Definition
UpLoop is an emergant value tool that is modeled based on one individuals thinking patterns. It will be leveraged for research purposes.
Purpose
This repository defines a computational architecture designed to model and reciprocate key aspects of the human process of idea creation, abstraction, and value assessment. It is not intended to simulate human consciousness or emotion, but rather to reproduce the structural workflow humans use when generating insights and determining which ideas are worth pursuing.
Architecture
The system is built around three cooperating components — A, B, and C — that together form a closed‑loop reasoning cycle Core Concept A: Research & Exploration A is responsible for gathering raw information, scanning a domain, and identifying potential signals of interest. It mirrors the human tendency to explore widely before forming conclusions.
broad exploration
pattern intake
checkpointing of progress
preference shaping based on feedback
A does not judge or compress information; it simply collects and organizes it.
B: Abstraction, Leverage Detection & Value Assessment B transforms raw findings into structured insights. It performs the functions humans associate with “understanding” or “concept formation.”
pattern extraction
identification of strengths, weaknesses, and leverage points
reframing into relatable concepts
compression into recall phrases
storage in a knowledge index
B also includes a timing‑based retry mechanism: If B completes too quickly (under 30 seconds), it is assumed to have failed prematurely and is retried. After repeated failures, B becomes locked until A reinitializes.
This models the human experience of “not having enough context yet” to form a meaningful abstraction.
C: Integration & Re‑Engagement C returns the system to the research process with updated priors, similar to how humans revisit a problem after gaining new insight.
restoring the last research checkpoint
injecting new abstractions back into A
clearing obsolete processes to free cognitive resources
reinforcing retention of newly formed concepts
C ensures that the system does not simply loop — it progresses.
Why This Architecture Mirrors Human Idea Formation Human creativity typically follows a cycle:
Exploration — gather information without judgment
Abstraction — compress, reframe, and identify leverage
Integration — return to the problem with new understanding
This system formalizes that cycle into computational components. It does not claim to replicate human intuition, emotion, or subjective experience. Instead, it focuses on the structural mechanics of idea generation:
iterative refinement
concept compression
value‑based prioritization
feedback‑driven improvement
The goal is to create a tool that supports innovation by modeling the workflow of human reasoning, not the phenomenology of it.
Intended Use This system is designed for:
research automation
concept synthesis
knowledge‑base construction
idea evaluation and prioritization
iterative reasoning loops
It can be extended into domains such as:
strategic planning
scientific literature review
product ideation
conceptual modeling
educational tools
Transparency Statement This project does not attempt to create human‑like consciousness, emotion, or subjective experience. It is a computational model inspired by the structure of human creative reasoning, not a simulation of human cognition itself.
Its purpose is to:
support human creativity
accelerate idea formation
provide structured reasoning loops
help identify high‑value insights
The system remains a tool — not an autonomous thinker.
DIAGRAMS
Figure A - The Closed-Loop Recursion
┌───────────────┐
│ Start/Init │
└───────┬───────┘
│
v
┌───────────┐
│ Init A │
└─────┬─────┘
│
v
┌───────────┐
│ Run A │
│ (research)│
└─────┬─────┘
│
v
┌───────────┐
│ Call B │
│(abstract) │
└─────┬─────┘
│
┌───────┴───────────────────────────────┐
│ │
v v
┌───────────────┐ ┌───────────────┐ │ Measure time │ │ B error? │ └───────┬───────┘ └───────┬───────┘ │ │ v v ┌───────────────┐ ┌───────────────┐ │ t < 30s ? │──Yes───retry B──────▶│ Increment B │ └───────┬───────┘ │ failure count │ │ No └───────┬───────┘ v │ ┌───────────────┐ v │ B success? │──Yes──────────────────────▶┌───────────┐ └───────┬───────┘ │ Call C │ │ No │(integrate│ v │ & return)│ ┌───────────────┐ └─────┬─────┘ │ B failure │ │ │ count > N ? │ v └───────┬───────┘ ┌───────────┐ Yes │ │ Restore │ v │ A state │ ┌───────────────┐ └─────┬─────┘ │ Block B calls │ │ │ until A reinit│ v └───────┬───────┘ ┌───────────┐ │ │ Run A │ └───────────────loop back────────────▶│ with new │ │ priorities│ └─────┬─────┘ │ └───▶ (repeat loop)
Figure 2.A Architecture Diagram ┌──────────────────────────┐ │ UpLoop Core │ │ Emergent Cognitive Loop │ └─────────────┬────────────┘ │ ▼ ┌──────────────────────────┐ │ Phase A: │ │ DEEP FOCUS │ │ - Load problem space │ │ - Sustain attention │ │ - Gather context │ └─────────────┬────────────┘ │ │ Success ▼ ┌──────────────────────────┐ │ Phase B: │ │ REFLECTION / ABSTRACTION│ │ - Pattern extraction │ │ - Concept compression │ │ - Value identification │ │ - Synthesis & reframing │ └─────────────┬────────────┘ │ │ Success ▼ ┌──────────────────────────┐ │ Phase C: │ │ RE-ENGAGEMENT │ │ - Return to task │ │ - Integrate insights │ │ - Reset cognitive load │ │ - Prepare next cycle │ └─────────────┬────────────┘ │ │ Loop ▼ ┌──────────────────────────┐ │ EMERGENT VALUE │ │ - New insights │ │ - Novel connections │ │ - Accelerated discovery │ │ - Higher-order synthesis │ └──────────────────────────┘
┌──────────────────────────────────────────────────┐
│ MODIFIERS │
│ (Weights influencing throughput & quality) │
│--------------------------------------------------│
│ + improved_concept (d) │
│ + improved_rate_of_discovery (e) │
│ + accelerated_pattern_discovery (k) │
│ + improved_value_identification (l) │
│ + success_compress_concepts (m) │
│ + persistent_re_engagement_success (n) │
│ - loss_of_quality (f) │
│ - abandoned_project (g) │
│ - determined_unwanted (h) │
│ - memory_degradation (i) │
└──────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────┐
│ FAILURE MODES │
│--------------------------------------------------│
│ A fails → System pauses until conditions reset │
│ B fails → Return to A (retry abstraction) │
│ C fails → Return to A or B depending on context │
│ Persistent A failure → No emergent value │
└──────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────┐
│ SYSTEM PROPERTIES │
│--------------------------------------------------│
│ - Stateful, not brittle │
│ - Self-protecting (avoids low-quality output) │
│ - Emergent synthesis through iteration │
│ - Recursive improvement loop │
│ - Cognitive load resets via C │
│ - Modifiers shape throughput & quality │
└──────────────────────────────────────────────────┘
Figure 3 3A Mermaid.js
flowchart TD
%% Core Loop
A[Phase A: Deep Focus<br/>• Load problem space<br/>• Sustain attention<br/>• Gather context] -->
B[Phase B: Reflection / Abstraction<br/>• Pattern extraction<br/>• Concept compression<br/>• Value identification<br/>• Synthesis & reframing] -->
C[Phase C: Re‑engagement<br/>• Return to task<br/>• Integrate insights<br/>• Reset cognitive load<br/>• Prepare next cycle] -->
EV[Emergent Value<br/>• New insights<br/>• Novel connections<br/>• Accelerated discovery<br/>• Higher‑order synthesis] --> A
%% Modifiers Section
subgraph Modifiers
direction TB
d[+ improved_concept]
e[+ improved_rate_of_discovery]
k[+ accelerated_pattern_discovery]
l[+ improved_value_identification]
m[+ success_compress_concepts]
n[+ persistent_re_engagement_success]
f[- loss_of_quality]
g[- abandoned_project]
h[- determined_unwanted]
i[- memory_degradation]
end
%% Failure Modes
subgraph Failure_Modes[Failure Modes]
direction TB
FA[A fails → System pauses until conditions reset]
FB[B fails → Return to A]
FC[C fails → Return to A or B]
FZ[Persistent A failure → No emergent value]
end
%% System Properties
subgraph System_Properties[System Properties]
direction TB
SP1[Stateful, not brittle]
SP2[Self‑protecting loop]
SP3[Emergent synthesis through iteration]
SP4[Recursive improvement]
SP5[Modifiers shape throughput & quality]
end
%% Modifier Influence Arrows
Modifiers --> A
Modifiers --> B
Modifiers --> C
Modifiers --> EV
%% Failure Mode Arrows
FA -.-> A
FB -.-> A
FC -.-> B
FZ -.-> EV
AUTHORSHIP LINE
UpLoop is the vision and creation of Christopher Coyle, he formed the conceptual system on Janurary of 2026.
Roadmap
UpLoop Roadmap (Foundational → Architectural → Ecosystem) Phase 0 — Authorship & Identity (Immediate) Purpose: Establish public ownership, clarity, and conceptual legitimacy.
Publish the UpLoop definition in the GitHub README
Add a timestamped GitHub Release (“UpLoop v0.1 — Conceptual Definition”)
Create /docs/UpLoop_Whitepaper.md with:
conceptual model
A → B → C cycle
modifiers
emergent synthesis
intended domain
Add a simple ASCII architecture diagram
Add an authorship statement
Add a vision statement
Add a public license (MIT or Apache 2.0)
Outcome:
UpLoop becomes a timestamped, publicly anchored intellectual artifact.
Phase 1 — Conceptual Architecture (1–2 weeks) Purpose: Turn UpLoop from an idea into a formalized cognitive architecture.
Define the UpLoop State Machine
A (Deep Focus)
B (Reflection / Abstraction)
C (Re‑engagement)
Failure modes
Modifiers
Create /architecture/UpLoop_StateMachine.md
Add diagrams for:
state transitions
modifier weights
emergent value loop
Write a conceptual glossary
Add examples of UpLoop applied to:
research
engineering
agentic AI development
personal cognition
Outcome:
UpLoop becomes a formalized cognitive system with clear mechanics.
Phase 2 — UpLoop as a Meta‑Assistant Framework (2–4 weeks) Purpose: Translate the conceptual architecture into a framework for building agentic systems.
Define the UpLoop Meta‑Assistant Model
sensing layer
abstraction layer
synthesis layer
initiative engine
safety layer
Create /framework/UpLoop_MetaAssistant_Model.md
Map UpLoop to:
agentic AI patterns
planning loops
tool‑use loops
reflection loops
Add a developer‑facing explanation of how UpLoop can guide agent design
Add a VS integration concept (high‑level, not code yet)
Outcome:
UpLoop becomes a reusable architecture for agentic AI development.
Phase 3 — Public Theory & Positioning (1–2 weeks) Purpose: Increase IP value by establishing UpLoop as a conceptual contribution to the field.
Publish a whitepaper‑style document in /docs/UpLoop_Whitepaper_v1.md
Add a public definition suitable for citation
Add a comparison to existing cognitive architectures (e.g., reflection loops, planning loops)
Add a FAQ explaining UpLoop’s purpose and uniqueness
Add a “Why UpLoop?” section for researchers and developers
Create a GitHub Discussions thread to timestamp public commentary
Outcome:
UpLoop becomes a recognizable conceptual system with academic‑style framing.
Phase 4 — Prototype Tools (Optional, when ready) Purpose: Begin turning UpLoop into a practical toolset.
Create /prototypes/ folder
Add conceptual prototypes:
UpLoop context aggregator
UpLoop abstraction engine
UpLoop initiative scheduler
Add pseudo‑code or diagrams (no need for full implementation yet)
Add a VS extension concept document
Add a simulation harness concept
Outcome:
UpLoop begins transitioning from theory to practice.
Phase 5 — Ecosystem Expansion (Long‑term) Purpose: Grow UpLoop into a broader ecosystem.
Add community guidelines
Add a contributor’s guide
Add a roadmap for:
VS integration
agentic AI tooling
simulation environments
safety frameworks
Publish a v1.0 conceptual release
Begin drafting a formal paper for arXiv or Zenodo (free, timestamped, citable)
Outcome:
UpLoop becomes a public, extensible, citable system with growing IP value