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Adaptive Compression as a MORK-Ready Cognitive Mod

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Adaptive Compression as a MORK-Ready Cognitive Mod

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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
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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/

Proposal Video

Not Avaliable Yet

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

  • Total Milestones

    5

  • Total Budget

    $100,000 USD

  • Last Updated

    6 Oct 2025

Milestone 1 - Month 1

Description

Profiling & Template Schema

Deliverables

Implement a structural data profiler for common formats (JSON, logs, time-series) and define a MeTTa-native schema for compression templates, including pattern conditions, executable actions, and metadata (e.g., tags, input schema, performance hints).

Budget

$20,000 USD

Milestone 2 - Month 2

Description

Hybrid Runtime & Discovery Engine

Deliverables

Build a hybrid execution layer: MeTTa handles semantic discovery via pattern matching over the template registry, while Rust backends perform high-efficiency compression (e.g., zstd, delta encoding, dictionary coding). Templates are stored as versioned .metta files with lightweight manifests.

Budget

$30,000 USD

Milestone 3 - Month 3

Description

Adaptive Learning Loop

Deliverables

Close the feedback cycle: after each compression, evaluate metrics (ratio, speed, reconstruction fidelity) and use AIT-inspired heuristics—such as favoring shorter MeTTa descriptions—to refine or generate new templates. Integrate template synthesis into the knowledge base.

Budget

$25,000 USD

Milestone 4 - Month 4

Description

Validation & MORK Integration Prep

Deliverables

Test the system on diverse real-world datasets, document the module interface, and publish the code as open source. Produce a compatibility guide for future MORK runtime integration, establishing this service as a reference implementation.

Budget

$15,000 USD

Milestone 5 - Months 5–6

Description

Community Integration & Ecosystem Readiness

Deliverables

Engage with the AGI community, incorporate feedback, perform stress testing, and collaborate directly with MORK/MeTTa developers to refine interoperability.

Budget

$10,000 USD

Join the Discussion (0)

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.

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