Franz

Project Owner

Adaptive Compression with WILLIAM-on-MORK

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

Status

Overall Status
⏳ Contract Pending
Funding Transfered
$0_USD
Max Funding Amount
$100,000_USD

Funding Schedule

View Milestones

Milestone Release 1	$20,000 USD	Pending	TBD
Milestone Release 2	$40,000 USD	Pending	TBD
Milestone Release 3	$40,000 USD	Pending	TBD

Overview

This proposal develops WILLIAM-on-MORK, an adaptive compression and pattern discovery service for the Hyperon ecosystem. By integrating WILLIAM’s MDL-based incremental compression with MORK’s hypergraph indexing, it will enable Hyperon to discover, cache, and reuse structural patterns across reasoning processes. The focus is on integration with ECAN, PLN, and unification, where WILLIAM-on-MORK will compress and index traces and subgraphs to identify recurring structures and improve efficiency. The resulting system will serve as a core Hyperon service, later accessible via SingularityNET for broader adaptive compression use cases.

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

Arthur Franz, Ph.D., is an independent AI researcher and cofounder of OCCAM (Odessa Competence Center for AI & ML). With a background in physics, cognitive science, and algorithmic information theory, he authored A Theory of Incremental Compression and AGI conference papers on recursive learning. His work bridges theory and application, from inductive programming to financial modeling and AGI systems integration.

Project details

Usefulness
WILLIAM-on-MORK will provide a scalable foundation for Hyperon’s growth by turning compression itself into a learning mechanism. They allow Hyperon to continuously discover, cache, and reuse structural patterns across domains—improving reasoning, efficiency, and interoperability between components like PLN, unification, and chaining. This makes Hyperon self-improving and increasingly valuable with use.

Problem
The core problem addressed by this project is that current AI reasoning systems—symbolic, neural, or hybrid—lack a general, scalable way to discover, represent, and reuse patterns across domains. Each subsystem (language models, theorem provers, planners, learners) typically operates with task-specific heuristics and loses knowledge once a task is done. There is no shared, evolving mechanism for recognizing structure and reapplying it efficiently elsewhere

Solution
The project tackles this by introducing adaptive compression as a unifying principle of learning and reasoning. In essence, every system that learns from data, text, or experience is performing some form of compression: reducing redundancy while retaining predictive power. However, today’s systems do not treat this explicitly or reuse their compressive “discoveries.” Each model or algorithm starts from scratch, wasting computation and failing to generalize structural insights beyond its training scope.

By integrating WILLIAM, an incremental compression framework, with MORK, the Hyperon hypergraph kernel, the project creates a substrate that continuously discovers and reuses patterns in real time. Instead of memorizing outcomes, the system learns the templates of structure—reversible, parameterized abstractions that can be applied across data streams, domains, and reasoning contexts.

Resources needed
Someone helping me with the integration of WILLIAM with Hyperon components at a later stage of the project.

Open Source Licensing

Custom

WILLIAM is published under the Creative Commons Attribution-NonCommercial Licence

Links and references

occam.com.ua
https://www.youtube.com/@occam_labs
https://lifeboat.com/ex/bios.arthur.franz

Activity Summary

Milestones

3

Total

Discussion

0

Total Comments

Reviews

2

Total Posted

Project Team

1

Total People

Reviews & Rating

New reviews and ratings are disabled for Awarded Projects

Overall Community

5

from 2 reviews

5

2
4

0
3

0
2

0
1

0

Feasibility

5

from 2 reviews

Viability

0

from 2 reviews

Desirabilty

5

from 2 reviews

Usefulness

4.5

from 2 reviews

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2 ratings

Expert Review 1
Overall

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

Clear best fit and highly interesting proposal from credible researcher. Normal risk of deep research not panning out but reward warrants funding.

Expert Review 2
Overall

5.0
- Compliance with RFP requirements 5.0
- Solution details and team expertise 5.0
- Value for money 5.0
A perfect fit

A completely thought through system, integrating MORK and other Hyper componentry. Clear and relevant process and milestone and a very strong team.

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Overall

Confidence level that the project is possible at all Feasibility

0
Confidence in a successful outcome considering team, time, and budget Viability

0
Market fit - Balancing needs and benefits against competition Desirabilty

0
To what extent will the project help the AI platform grow? Usefulness

0

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Review Summary

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Expert Review

Overall

Confidence level that the project is possible at all Feasibility

0
Confidence in a successful outcome considering team, time, and budget Viability

0
Market fit - Balancing needs and benefits against competition Desirabilty

0
To what extent will the project help the AI platform grow? Usefulness

0

Review Headline

0 /50 chars

Review Summary

0 /5000 chars

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.

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.

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

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.

Total Milestones
3
Total Budget
$100,000_USD
Last Updated
28 Oct 2025

Milestone 1 - Research Plan

Status

😐 Not Started

Description

A comprehensive research plan will be developed, detailing the conceptual foundations of adaptive incremental compression, its integration with Hyperon, and the timeline for implementation. The framework design will outline data flow, module interfaces, and evaluation methods.

Deliverables

Research plan, agile task breakdown with timeline, and preliminary framework architecture.

Budget

$20,000 USD

Link URL

Milestone 2 - Core functionality and integration with MORK

Status

😐 Not Started

Description

Develop the core functionality of WILLIAM to efficiently compress multiple data streams and generate compressing graphs in sequence and image domains. Implement parameter elevation and b-compression to establish a performance baseline. Integrate with the MORK hypergraph kernel by extending WeightedTriemap nodes with additional statistical fields and enabling feedback between WILLIAM and MORK for adaptive pattern reuse.

Deliverables

Draft implementation, integration tests with MORK, initial compression benchmarks including acceleration of compression by MORK-assisted reuse of useful patterns.

Budget

$40,000 USD

Link URL

Milestone 3 - Optimization and deployment

Status

😐 Not Started

Description

Optimize the framework by transitioning from trees to DAGs, implementing search within Callables and parallelizing the algorithm. Use a fixed, small but general template library achieve domain specialization through MORK-assisted reuse of domain-relevant patterns. Deploy WILLIAM-on-MORK as an internal Hyperon service that continuously compresses and indexes traces, subgraphs, and activity patterns produced by PLN, backward/forward chaining, and ECAN. This allows Hyperon to cache, generalize, and reuse recurring reasoning patterns, effectively building a long-term memory of compressive templates. The system will also be made available as an external SingularityNET service for third-party adaptive compression tasks.

Deliverables

Optimized codebase, documentation, performance and compression reports, Hyperon integration tests on PLN/chaining/ECAN traces, and deployment-ready service on SingularityNET.

Budget

$40,000 USD

Link URL

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Expert Ratings

Reviews & Ratings

New reviews and ratings are disabled for Awarded Projects

Expert Review 1
Overall

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

Clear best fit and highly interesting proposal from credible researcher. Normal risk of deep research not panning out but reward warrants funding.

Expert Review 2
Overall

5.0
- Compliance with RFP requirements 5.0
- Solution details and team expertise 5.0
- Value for money 5.0
A perfect fit

A completely thought through system, integrating MORK and other Hyper componentry. Clear and relevant process and milestone and a very strong team.

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

Review Summary

0 /5000 chars

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.

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

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.

Franz

Project Owner

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