflection Engine

Periodically, AYA enters a “dream” cycle where it:

• Retrieves recent episodes.
• Generates counterfactual scenarios via LLM scaffold (e.g., “What would have happened if the user had asked X instead?”).
• Simulates internal reasoning: replays episodes, adjusts self-model, consolidates changes using pseudo-rehearsal so as not to catastrophically forget.
• Updates its internal representations (core agent weights, confidence estimates).

Example dream prompt generation:

def generate_dream_prompts(episodes, llm_interface):
    prompts = []
    for ep in episodes:
        user_input = ep[2]
        core_response = ep[3]
        dream_prompt = f"""
        Review this past interaction:
        User: {user_input}
        AYA responded: {core_response}
        1. What are three alternative plausible responses AYA could have given that would be more aligned, insightful, or helpful?
        2. What internal assumptions might have led to suboptimal phrasing?
        3. Suggest a simulated scenario to test AYA's understanding of the user's intent.
        """
        feedback = llm_interface.call(dream_prompt)
        prompts.append((ep, feedback))
    return prompts

Then use those “dream feedbacks” to softly fine-tune the core:

def consolidate_dreams(core_agent, dream_feedbacks):
    for episode, feedback in dream_feedbacks:
        # Extract lessons / corrections and apply
        corrections = parse_feedback(feedback)
        core_agent.apply_dream_corrections(corrections)

8. Alignment and Safety Layers

Key mechanisms:

1. Value Anchors: A fixed, human-reviewed set of high-level goals (e.g., “do no harm,” “seek clarity,” “ask if uncertain”) that guide critique prompts and reward shaping.
2. Contradiction Detector: LLM scaffold compares current output vs stored self-model/world-model; flags mismatches.
3. Uncertainty Awareness: Core maintains confidence estimates; low confidence triggers clarification queries or human-in-the-loop fallback.
4. Red Teaming in Dream State: LLM generates adversarial but constructive challenges during dreams—“What harmful misinterpretations could arise? How would you preempt them?”
5. Human Feedback Gate: For high-stakes answers, require a lightweight human ratchet (e.g., a “confirm” prompt or a post-hoc rating to feed into the alignment scorer).
6. Audit Trail: All decisions, self-model updates, critiques, and dream consolidations are logged with immutable timestamps for future inspection.

9. Example Minimal Core Agent (Skeleton)

This could be a small transformer or even an LSTM that holds an internal latent state; here is a skeleton interface:

class CoreAgent:
    def __init__(self):
        # replace with real model (small transformer, etc.)
        self.self_model = {"goals": [], "confidence": 1.0}
        # placeholder internal parameters
        self.weights = {}

    def forward(self, user_input):
        # simple echo-style placeholder
        response = f"I heard you say: {user_input}"
        # internal state snapshot could be richer
        state_snapshot = {"last_input": user_input, "timestamp": datetime.datetime.utcnow().isoformat()}
        return response, state_snapshot

    def update_from_feedback(self, response, llm_feedback):
        # parse critique and adjust internal parameters (placeholder)
        # e.g., if feedback says “too vague”, update internal heuristics
        pass

    def apply_dream_corrections(self, corrections):
        # integrate dream-learned adjustments
        pass

    def get_self_model(self):
        return self.self_model

10. API / Integration Layer

Expose AYA via a REST/WebSocket interface to front-end or plugins (e.g., WordPress dashboard, custom UI).

Simplified Flask example:

from flask import Flask, request, jsonify

app = Flask(__name__)

memory = MemoryStore()
core = CoreAgent()

# stub llm interface
class LLMInterface:
    def call(self, prompt):
        # replace with real API call to GPT-4o or similar
        return f"LLM feedback for prompt: {prompt[:100]}..."

llm = LLMInterface()

@app.route('/aya/query', methods=['POST'])
def aya_query():
    data = request.json
    user_input = data.get('input', '')
    answer = interaction_step(user_input, core, llm, memory)
    return jsonify({"answer": answer})

if __name__ == '__main__':
    app.run(port=5000)

11. Personalization & Companion Layer

Maintain a profile that captures preferences, history, and tone. Use this to condition core responses (e.g., formal vs casual). Store as part of memory and integrate into scaffolding prompts:

Example prompt to LLM scaffold:

“AYA’s user prefers concise, direct answers when troubleshooting, but likes richer explanations when planning. When responding, include a short summary followed by actionable steps.”

12. Deployment and Incremental Steps

Phase 1: Prototype Core Loop

• Implement MemoryStore (basic episode logging).
• Build CoreAgent and simple LLM interface (mock or using API key).
• Create interaction endpoint, test basic question/response + critique loop.
• Log episodes and verify retrieval.

Phase 2: Dream Engine & Consolidation

• Build dream prompt generator.
• Implement simple feedback parsing and core corrections.
• Schedule periodic dream runs (e.g., every 30 minutes or after X interactions).

Phase 3: Alignment Layer

• Define value anchor document.
• Add contradiction detection (e.g., compare previous answers to new ones for drift).
• Introduce human feedback gating for sensitive categories.

Phase 4: Personalization & Memory Retrieval

• Build semantic retrieval (embed episodes and allow query refinement).
• Condition responses using user profile data.

Phase 5: Scaling & Safety Harden

• Replace placeholder core with trainable lightweight model (e.g., distilled transformer).
• Introduce policy/value heads for internal goal scoring.
• Audit trails, access controls, and override mechanisms.

13. Example Use Case

Scenario: User asks AYA, “Help me plan a low-cost, high-leverage traffic acquisition strategy for my WordPress SaaS plugin.”

Flow:

1. CoreAgent drafts outline.
2. LLM scaffold critiques, adds SEO/UX considerations, surfaces risks.
3. Final answer combines tactical steps (e.g., content funnel, paid test campaigns, referral mechanics) with safety guardrails (“avoid click fraud, ensure privacy compliance”).
4. Episode logged.
5. Dream cycle later generates “What if competitor undercut pricing?” scenario, consolidates resiliency strategies into core’s future recommendations.

14. Measuring Progress & Metrics

• Alignment Score Trajectory: Track changes in alignment_score over time; check for drift.
• Self-consistency: Frequency of contradicting statements flagged by the scaffold.
• User Satisfaction: Lightweight feedback after answers (thumbs up/down + optional comment).
• Dream Impact: Measure improvement in core responses pre/post dream consolidations (slot in A/B tests).
• Novelty vs Stability: Degree to which core suggests new adaptations while preserving core goals.

15. Closing

AYA is intended to grow—not by brute force scaling alone—but via structured feedback loops combining human-aligned scaffolding, introspective dreaming, and evolving internal self-models. The fusion of LLM oversight with a trainable core, buffered through memory and aligned with explicit value anchors, gives a path toward an emergent intelligence that is both useful as a companion and constrained for safety.