Matt Zimak

Project Owner

Symbolica: Neural-Symbolic DNN (Music Demo)

Expert Rating

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Type SingularityNET RFP
Funding Request $100,000 USD
RFP Guidelines Neural-symbolic DNN architectures

Overview

Symbolica introduces an innovative neural-symbolic architecture, seamlessly integrating differentiable symbolic logic constraints within advanced neural frameworks (Transformers and VAEs). Our unique approach uses improved PyNeuraLogic techniques, allowing neural models to intrinsically learn from symbolic rules. We initially validate Symbolica using music as a practical demo due to music’s dual symbolic and continuous nature, clearly demonstrating the system’s general-purpose strength and applicability across broader domains.

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RFP Guidelines

Neural-symbolic DNN architectures

Complete & Awarded

Type SingularityNET RFP
Total RFP Funding $160,000 USD
Proposals 17
Awarded Projects 1

SingularityNET

Apr. 14, 2025

SingularityNET

rfp owner

Neural-symbolic DNN architectures

Complete & Awarded

This RFP invites proposals to explore and demonstrate the use of neural-symbolic deep neural networks (DNNs), such as PyNeuraLogic and Kolmogorov Arnold Networks (KANs), for experiential learning and/or higher-order reasoning. The goal is to investigate how these architectures can embed logic rules derived from experiential systems like AIRIS or user-supplied higher-order logic, and apply them to improve reasoning in graph neural networks (GNNs), LLMs, or other DNNs. Bids are expected to range from $40,000 - $100,000.

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Proposal Description

Our Team

Our experienced multidisciplinary team combines deep expertise in neural-symbolic AI, advanced neural networks, symbolic logic integration, and software development. Proven in delivering innovative AI-driven solutions, our compact, agile team is uniquely positioned for successful execution.

Company Name (if applicable)

Deep Noise

Project details

Symbolica represents a novel approach in neural-symbolic architectures, addressing current challenges by embedding differentiable symbolic logic directly into neural networks. We innovate by utilizing PyNeuraLogic to encode domain-specific rules into fuzzy factor graphs, turning traditionally rigid, post-hoc constraints into intrinsic, differentiable components of the training process.

This enables our Transformer and Variational Autoencoder (VAE) models to inherently respect and learn complex symbolic rules - dramatically improving coherence, reducing rule violations by ≥50%, and enhancing interpretability. Crucially, our method preserves generative diversity and creativity by guiding rather than restricting neural outputs.

We validate the architecture through three specific functionalities: a neural-symbolic sound character analyzer, a MIDI-based chord recognition module, and a melody reasoning analysis module. These tools serve as practical benchmarks demonstrating Symbolica’s effectiveness and flexibility.

We select music as an optimal initial demo due to its balanced symbolic (chord progressions, keys, scales) and continuous (timbre, dynamics) attributes. Through rigorous evaluation in this domain, Symbolica demonstrates clear evidence of robust performance and versatility, confirming its broad applicability for diverse neural-symbolic tasks.

Symbolica sets a replicable standard for neural-symbolic integration, significantly advancing general-purpose beneficial AGI development, aligned closely with SingularityNET’s strategic vision.

Links and references

Team links

Matt Zimak - https://www.linkedin.com/in/mattzimak/
Patrik Gudev - https://www.linkedin.com/in/patrik-gudev/
Nikolai Russkikh - https://www.linkedin.com/in/nikolay-russkikh-b389a2239/
Rithik Kundu - https://www.linkedin.com/in/rithik-kundu/

Activity Summary

Milestones

3

Total

Discussion

0

Total Comments

Reviews

0

Total Posted

Project Team

2

Total People

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

Milestone 1 - Research & Architecture Design

Description

Conduct comprehensive research to detail innovative neural-symbolic architecture and differentiable symbolic logic integration. Clearly outline methodologies, tasks, timelines, and framework design, establishing the foundational plan for subsequent development.

Deliverables

Detailed research plan, differentiable logic integration plan, agile breakdown of tasks with timelines, architecture design documentation, and clearly defined evaluation criteria.

Budget

$20,000 USD

Milestone 2 - Initial Architecture Implementation

Description

Develop an initial version of Symbolica architecture, explicitly demonstrating integration of differentiable symbolic constraints into neural frameworks. Validate the conceptual underpinnings through initial practical implementations using sound character analysis, MIDI-based chord recognition, and melody reasoning as internal benchmarks.

Deliverables

Draft implementations of neural-symbolic sound character analyzer, MIDI-based chord recognition module, melody reasoning analysis module, preliminary benchmark results (≥25% rule violation reduction), initial testing results, and internal technical documentation.

Budget

$40,000 USD

Milestone 3 - Final Validation & Demo

Description

Complete the optimized Symbolica architecture, demonstrating robust integration of differentiable symbolic logic. Conduct comprehensive validation within the music domain, clearly showcasing significant performance improvements and interpretability enhancements.

Deliverables

Fully functional Symbolica architecture with finalized neural-symbolic sound analyzer, MIDI-based chord recognition solution, melody reasoning module, comprehensive final report (≥50% rule violation reduction, coherence improvement), internally accessible documented codebase, detailed technical documentation, and practical demonstration of Symbolica’s capabilities.

Budget

$40,000 USD

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The weighted average of the 4 perspectives Overall

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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

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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

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Matt Zimak

Project Owner

As Project Owner, Matt leads overall project direction, strategy alignment, stakeholder management, and integration of technical deliverables to ensure successful execution and outcome.

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Account Pending

Technical Owner

As Technical Owner, Patrik oversees all technical development aspects, including neural-symbolic framework integration, methodology implementation, and ensures alignment with strategic objectives.

Account Pending