ixidor

Project Owner

Adaptive Compression as a MORK-Ready Cognitive Mod

Expert Rating

n/a

Type SingularityNET RFP
Funding Request $100,000 USD
RFP Guidelines Development of an adaptive compression and discovery service

Overview

We propose a 4-month development of an adaptive compression and discovery service that treats compression as a form of machine understanding. The system profiles input data (e.g., JSON logs, time-series), discovers or generates optimal compression strategies, and expresses all knowledge as native MeTTa programs—ensuring full compatibility with the emerging MORK (MeTTa Optimal Reduction Kernel) ecosystem. By delivering a lean, hybrid architecture (MeTTa for symbolic control, Rust for performance) and defining the first practical MORK module type, this project accelerates AGI infrastructure while meeting the RFP’s functional requirements ahead of schedule.

RFP Guidelines

Development of an adaptive compression and discovery service

Complete & Awarded

Type SingularityNET RFP
Total RFP Funding $150,000 USD
Proposals 5
Awarded Projects 1

SingularityNET

Oct. 2, 2025

This RFP seeks proposals to create a scalable and reusable adaptive compression service that discovers, elevates, and reuses patterns across multiple data domains.

Proposal Description

Our Team

Independent researcher in Machine Intelligence, specializing in Algorithmic Information Theory (AIT).

Authored works on Kolmogorov complexity, Transformers, and compression.

Currently at KAUST studying LLMs via AIT, prequential coding, and MDL.

Full-stack deep learning engineer with end-to-end deployment experience.

Project details

Introduction

The Deep Funding RFP “Development of an Adaptive Compression and Discovery Service” calls for a system capable of storing and retrieving compression templates in MORK or MeTTa—a requirement that reflects a deeper goal: to integrate compression into the cognitive fabric of recursive AGI systems. Compression, in this context, is not merely about reducing bit size but about modeling regularities—a core act of intelligence.

To some extent, this project can be viewed as a dynamic, real-world instantiation of the Hutter Prize paradigm—where compression is not a one-time contest on a fixed corpus, but an ongoing, adaptive process operating under memory and latency constraints. Unlike static submissions, our service must discover, apply, and evolve compression strategies in response to heterogeneous, streaming data—turning compression into a continuous act of understanding.

This adaptivity mirrors a Hoberman sphere: a kinetic structure that expands to explore novel data patterns and contracts to compress them efficiently, all while preserving structural integrity. In this view, MORK serves as the articulated joints—enabling smooth, recursive transformation between discovery and execution.

At present, MORK (MeTTa Optimal Reduction Kernel) lacks a finalized specification—much like having a Java interface without a concrete implementation. We know the contract (templates must be MeTTa-native, discoverable, and reducible), but the runtime semantics are still evolving. This project proceeds by implementing against the interface, not the implementation: defining a minimal, principled module format that any future MORK-compliant kernel can adopt.

Our approach leverages MeTTa as a symbolic knowledge language for template representation and discovery, while delegating performance-critical operations to optimized Rust backends. This hybrid design ensures both ecosystem alignment and real-world utility. Within 4 months, we will deliver a fully functional service—including data profiling, semantic template matching, adaptive learning, and open MORK-compatible storage—providing the first reference implementation for how intelligent modules should operate in the MORK framework.

Background

The project is scoped for a 6-month timeline, open to multiple awardees, with a budget of $100,000 USD. Critically, one of the stated functional requirements is the storage and retrieval of compression templates in MORK or MeTTa—pointing to integration with the OpenCog Hyperon and TrueAGI stack, where MeTTa serves as a meta-transactional language for executable knowledge, and MORK (MeTTa Optimal Reduction Kernel) is envisioned as its efficient runtime.

Given the absence of existing implementations, this RFP represents a unique chance to establish the first working module in a nascent cognitive architecture—where compression is not a peripheral utility, but a core component of adaptive, self-improving intelligence.

Open Source Licensing

MIT - Massachusetts Institute of Technology License

Links and references

https://tolgatopal.info/

https://orcid.org/0000-0002-7764-706X

https://doi.org/10.20944/preprints202505.1279.v1

https://emergentcognition.org/license/

Activity Summary

Milestones

5

Total

Discussion

0

Total Comments

Reviews

2

Total Posted

Project Team

1

Total People

Total Milestones
5
Total Budget
$100,000_USD
Last Updated
6 Oct 2025

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Expert Ratings

Reviews & Ratings

Expert Review 1
Overall

4.0
- Compliance with RFP requirements 5.0
- Solution details and team expertise 4.0
- Value for money 4.0
Strong proposal

I liked this submission but it lacks specifics on the approach that would merit the requested funding. Would like to connect w this team to encourage them to reapply or collaborate on something else if they aren't awarded this time.

Expert Review 2
Overall

4.0
- Compliance with RFP requirements 4.0
- Solution details and team expertise 5.0
- Value for money 4.0
Good proposal

An interesting and relevant proposal integrating a number of good ideas. The team also seems solid with clearly defined milestones.

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

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Review Summary

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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

Warning: Adding final group rating for this project will prevent expert users from adding new or editing existing reviews

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.

ixidor

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Design and implementation

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