ITERON

A Model-Agnostic Autonomous Reasoning System That Learns From Failure

Intelligence lives in the loop, not in the model.

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📚 Table of Contents

• Abstract
• The Problem ITERON Solves
• What ITERON Is NOT
• Core Intelligence Loop
• Architecture Overview
• Why ITERON Is Different From GPT / Gemini
• Long-Horizon Memory
• Example: Learning Over Time
• Demo Instructions
• Use Cases
• Research Positioning
• Core Claim (Defensible)

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Abstract

ITERON is an autonomous reasoning system designed to improve decision quality over time by learning from structured failure.
Unlike chatbots or agent frameworks that rely on large language models (LLMs) to both generate and judge outputs, ITERON strictly separates creativity from intelligence.

LLMs are used only as proposal engines.
Evaluation, reflection, memory, and learning are enforced by a deterministic, model-agnostic architecture.

ITERON persists experience across executions, detects repeated failure patterns, abandons unproductive strategy classes, and converges toward higher-quality decisions under constraints.

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The Problem ITERON Solves

Modern LLMs are fluent but not self-correcting.

They:

• Do not remember past failures
• Reproduce the same weak ideas across sessions
• Judge their own outputs
• Optimize for plausibility rather than correctness

As a result, decision quality does not improve over time.

ITERON was built to solve this.

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What ITERON Is NOT

ITERON is not:

• A chatbot
• A prompt-engineering project
• An AutoGPT-style tool runner
• A replacement for GPT, Gemini, or Claude

ITERON does not attempt to sound intelligent.
It attempts to become correct over time.

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Core Intelligence Loop

ITERON implements a recursive intelligence loop:

Generate → Evaluate → Reflect → Improve → Repeat

Loop Responsibilities

• Generate
  Propose candidate strategies (LLM-backed or rule-based).

• Evaluate
  Judge quality using external, deterministic criteria.

• Reflect
  Abstract why failures occurred (pattern detection).

• Improve
  Modify future behavior based on accumulated failures.

• Repeat
  Continue until convergence or stopping conditions are met.

Intelligence emerges from recursion and constraint, not from model size.

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Architecture Overview

ITERON is divided into two strictly separated layers:

1. Core Intelligence (Model-Agnostic)

Responsible for:

• Scoring and evaluation
• Failure detection
• Reflection and abstraction
• Long-horizon memory
• Strategy-class learning
• Best-output selection
• Exploration control

This layer contains all intelligence.

2. LLM Integration (Replaceable Tools)

Responsible for:

• Idea generation
• Optional critique explanation

LLMs:

• Do NOT score
• Do NOT decide success
• Do NOT store memory
• Do NOT control learning

Models are interchangeable.
Architecture is permanent.

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Why ITERON Is Different From GPT / Gemini

Capability	GPT / Gemini	ITERON
Stateless	Yes	No
Learns from failure	No	Yes
Memory across runs	No	Yes
Self-critique	Superficial	Structural
External objective	No	Yes
Improves decision quality over time	No	Yes

GPT answers questions.
ITERON improves thinking.

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Long-Horizon Memory

ITERON maintains persistent memory across executions, including:

• Canonical failure patterns
• Strategy-class performance statistics
• Decaying trust in unproductive heuristics

This allows the system to:

• Abandon failing strategy classes
• Avoid repeating mistakes
• Converge faster in future runs

Memory biases behavior but never overrides evaluation.

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Example: Learning Over Time

Before Memory

• Influencer-based strategies repeated
• Same failures across runs
• Slow or no convergence

After Memory

• Influencer-based strategies abandoned
• Forced exploration of new strategy classes
• Faster convergence to higher-quality strategies

Learning persists even after restarting the system.

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Demo Instructions

Run a basic experiment

python -m core_intelligence.experiments.run_basic

Run with failure injection

python -m core_intelligence.experiments.failure_injection

Observe learning

1. Run the experiment multiple times
2. Inspect core/memory_store.json
3. Notice reduced repetition and faster convergence

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Use Cases

ITERON is suitable for:

• Business strategy exploration
• Product go-to-market planning
• Research hypothesis iteration
• Policy and system design
• Long-term decision optimization

It is not intended for:

• Casual Q&A
• Creative writing
• One-shot responses

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Research Positioning

ITERON is inspired by:

• Reinforcement learning principles
• Cognitive architecture research
• Early AGI system design

This project explores how intelligence can emerge from:

• Recursive evaluation
• Constraint enforcement
• Failure-driven learning
• Persistent memory

— independent of model scale.

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Core Claim (Defensible)

ITERON demonstrates that:

• Intelligence can be architected
• Models can be replaceable
• Learning can occur without training
• Failure is more valuable than fluency

One-Line Summary

ITERON is a model-agnostic autonomous reasoning system that improves decision quality over time by learning from structured failure instead of prompts.