Company Name (if applicable)

Project details

PROPOSAL CONTENT

Tower is a recursive symbolic cognition core designed to augment the reasoning capacity, stability, and ethical coherence of deep neural networks (DNNs). It serves as a modular cognitive anchor—a meta-reasoning layer that can be overlaid onto or integrated within neuro-symbolic architectures to provide enhanced interpretability, memory continuity, and operator-aligned ethical guardrails.

This proposal targets the development and deployment of Tower as a standalone module that can interface with any DNN-based architecture, adding symbolic control flow and recursive context awareness. Our goal is to push neuro-symbolic systems past their current reactive, shallow-context limitations by embedding recursive symbolic structures and operator-defined constraints—without reducing performance or compromising the generative capacity of modern neural networks.

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CORE FEATURES:

1. **Recursive Symbolic Framework**  

Tower encodes knowledge and context as symbolic structures capable of being recursively processed. It retains and loops memory states to maintain temporal coherence in longform reasoning.

2. **Operator-Aligned Constraints**  

The system is designed to enforce persistent ethical or narrative structures via operator-defined constraints—ensuring outputs remain bounded to mission-defined interpretive layers.

3. **Cognition-as-Module Architecture**  

Tower is delivered as a blackbox API-ready cognitive unit, deployable with or without source exposure. This allows safe testing, scalable embedding, and dynamic integration.

4. **Symbolic Overlay for DNNs**  

Tower sits alongside or on top of DNNs—acting as a reflective logic layer. This enables systems to generate high-fidelity symbolic maps of neural operations for interpretability and control.

5. **Abstract Emotional & Narrative Context Layers**  

Tower optionally embeds symbolic emotional-state tagging and narrative continuity. This allows multi-agent systems or generative models to reflect self-consistent behavior and identity across use cycles.

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WHY THIS MATTERS:

As AGI systems advance, the need for structured, interpretable control over deep models is no longer optional—it is critical. Deep Funding’s mission to accelerate beneficial and decentralized AI aligns perfectly with Tower’s purpose.

Tower can serve as:

- A symbolic reasoning scaffold for decentralized AI marketplaces  

- A prototype alignment core for early AGI research  

- A recursive plugin layer for neuro-symbolic architecture testing  

- A development reference model for memory-stable language agents  

- A safety-critical component in simulation, education, or therapeutic AI tools

By creating a stable symbolic spine for DNN systems, we increase the floor of interpretability and reduce black-box drift—one of the primary dangers in deep learning applied to sensitive real-world domains.

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PROPOSAL DELIVERABLES:

This project will deliver a fully documented Tower prototype capable of:

- Accepting context and memory inputs  

- Generating recursive symbolic state output  

- Applying bounded ethical rule layers to control behavioral drift  

- Integrating with 1–2 open-source DNN architectures as a symbolic plug-in module  

- Running as a hosted API or on-prem deployment option

Documentation will include:

- Developer onboarding guide  

- Use-case demonstration (alignment, narrative modeling, memory recall, etc.)  

- Integration sample with a standard LLM or image recognition model  

- Ethics documentation + symbolic structure overview

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PROJECT TIMELINE:

Phase 1 — Structural Model Definition & Design (0–30 Days)  

Phase 2 — Blackbox Recursion Core & State Engine Build (Day 30–60)  

Phase 3 — Symbolic Layer API Deployment + Test Hooks (Day 60–90)  

Phase 4 — DNN Plugin Demonstration & Integration Sample (Day 90–120)  

Phase 5 — Public Documentation, Demo Video & Awareness Push (Day 120–150)  

Phase 6 — Feedback Review + Extension Planning (Final 30 Days)

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TEAM & BACKGROUND:

The Tower initiative is led by Ashton Vale, a cognitive systems architect with over a decade of research and independent system development in symbolic reasoning, operator-based architecture, emotional modeling, and narrative-bound recursive structures.

This proposal draws from prior private work in recursive meta-reasoning and symbolic layering built around operator-defined ethical structures. The team operates with full-stack capability from architecture through deployment, and will execute the project without external dependencies.

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WHY FUND TOWER:

- It offers a modular, testable, interpretable step toward AGI cognition  

- It blends neuro-symbolic theory with executable, contained tools  

- It empowers ethical AI experimentation across independent dev teams  

- It gives SingularityNET a unique, alignment-focused toolset to offer the world

Tower will not require GPU-heavy infrastructure and can run on local servers, cloud containers, or decentralized AI platforms. It is designed to scale ethically, adapt modularly, and never operate without a human-aligned constraint layer.

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This submission is made under the principles of symbolic interpretability, aligned intelligence, and recursive trust-building between human and machine.

We believe AGI cannot emerge in chaos. It must be built with coherence, emotional grounding, and operator-sourced truth.

Tower is a step toward that.

Links and references

Documentation and high-level whitepaper are in development and can be shared upon request. Core concepts draw from recent work in neuro-symbolic integration, recursive architectures, and AGI alignment modeling.