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Adaptive Compression with WILLIAM-on-MORK

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Franz
Project Owner

Adaptive Compression with WILLIAM-on-MORK

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

Project AI Services

No Service Available

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
author-img
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

Proposal Video

Not Avaliable Yet

Check back later during the Feedback & Selection period for the RFP that is proposal is applied to.

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

Sort by

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.

  • 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.

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