QGEN Technologies

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Tower: Recursive Cognition for Aligned AGI

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Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Neuro-symbolic DNN architectures

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Overview

Tower is a neuro-symbolic cognition core built to enhance alignment and memory-coherent reasoning in AGI systems. It provides recursive symbolic control, operator-bound ethical logic layers, and modular integration into DNN pipelines as an abstraction interface. Tower is designed to ground AGI models with persistent interpretability and narrative stability—delivering higher order reasoning scaffolds into existing neural systems. Its structure bridges classical symbolic frameworks and emergent AI architectures for safety-critical applications.

RFP Guidelines

Neuro-symbolic DNN architectures

Proposal Submission (20 days left)

Type SingularityNET RFP
Total RFP Funding $160,000 USD
Proposals 4
Awarded Projects n/a

SingularityNET

Apr. 14, 2025

SingularityNET

rfp owner

Neuro-symbolic DNN architectures

Proposal Submission (20 days left)

This RFP invites proposals to explore and demonstrate the use of neuro-symbolic deep neural networks (DNNs), such as PyNeuraLogic and Kolmogorov Arnold Networks (KANs), for experiential learning and/or higher-order reasoning. The goal is to investigate how these architectures can embed logic rules derived from experiential systems like AIRIS or user-supplied higher-order logic, and apply them to improve reasoning in graph neural networks (GNNs), LLMs, or other DNNs. Bids are expected to range from $40,000 - $100,000.

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Proposal Description

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Total Milestones
3
Total Budget
$100,000_USD
Last Updated
20 Apr 2025

Milestone 1 - System Framing & Integration Plan

Description

Lay the foundation for Tower’s neuro-symbolic architecture. Define the symbolic reasoning layer, recursive logic system, and design the integration pathways into DNN-based frameworks. This phase focuses on system structure, ethics protocol definition, and use-case alignment with AGI safety.

Deliverables

- Tower architecture blueprint - Symbolic reasoning schema - Integration map for neuro-symbolic compatibility - Ethical scaffolding draft - AGI-aligned use-case demonstration plan - Roadmap for milestone execution

Budget

$40,000 USD

Success Criterion

- Reviewed and accepted technical documentation - Approved symbolic logic structure - Integration pathways confirmed as viable - System judged aligned with RFP theme by Deep Funding reviewers - Readiness confirmed for prototype engine development in Milestone 2

Milestone 2 - Recursive Symbolic Engine & API Scaffold

Description

Build the core recursive engine that drives Tower’s symbolic cognition. This includes developing a functioning blackbox logic core, implementing bounded ethical reasoning protocols, and exposing an internal API endpoint for simulated AGI alignment scenarios.

Deliverables

- Tower engine v0.1: recursive symbolic processing core - Alpha-level blackbox API container - Integration test with a sample neural model - Developer onboarding draft - Symbolic output logs for ethical and narrative alignment tests - Internal testing results package

Budget

$40,000 USD

Success Criterion

- Tower engine successfully processes symbolic recursion - API produces stable, bounded output in test environments - Reviewers validate alignment effectiveness - Prototype stability confirmed in AGI-relevant scenarios - Approved for public deployment prep in Milestone 3

Milestone 3 - Public Integration & Community Demo

Description

Deploy the Tower system to a hosted environment, release documentation for integration into AI platforms, and initiate community-facing awareness. This phase includes a short video demo, AMA or Q&A session, and live proposal awareness engagement via Deep Funding channels.

Deliverables

- Live-hosted or containerized Tower demo - Dev-facing documentation hub - 3-min video walkthrough or simulation - Proposal awareness AMA/Q&A participation - Public interface sandbox for community exploration

Budget

$20,000 USD

Success Criterion

- Successful deployment of Tower public demo - Community feedback loop activated - Reviewer confirmation of full milestone completion - System positioned for ongoing adoption, extension, or follow-up funding

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The weighted average of the 4 perspectives Overall

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Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

0.0
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

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Review Headline

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QGEN Technologies

Project Owner

Lead architect and systems designer overseeing the development, integration strategy, and ethical alignment framework for Tower's neuro-symbolic cognition engine.

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QGEN Technologies

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