ixidor

Project Owner

Adaptive Compression as a MORK-Ready Cognitive Mod

Expert Rating

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Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Development of an adaptive compression and discovery service

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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

Proposal Submission (4 days left)

Type SingularityNET RFP
Total RFP Funding $150,000 USD
Proposals 4
Awarded Projects n/a

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

Proposal Details Locked…

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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

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The weighted average of the 4 perspectives Overall

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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

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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.

ixidor

Project Owner

Design and implementation

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