mohamed.bani

Project Owner

In the Topology of Thought

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Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Advanced knowledge graph tooling for AGI systems

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Overview

As an extention to KG, we propose a novel framework in which a language model recursively restructures a representation of Problem Space by evaluating reasoning-based proximity between problems. Using self-reflective queries, behavioural transfer tests, and iterative clustering, the system constructs a geometric topology in which vector proximity reflects epistemic similarity, not statistical co-occurrence. This enables the emergence of a structured reasoning space aligned with logical coherence. We argue that this architecture supports continual, insight-driven learning without external supervision, and may offer a path toward non-plateauing cognitive development in artificial intelligence.

Project Tags:
Community and Collaboration

RFP Guidelines

Advanced knowledge graph tooling for AGI systems

Internal Proposal Review

Type SingularityNET RFP
Total RFP Funding $350,000 USD
Proposals 40
Awarded Projects n/a

SingularityNET

Apr. 16, 2025

This RFP seeks the development of advanced tools and techniques for interfacing with, refining, and evaluating knowledge graphs that support reasoning in AGI systems. Projects may target any part of the graph lifecycle — from extraction to refinement to benchmarking — and should optionally support symbolic reasoning within the OpenCog Hyperon framework, including compatibility with the MeTTa language and MORK knowledge graph. Bids are expected to range from $10,000 - $200,000.

Proposal Description

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Total Milestones
6
Total Budget
$200,000_USD
Last Updated
27 May 2025

Milestone 1 - Architecture

Description

Finalize Architecture and Core Tools

Deliverables

Architecture spec for agents (Creative, Executioner, Scrutinizer & Ghost) base LLM chosen task definition for reasoning problems basic infra setup.

Budget

$25,000 USD

Success Criterion

Approved architecture document; LLM selected and tested; dataset outline created; core team onboarded.

Milestone 2 - Problem space

Description

Build Reflection Loop and Problem Space topology

Deliverables

Working self-reflection loop on ~10000 problems; first proximity signals captured; early clustering trials

Budget

$35,000 USD

Success Criterion

Loop shows reproducible proximity patterns; reflection leads to performance deltas on held-out problems.

Milestone 3 - Transformation & Clustering

Description

Implement Topology Transformation and Clustering

Deliverables

Functioning T transform and clustering pipeline; problem topology built and visualized; cluster-linked memory store.

Budget

$40,000 USD

Success Criterion

Clusters are internally consistent; problem placement correlates with reasoning similarity; memory retrieval functional.

Milestone 4 - Agents integration

Description

Integrate Agents in Recursive Loop

Deliverables

Fully connected agent loops (Creative → Executioner → Scrutinizer, Ghost→ Scrutinizer); performance tracking dashboard; early insight scoring.

Budget

$35,000 USD

Success Criterion

Loop executes end-to-end on 100+ problems; insights evaluated; Scrutinizer decisions reproducible.

Milestone 5 - LUPI heuristic

Description

Deploy Heuristic Contributors and Persistent Insight Layer

Deliverables

Contributor simulation layer; insight ranking across clusters; latent queue implementation for Creative agent.

Budget

$30,000 USD

Success Criterion

Insights reused with measurable performance improvement; differentiation heuristic tested and ranked.

Milestone 6 - Final evaluation

Description

Evaluate System and Publish Results

Deliverables

Performance comparison with baseline; polished whitepaper/report; system diagram for expansion.

Budget

$35,000 USD

Success Criterion

Demonstrated improvement on problem-solving tasks; public-facing artifact produced; peer/shareholder review positive.

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The weighted average of the 4 perspectives Overall

0.0
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

0.0

Review Headline

0 /50 chars

0 /5000 chars

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding. Viability (secondary) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary) Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference? Example:

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

Usefulness (secondary) This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?” For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?). For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things? Examples:

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

mohamed.bani

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