Development of an adaptive compression and discovery service

Type SingularityNET RFP
Total RFP Funding $150,000 USD
Proposals 5
Awarded Projects n/a

Overview

Est. Execution Time
⏱️ 6 Months
Proposal Winners
🏆 Multiple
Max Funding / Proposal
$100,000 USD

RFP Details

Short summary
Main purpose
Long description
Functional Requirements
Non-functional Requirements
Team requirements
Main evaluation criteria
Other resources

Short summary

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

Main purpose

Provide efficient compression and feature extraction for AGI workloads.
Enable recursive self-improvement
Expose the system as both an internal Hyperon service and an external SingularityNET service.
Demonstrate broad applicability across tasks such as transformers, theorem proving, evolutionary program search, financial prediction, and memory systems

Long description

Context and background:

SingularityNET Foundation, in collaboration with other partners such as the OpenCog Foundation and TrueAGI, is working toward a scalable implementation of the Hyperon AGI framework running on decentralized infrastructure, and toward implementation of the PRIMUS cognitive architecture within this framework. Hyperon and PRIMUS are complex systems involving multiple components, which need to demonstrate appropriate functionalities both individually and in combination. This RFP aims to address a portion of this overall need, via funding the initial iteration of one significant component of PRIMUS within Hyperon.

Expected Outcomes:

A working compression service, preferably integrated into Hyperon.
- For proposals with successful Hyperon integration up to $100k will be awarded, otherwise only up to $60k
Demonstrated recursive self-improvement through adaptive compression.
A library of reusable compression templates across sequence and image domains.
Deployments as:
- Internal service for Hyperon components (ECAN, PLN, unification).
- External service via SingularityNET marketplace.
Application demos in multiple domains (transformers, theorem proving, evolutionary search, finance, episodic memory).

Functional Requirements

Must Have:

Integration of Weighted Triemap statistics (local count, subtree count, compress gain sum, topk).
Storage and retrieval of compression templates in MORK or MeTTa.
End-to-end pipeline demonstrating recursive self-improvement

Could Have:

LLM-driven template proposals for new domains.
Multi-domain template transfer (sequence → image).
Internal deployment on Hyperon and external deployment on SingularityNET marketplace.
Rust acceleration for hot loops (value propagation, DAG enumeration).
Compression of leaves in parallel.
Comparison between tree-based and DAG-based approaches.
Combine/update pure MDL by weakness-guided search.

Non-functional Requirements

Programming language: The system should be implemented in languages compatible with Hyperon (preferably in MeTTa or MORK).
Performance: Capable of real-time streaming compression with constant memory.
Usability: Simple API for easy usage for compression and possibly prediction.
Maintainability: Modular design with clear separation of compression core, template library, and Hyperon integration.
Documentation: Provide clear, well-organized documentation detailing the methods, architecture, and processes used. Ensure that the solution can be replicated, extended, or adapted for future research, including instructions for reproducing results and modifying the system for other applications.

Team requirements

Proposers should demonstrate expertise in:

Algorithmic information theory & compression.
Graph search and optimization algorithms.
Practical implementation in Python, Rust, and possibly MeTTa.
Prior work in AGI research.

Main evaluation criteria

Proposals will be evaluated on the following criteria:

Alignment with requirements and objective
- Does the proposal meet the requirements and advances the objectives of the RFP.
Pre-existing R&D
- Has the team previously done similar or related research or development work in other platforms / languages / contexts? Prior experience in incremental compression.
Team competence
- Does the team have relevant skills? Strong track record in AI/AGI development.
Cost
- Does the proposal offer good value for money?
Timeline
- Does the proposal include a set of clearly defined milestones? We highly recommend submitting proposals with project milestones along the lines of the following:

- Milestone 1: Submit a thorough research plan outlining and detailing the approach and work to be done. Deliverables: detailed research plan, agile breakdown of tasks with timeline, and framework design. 20% of grant
- Milestone 2: Complete initial development of the framework definitively showing implementation of the conceptual underpinnings of the RFP along with preliminary testing. Deliverables: draft implementation, initial testing results, and analysis against standard benchmarks. 40% of grant
- Milestone 3: Submit all final materials as committed to in the grant proposal. Deliverables (as applicable): final report with performance analysis, code, framework demonstration, documentation, recommendations, websites, etc. 40% of grant

Other resources

Hyperon and related AI-platforms are quickly evolving! This is a bit of a moving target, but the internal SingularityNET team will be available for help and expert advice, where needed. Also included:

SingularityNET technology links
Educational materials and resources for learning MeTTa
SingularityNET holds MeTTa study group calls every other week. Proposers are welcome to attend for support from our researchers and community.
Recurring Hyperon study group calls for community are currently being planned. These will cover MOSES, ECAN, PLN, and other key components of the OpenCog and PRIMUS Hyperon cognitive architectures.
Access to the SingularityNET World Mattermost server, with a dedicated channel for discussion and support among the RFP-winning teams and SingularityNET resources.