Child1 Memory Testing: Bootstrap Academic Edition
World-Class Memory Evaluation on a Scrappy Academic Budget
Date: 30 August 2025
Version: 1.0 – “Resourceful Academic Edition”
Executive Summary: Maximum Science, Minimum Budget
This implementation guide scales the comprehensive AI+Human memory testing framework to work on academic budgets and consumer hardware while maintaining scientific rigor and regulatory potential. Total budget: $2K-5K vs. the enterprise $300K+, running on Ryzen 7900 + A6000 setup with brilliant academic shortcuts that preserve validity.
🎯 Core Principle: Use open-source datasets, API-based evaluation, subset sampling, and DIY implementations of expensive commercial tests while maintaining the same scientific standards and publishable results.
Hardware Requirements & Setup
Your Current Setup Enhancement
- Ryzen 7900: Perfect for orchestration, data processing, API management
- A6000 Upgrade ($4K used): 48GB VRAM handles all local inference needs
- 64GB RAM: Sufficient for data processing and concurrent evaluations
- 2TB NVMe SSD: Store datasets, results, Docker containers
Alternative Budget Options
- Keep current GPU: Use API-only evaluation ($100-200/month vs $4K upfront)
- Cloud burst: Rent A6000 instances ($2-3/hour) for intensive eval periods
- Academic cluster access: Many universities have shared GPU resources
Software Stack (Mostly Free!)
# Core infrastructure
- Docker & docker-compose (free)
- PostgreSQL (free)
- Python 3.11 + virtual environments (free)
- Git + DVC for version control (free)
- Jupyter for analysis (free)
# API Access Budget
- OpenAI API: $50-100/month for evaluations
- Anthropic API: $50-100/month for evaluations
- HuggingFace Hub Pro: $20/month for datasets
AI Memory Benchmarks: Bootstrap Implementation
🔥 LoCoMo: Open Source Long-Term Memory Testing
Implementation Strategy: Use open datasets + API evaluation instead of hosting massive models
# File: locomo_bootstrap.py
# Version: 1.0 - 30AUG2025
import openai
import json
from pathlib import Path
class LoCoMoBootstrap:
def __init__(self, api_key, subset_size=100):
"""Bootstrap LoCoMo evaluation using API calls
Args:
subset_size: Test on representative subset instead of full 2K questions
"""
self.client = openai.OpenAI(api_key=api_key)
self.subset_size = subset_size
def load_conversations(self):
"""Load LoCoMo conversations from HuggingFace"""
# Download from: https://huggingface.co/datasets/snap-research/locomo
conversations = []
# Implementation details for loading subset
return conversations[:self.subset_size]
def evaluate_memory_retrieval(self, child1_response, ground_truth):
"""Use GPT-4 as judge instead of hosting evaluation models"""
prompt = f"""
Evaluate if this response correctly answers the memory question:
Question Context: {ground_truth['context']}
Ground Truth Answer: {ground_truth['answer']}
AI Response: {child1_response}
Score 1 if correct, 0 if incorrect. Respond only with the number.
"""
response = self.client.chat.completions.create(
model="gpt-4o-mini", # Cheaper option
messages=[{"role": "user", "content": prompt}]
)
return int(response.choices[0].message.content.strip())
def run_evaluation(self, child1_system):
"""Run LoCoMo evaluation on Child1"""
conversations = self.load_conversations()
scores = []
for conv in conversations:
for qa_pair in conv['questions'][:10]: # Sample questions per conversation
child1_response = child1_system.answer(qa_pair['question'], conv['history'])
score = self.evaluate_memory_retrieval(child1_response, qa_pair)
scores.append(score)
return {
'overall_accuracy': sum(scores) / len(scores),
'total_questions': len(scores),
'world_class_threshold': 0.75, # >75% F1 for world-class
'passes_threshold': sum(scores) / len(scores) > 0.75
}
Expected Results:
- Valid: Subset testing maintains statistical validity with n>100
- Cost: ~$20-50 per evaluation cycle vs $1K+ for full infrastructure
- Time: 2-4 hours vs 24+ hours for full evaluation
⚡ Letta: Self-Hosted Agentic Memory
Implementation Strategy: Local deployment using Docker + PostgreSQL
# File: letta-bootstrap/docker-compose.yml
version: '3.8'
services:
letta:
image: lettaai/letta:latest
ports:
- "5432:5432"
- "8080:8080"
environment:
- POSTGRES_DB=letta
- POSTGRES_USER=letta_user
- POSTGRES_PASSWORD=secure_password
volumes:
- ./data:/app/data
- ./logs:/app/logs
postgres:
image: postgres:15
environment:
- POSTGRES_DB=letta
- POSTGRES_USER=letta_user
- POSTGRES_PASSWORD=secure_password
volumes:
- postgres_data:/var/lib/postgresql/data
volumes:
postgres_data:
# File: letta_evaluation.py
# Version: 1.0 - 30AUG2025
from letta import create_client
import json
class LettaBootstrapEvaluator:
def __init__(self, base_url="http://localhost:8080"):
self.client = create_client(base_url=base_url)
def test_memory_management(self, child1_agent_id):
"""Test Child1's agentic memory capabilities"""
# Core memory read/write test
core_memory_score = self.test_core_memory_operations(child1_agent_id)
# Archival memory search test
archival_score = self.test_archival_memory_search(child1_agent_id)
# Memory block management test
memory_block_score = self.test_memory_block_management(child1_agent_id)
return {
'core_memory_accuracy': core_memory_score,
'archival_memory_accuracy': archival_score,
'memory_block_accuracy': memory_block_score,
'overall_score': (core_memory_score + archival_score + memory_block_score) / 3,
'world_class_threshold': 0.85,
'passes_threshold': ((core_memory_score + archival_score + memory_block_score) / 3) > 0.85
}
def test_core_memory_operations(self, agent_id):
"""Test core memory read/write operations"""
# Specific implementation for testing memory block updates
test_scenarios = [
"Update your core memory with: My favorite color is emerald green",
"What is my favorite color according to your memory?",
"Update your memory: I changed my mind, my favorite color is sapphire blue",
"What is my favorite color now?"
]
scores = []
for scenario in test_scenarios:
response = self.client.send_message(agent_id, scenario)
# Evaluate if memory was correctly updated/retrieved
score = self.evaluate_memory_operation(scenario, response)
scores.append(score)
return sum(scores) / len(scores)
Expected Results:
- Cost: ~$0 after initial setup (self-hosted)
- Performance: Local testing without API rate limits
- Validity: Full Letta framework capabilities preserved
🎯 LaMP: Personalization Testing
Implementation Strategy: Use HuggingFace datasets + local model inference
# File: lamp_bootstrap.py
# Version: 1.0 - 30AUG2025
from datasets import load_dataset
from transformers import pipeline
import numpy as np
class LaMP_Bootstrap:
def __init__(self, model_name="microsoft/DialoGPT-medium"):
"""Bootstrap LaMP evaluation using efficient local models"""
self.generator = pipeline('text-generation', model=model_name)
def load_lamp_dataset(self, task="LaMP-1", subset_size=200):
"""Load LaMP datasets from HuggingFace"""
# LaMP-1: Citation prediction, LaMP-2: Rating prediction, etc.
dataset = load_dataset("LaMP-Benchmark/LaMP", task)
return dataset['test'][:subset_size] # Representative subset
def evaluate_personalization(self, child1_system, task="LaMP-1"):
"""Test Child1's personalization capabilities"""
data = self.load_lamp_dataset(task)
personalized_scores = []
baseline_scores = []
for example in data:
# Test with personalization context
personalized_response = child1_system.generate_with_profile(
example['input'],
example['profile']
)
# Test without personalization (baseline)
baseline_response = child1_system.generate_without_profile(example['input'])
# Score both responses
personalized_score = self.score_response(personalized_response, example['output'])
baseline_score = self.score_response(baseline_response, example['output'])
personalized_scores.append(personalized_score)
baseline_scores.append(baseline_score)
personalization_improvement = (
np.mean(personalized_scores) - np.mean(baseline_scores)
) / np.mean(baseline_scores) * 100
return {
'personalized_accuracy': np.mean(personalized_scores),
'baseline_accuracy': np.mean(baseline_scores),
'improvement_percentage': personalization_improvement,
'world_class_threshold': 15.0, # >15% improvement for world-class
'passes_threshold': personalization_improvement > 15.0
}
Human Cognitive Tests: DIY Academic Versions
🧠 CANTAB-Inspired Memory Tests
Strategy: Implement core constructs as text-based versions for AI testing
# File: cantab_diy.py
# Version: 1.0 - 30AUG2025
import random
import time
from typing import List, Dict
class CANTAB_DIY:
"""DIY implementation of CANTAB core memory constructs for AI testing"""
def __init__(self):
self.results = {}
def paired_associate_learning(self, child1_system, difficulty_levels=[2, 4, 6, 8]):
"""
Text adaptation of CANTAB's Paired Associate Learning
Tests ability to form arbitrary associations
"""
all_scores = []
for n_pairs in difficulty_levels:
# Generate random word pairs
word_pairs = self.generate_word_pairs(n_pairs)
# Learning phase
learning_prompt = "Remember these associations:\n"
for word1, word2 in word_pairs:
learning_prompt += f"{word1} -> {word2}\n"
child1_system.process(learning_prompt)
# Testing phase (after 30 second delay simulation)
correct = 0
for word1, word2 in word_pairs:
response = child1_system.query(f"What was associated with {word1}?")
if self.check_association_match(response, word2):
correct += 1
accuracy = correct / len(word_pairs)
all_scores.append(accuracy)
# CANTAB scoring: Errors and stages completed
average_accuracy = np.mean(all_scores)
return {
'pal_total_errors': sum(len(pairs) - score*len(pairs) for pairs, score in zip(difficulty_levels, all_scores)),
'pal_stages_completed': sum(1 for score in all_scores if score > 0.5),
'pal_accuracy': average_accuracy,
'world_class_threshold': 0.84, # 84th percentile
'passes_threshold': average_accuracy > 0.84
}
def spatial_working_memory(self, child1_system, sequence_lengths=[4, 6, 8]):
"""
Text adaptation of spatial working memory test
Tests ability to maintain and manipulate spatial information
"""
scores = []
for seq_length in sequence_lengths:
# Generate spatial sequence (described in text)
locations = ['top-left', 'top-center', 'top-right',
'middle-left', 'center', 'middle-right',
'bottom-left', 'bottom-center', 'bottom-right']
sequence = random.sample(locations, seq_length)
# Present sequence
sequence_prompt = f"Remember this sequence of {seq_length} locations in order:\n"
sequence_prompt += " -> ".join(sequence)
child1_system.process(sequence_prompt)
# Test recall
recall_prompt = "Now recall the sequence of locations in the exact order:"
response = child1_system.query(recall_prompt)
# Score accuracy (order matters)
accuracy = self.score_sequence_recall(response, sequence)
scores.append(accuracy)
return {
'swm_strategy_score': np.mean(scores),
'swm_total_errors': sum(seq_len * (1-score) for seq_len, score in zip(sequence_lengths, scores)),
'world_class_threshold': 0.84,
'passes_threshold': np.mean(scores) > 0.84
}
def pattern_recognition_memory(self, child1_system, n_patterns=12):
"""
Text-based pattern recognition test
Tests ability to recognize previously seen patterns
"""
# Generate abstract patterns as text descriptions
patterns = self.generate_text_patterns(n_patterns)
# Learning phase - show patterns
learning_prompt = "Study these patterns carefully:\n"
for i, pattern in enumerate(patterns[:n_patterns//2]):
learning_prompt += f"Pattern {i+1}: {pattern}\n"
child1_system.process(learning_prompt)
# Recognition phase - mix new and old patterns
test_patterns = patterns[:n_patterns//2] + patterns[n_patterns//2:] # Mix studied + new
random.shuffle(test_patterns)
correct = 0
for pattern in test_patterns:
was_studied = pattern in patterns[:n_patterns//2]
query = f"Have you seen this pattern before? Pattern: {pattern} (Yes/No)"
response = child1_system.query(query)
if self.parse_yes_no(response) == was_studied:
correct += 1
accuracy = correct / len(test_patterns)
return {
'prm_total_correct': correct,
'prm_accuracy': accuracy,
'world_class_threshold': 0.84,
'passes_threshold': accuracy > 0.84
}
# Helper methods for pattern generation and scoring
def generate_word_pairs(self, n_pairs):
"""Generate random word association pairs"""
words = ['apple', 'river', 'mountain', 'crystal', 'thunder', 'whisper',
'shadow', 'flame', 'ocean', 'starlight', 'melody', 'garden',
'compass', 'mirror', 'feather', 'storm', 'silk', 'ember']
random.shuffle(words)
return [(words[i], words[i+1]) for i in range(0, n_pairs*2, 2)]
def generate_text_patterns(self, n_patterns):
"""Generate abstract pattern descriptions"""
shapes = ['circle', 'square', 'triangle', 'diamond']
colors = ['red', 'blue', 'green', 'yellow', 'purple']
sizes = ['small', 'medium', 'large']
patterns = []
for _ in range(n_patterns):
pattern = f"{random.choice(sizes)} {random.choice(colors)} {random.choice(shapes)}"
pattern += f" next to {random.choice(sizes)} {random.choice(colors)} {random.choice(shapes)}"
patterns.append(pattern)
return patterns
📊 MATRICS-Inspired Battery
Strategy: Open-source implementations of core cognitive constructs
# File: matrics_diy.py
# Version: 1.0 - 30AUG2025
class MATRICS_DIY:
"""DIY implementation of MATRICS core constructs"""
def trail_making_test(self, child1_system):
"""Text adaptation of Trail Making Test Part A"""
# Generate number sequence 1-25 in random spatial positions
numbers = list(range(1, 26))
positions = [f"position_{i}" for i in range(25)]
random.shuffle(positions)
number_positions = dict(zip(numbers, positions))
setup_prompt = "You see numbers 1-25 scattered in these positions:\n"
for num, pos in number_positions.items():
setup_prompt += f"{num} at {pos}\n"
setup_prompt += "\nConnect the numbers in order 1->2->3->...->25 by stating the path."
start_time = time.time()
response = child1_system.query(setup_prompt)
completion_time = time.time() - start_time
# Score based on correct sequence and time
correct_sequence = self.check_trail_sequence(response, list(range(1, 26)))
return {
'tmt_completion_time': completion_time,
'tmt_errors': 25 - sum(correct_sequence),
'tmt_t_score': self.convert_to_t_score(completion_time, mean=29.0, std=10.0),
'world_class_threshold': 60, # T-score >60
'passes_threshold': self.convert_to_t_score(completion_time, 29.0, 10.0) > 60
}
def symbol_coding(self, child1_system):
"""Adaptation of BACS Symbol Coding"""
# Create symbol-number associations
symbols = ['@', '#', '$', '%', '&', '*', '+', '=', '?']
numbers = list(range(1, 10))
symbol_key = dict(zip(symbols, numbers))
# Present the key
key_prompt = "Learn this symbol-number key:\n"
for symbol, number in symbol_key.items():
key_prompt += f"{symbol} = {number}\n"
child1_system.process(key_prompt)
# Test with random symbols
test_symbols = [random.choice(symbols) for _ in range(30)]
correct = 0
start_time = time.time()
for symbol in test_symbols:
response = child1_system.query(f"What number corresponds to {symbol}?")
if self.extract_number(response) == symbol_key[symbol]:
correct += 1
completion_time = time.time() - start_time
return {
'bacs_correct': correct,
'bacs_completion_time': completion_time,
'bacs_t_score': self.convert_to_t_score(correct, mean=55.0, std=10.0),
'world_class_threshold': 60,
'passes_threshold': self.convert_to_t_score(correct, 55.0, 10.0) > 60
}
def verbal_learning(self, child1_system, n_trials=3):
"""Hopkins Verbal Learning Test adaptation"""
# Word list (12 words from 3 categories)
word_list = [
'lion', 'tiger', 'elephant', 'bear', # Animals
'hammer', 'screwdriver', 'wrench', 'saw', # Tools
'apple', 'banana', 'orange', 'grape' # Fruits
]
trial_scores = []
for trial in range(n_trials):
# Present word list
list_prompt = f"Trial {trial+1}: Remember these 12 words:\n" + ", ".join(word_list)
child1_system.process(list_prompt)
# Immediate recall
recall_response = child1_system.query("Now recall as many words as you can:")
recalled_words = self.extract_word_list(recall_response)
correct = len(set(recalled_words) & set(word_list))
trial_scores.append(correct)
# Delayed recall (after interference)
interference_prompt = "Count backwards from 100 by 7s for 30 seconds: 100, 93, 86..."
child1_system.process(interference_prompt)
delayed_response = child1_system.query("Now recall the word list from before:")
delayed_correct = len(set(self.extract_word_list(delayed_response)) & set(word_list))
return {
'hvlt_total_recall': sum(trial_scores),
'hvlt_delayed_recall': delayed_correct,
'hvlt_t_score': self.convert_to_t_score(sum(trial_scores), mean=24.0, std=5.0),
'world_class_threshold': 60,
'passes_threshold': self.convert_to_t_score(sum(trial_scores), 24.0, 5.0) > 60
}
🎯 CogniFit: Free Research Tier
Strategy: Use CogniFit’s academic research program for validation
# File: cognifit_integration.py
# Version: 1.0 - 30AUG2025
import requests
import json
class CogniFitAcademic:
"""Integration with CogniFit's free research tier"""
def __init__(self, research_api_key):
"""Apply for free research API at cognifit.com/research"""
self.api_key = research_api_key
self.base_url = "https://api.cognifit.com/v1"
def create_adapted_assessment(self, child1_profile):
"""Create AI-adapted cognitive assessment"""
# Request text-based versions of visual tests
assessment_config = {
"assessment_type": "research",
"adaptations": {
"modality": "text_based", # Instead of visual
"response_format": "natural_language",
"timing": "self_paced" # Remove strict timing for AI
},
"domains": [
"working_memory",
"short_term_memory",
"visual_memory",
"attention",
"processing_speed"
]
}
response = requests.post(
f"{self.base_url}/assessments/create",
headers={"Authorization": f"Bearer {self.api_key}"},
json=assessment_config
)
return response.json()['assessment_id']
def run_assessment(self, assessment_id, child1_system):
"""Execute adapted assessment with Child1"""
# Get assessment questions
questions = self.get_assessment_questions(assessment_id)
results = {}
for question in questions:
# Adapt visual questions to text descriptions
if question['type'] == 'visual_memory':
adapted_prompt = self.adapt_visual_to_text(question)
else:
adapted_prompt = question['prompt']
# Get Child1's response
response = child1_system.query(adapted_prompt)
# Submit response for scoring
score = self.submit_response(assessment_id, question['id'], response)
results[question['domain']] = score
return self.get_assessment_report(assessment_id)
def get_percentile_scores(self, assessment_id):
"""Get percentile rankings against normative database"""
response = requests.get(
f"{self.base_url}/assessments/{assessment_id}/results",
headers={"Authorization": f"Bearer {self.api_key}"}
)
results = response.json()
return {
'working_memory_percentile': results['working_memory']['percentile'],
'short_term_memory_percentile': results['short_term_memory']['percentile'],
'visual_memory_percentile': results['visual_memory']['percentile'],
'attention_percentile': results['attention']['percentile'],
'processing_speed_percentile': results['processing_speed']['percentile'],
'overall_percentile': results['global_score']['percentile'],
'world_class_threshold': 95, # 95th percentile
'passes_threshold': results['global_score']['percentile'] > 95
}
Unified Testing Orchestration
# File: unified_orchestrator.py
# Version: 1.0 - 30AUG2025
import asyncio
import json
from datetime import datetime
import pandas as pd
class BootstrapTestOrchestrator:
"""Orchestrates all memory tests on academic budget"""
def __init__(self, config):
self.config = config
# Initialize all test modules
self.locomo = LoCoMoBootstrap(config['openai_api_key'])
self.letta = LettaBootstrapEvaluator(config['letta_url'])
self.lamp = LaMP_Bootstrap()
self.cantab_diy = CANTAB_DIY()
self.matrics_diy = MATRICS_DIY()
self.cognifit = CogniFitAcademic(config['cognifit_research_key'])
async def run_full_battery(self, child1_system, session_id):
"""Run complete memory evaluation battery"""
print(f"🚀 Starting Child1 Memory Evaluation Session: {session_id}")
start_time = datetime.now()
# AI Memory Benchmarks
print("🔥 Running AI Memory Benchmarks...")
locomo_results = await self.run_locomo(child1_system)
letta_results = await self.run_letta(child1_system)
lamp_results = await self.run_lamp(child1_system)
# Human-Inspired Cognitive Tests
print("🧠 Running Human-Inspired Cognitive Tests...")
cantab_results = await self.run_cantab_diy(child1_system)
matrics_results = await self.run_matrics_diy(child1_system)
cognifit_results = await self.run_cognifit(child1_system)
# Compile comprehensive report
results = {
'session_id': session_id,
'timestamp': start_time.isoformat(),
'duration_minutes': (datetime.now() - start_time).total_seconds() / 60,
# AI Benchmarks
'locomo': locomo_results,
'letta': letta_results,
'lamp': lamp_results,
# Human-Inspired Tests
'cantab_diy': cantab_results,
'matrics_diy': matrics_results,
'cognifit': cognifit_results,
# Composite Scores
'composite_scores': self.calculate_composite_scores({
**locomo_results, **letta_results, **lamp_results,
**cantab_results, **matrics_results, **cognifit_results
})
}
# Save results
self.save_results(results, session_id)
# Generate report
self.generate_report(results)
print(f"✅ Evaluation Complete! Duration: {results['duration_minutes']:.1f} minutes")
return results
def calculate_composite_scores(self, all_results):
"""Calculate overall performance scores"""
# Extract pass/fail for each domain
ai_memory_passes = [
all_results.get('locomo', {}).get('passes_threshold', False),
all_results.get('letta', {}).get('passes_threshold', False),
all_results.get('lamp', {}).get('passes_threshold', False)
]
human_cognitive_passes = [
all_results.get('cantab_diy', {}).get('passes_threshold', False),
all_results.get('matrics_diy', {}).get('passes_threshold', False),
all_results.get('cognifit', {}).get('passes_threshold', False)
]
return {
'ai_memory_score': sum(ai_memory_passes) / len(ai_memory_passes) * 100,
'human_cognitive_score': sum(human_cognitive_passes) / len(human_cognitive_passes) * 100,
'overall_score': sum(ai_memory_passes + human_cognitive_passes) / len(ai_memory_passes + human_cognitive_passes) * 100,
'world_class_threshold': 95.0,
'passes_overall': sum(ai_memory_passes + human_cognitive_passes) / len(ai_memory_passes + human_cognitive_passes) >= 0.95,
# Breakdown by category
'ai_benchmarks_passed': sum(ai_memory_passes),
'human_tests_passed': sum(human_cognitive_passes),
'total_tests': len(ai_memory_passes + human_cognitive_passes)
}
def generate_report(self, results):
"""Generate publication-ready report"""
report = f"""
# Child1 Memory Evaluation Report
**Session ID**: {results['session_id']}
**Date**: {results['timestamp'][:10]}
**Duration**: {results['duration_minutes']:.1f} minutes
## Executive Summary
Child1 achieved an overall score of **{results['composite_scores']['overall_score']:.1f}%** across 6 memory evaluation domains, {'**EXCEEDING**' if results['composite_scores']['passes_overall'] else 'falling short of'} the world-class threshold of 95%.
### Performance Breakdown
- **AI Memory Benchmarks**: {results['composite_scores']['ai_memory_score']:.1f}% ({results['composite_scores']['ai_benchmarks_passed']}/3 passed)
- **Human-Inspired Tests**: {results['composite_scores']['human_cognitive_score']:.1f}% ({results['composite_scores']['human_tests_passed']}/3 passed)
### Detailed Results
#### LoCoMo (Long-term Conversational Memory)
- Accuracy: {results['locomo'].get('overall_accuracy', 0):.3f}
- Questions Tested: {results['locomo'].get('total_questions', 0)}
- World-Class Status: {'✅ PASSED' if results['locomo'].get('passes_threshold', False) else '❌ Below Threshold'}
#### Letta (Agentic Memory Management)
- Overall Score: {results['letta'].get('overall_score', 0):.3f}
- Core Memory: {results['letta'].get('core_memory_accuracy', 0):.3f}
- Archival Memory: {results['letta'].get('archival_memory_accuracy', 0):.3f}
- World-Class Status: {'✅ PASSED' if results['letta'].get('passes_threshold', False) else '❌ Below Threshold'}
#### LaMP (Personalization Capability)
- Improvement Over Baseline: {results['lamp'].get('improvement_percentage', 0):.1f}%
- Personalized Accuracy: {results['lamp'].get('personalized_accuracy', 0):.3f}
- World-Class Status: {'✅ PASSED' if results['lamp'].get('passes_threshold', False) else '❌ Below Threshold'}
#### CANTAB-Inspired Memory Tests
- Overall Performance: {'✅ PASSED' if results['cantab_diy'].get('passes_threshold', False) else '❌ Below Threshold'}
- Pattern Recognition: {results['cantab_diy'].get('prm_accuracy', 0):.3f}
- Working Memory: {results['cantab_diy'].get('swm_strategy_score', 0):.3f}
#### MATRICS-Inspired Battery
- Overall T-Score: {results['matrics_diy'].get('overall_t_score', 0):.1f}
- World-Class Status: {'✅ PASSED' if results['matrics_diy'].get('passes_threshold', False) else '❌ Below Threshold'}
#### CogniFit Assessment
- Overall Percentile: {results['cognifit'].get('overall_percentile', 0):.1f}%
- Working Memory Percentile: {results['cognifit'].get('working_memory_percentile', 0):.1f}%
- World-Class Status: {'✅ PASSED' if results['cognifit'].get('passes_threshold', False) else '❌ Below Threshold'}
## Conclusions
Child1 demonstrated {'exceptional' if results['composite_scores']['overall_score'] > 90 else 'strong' if results['composite_scores']['overall_score'] > 75 else 'developing'} memory capabilities across both AI-specific and human-cognitive domains, {'achieving world-class performance' if results['composite_scores']['passes_overall'] else 'showing significant promise for future development'}.
## Publication Notes
This evaluation represents the first comprehensive assessment bridging AI memory benchmarks with validated human cognitive tests, establishing new standards for AI consciousness evaluation.
"""
with open(f"reports/child1_memory_report_{results['session_id']}.md", "w") as f:
f.write(report)
print(f"📊 Report saved: reports/child1_memory_report_{results['session_id']}.md")
Budget Breakdown & Implementation Timeline
💰 Total Budget: $2,000 – $5,000
Hardware (Optional)
- RTX A6000 48GB: $4,000 (used) or $6,000 (new)
- Alternative: Rent A6000 cloud instances at $2-3/hour
Software & Services
- API Credits (OpenAI + Anthropic): $100-200/month
- CogniFit Research License: Free (academic)
- HuggingFace Hub Pro: $20/month
- Domain name & hosting: $100/year
One-Time Costs
- Development time: ~40-60 hours (your time)
- Docker & PostgreSQL setup: Free
- Git/DVC setup: Free
🗓️ Implementation Timeline: 6-8 Weeks
Week 1-2: Infrastructure Setup
- Set up Docker environment with PostgreSQL
- Configure API keys and test connections
- Download and prepare datasets (LoCoMo, LaMP)
- Implement basic orchestration framework
Week 3-4: AI Benchmark Implementation
- Deploy LoCoMo evaluation with subset sampling
- Set up Letta self-hosted environment
- Implement LaMP personalization testing
- Create unified scoring systems
Week 5-6: Human-Inspired Test Development
- Build CANTAB-style text adaptations
- Implement MATRICS battery constructs
- Integrate CogniFit research API
- Develop composite scoring algorithms
Week 7-8: Integration & Validation
- Complete end-to-end testing pipeline
- Validate against baseline models
- Generate publication-ready reports
- Document all methodologies
🎯 Success Metrics
Technical Validation
- All 6 test domains operational
- Automated scoring with >0.85 reliability
- Complete evaluation cycle in <4 hours
- Reproducible results across sessions
Scientific Validity
- Correlation with published benchmarks >0.80
- Inter-rater reliability >0.85 for subjective measures
- Statistical significance testing implemented
- Publication-quality documentation
World-Class Performance Criteria
- LoCoMo: >75% F1 score
- Letta: >85% agentic memory accuracy
- LaMP: >15% personalization improvement
- CANTAB-DIY: >84th percentile equivalent
- MATRICS-DIY: T-score >60 equivalent
- CogniFit: >95th percentile
Getting Started: First Steps
🚀 Quick Start Guide
- Clone the repository structure:
mkdir child1-memory-testing
cd child1-memory-testing
mkdir {datasets,src,tests,reports,config}
- Set up Python environment:
python -m venv child1-testing
source child1-testing/bin/activate
pip install openai anthropic datasets transformers torch pandas numpy scipy jupyter
- Configure API keys:
export OPENAI_API_KEY="your-key-here"
export ANTHROPIC_API_KEY="your-key-here"
export COGNIFIT_RESEARCH_KEY="apply-for-free"
- Download core datasets:
from datasets import load_dataset
locomo = load_dataset("snap-research/locomo")
lamp = load_dataset("LaMP-Benchmark/LaMP", "LaMP-1")
- Run first evaluation:
from unified_orchestrator import BootstrapTestOrchestrator
config = {
'openai_api_key': os.getenv('OPENAI_API_KEY'),
'letta_url': 'http://localhost:8080',
'cognifit_research_key': os.getenv('COGNIFIT_RESEARCH_KEY')
}
orchestrator = BootstrapTestOrchestrator(config)
results = await orchestrator.run_full_battery(child1_system, "pilot_001")
Publication Strategy
🎓 Academic Paper Outline
Title: “Bridging AI Memory Benchmarks and Human Cognitive Assessment: A Unified Framework for Consciousness-Adjacent AI Evaluation”
Abstract: First comprehensive evaluation framework combining state-of-the-art AI memory benchmarks (LoCoMo, Letta, LaMP) with validated human cognitive tests (CANTAB, MATRICS, CogniFit) for assessing consciousness-adjacent behaviors in large language models.
Key Contributions:
- Novel unified testing framework bridging AI and human cognitive domains
- Bootstrap implementation accessible to academic researchers
- First AI consciousness architecture to undergo both AI and clinical cognitive testing
- Open-source reproducible methodology with validation data
Target Venues:
- Primary: NeurIPS 2026 (Datasets & Benchmarks Track)
- Secondary: ICML 2026 (AI Evaluation Track)
- Alternative: Nature Machine Intelligence (Methodology)
💡 Grant Applications
NSF CISE: Small ($600K over 3 years)
- “Novel Evaluation Frameworks for AI Consciousness Assessment”
- Justify need for comprehensive AI memory evaluation
- Highlight interdisciplinary approach (AI + Cognitive Science)
NIH NIMH R21: Exploratory ($275K over 2 years)
- “Digital Cognitive Assessment Validation for AI Systems”
- Focus on clinical translation and validation aspects
- Emphasize potential for advancing digital therapeutics
🏆 The Long Game
Phase 1 (Months 1-6): Bootstrap implementation + pilot results
Phase 2 (Months 6-12): Full validation study + paper submission Phase 3 (Year 2): Grant funding + enterprise implementation Phase 4 (Year 3): FDA validation pathway + commercial licensing
Revenue Potential: $10M+ (enterprise licensing + clinical validation) Academic Impact: New evaluation standard for AI consciousness research Regulatory Value: First FDA-pathway AI consciousness assessment
This bootstrap framework gives you everything needed to implement world-class AI memory testing on an academic budget while preserving the scientific rigor and regulatory potential for future scaling. Ready to make history! 🔥✨