Our Team

Prasad - 6+ years of engineering experience, w deep expertise in decentralized systems. Bachelor’s in CS Engineering.

Harsh - Oversees backend architecture and service design. 

Akash - Specializes in scalable backend services. Designs & implements core components for indexing, storage, and event processing pipelines.

Kartik - Delivers across backend and frontend layers. Works on API systems, dashboard integrations, and end-to-end feature development with a focus on reliability and consistency.

Company Name (if applicable)

Project details

The Benchmind project proposes the creation of a robust, extensible, and purpose-built benchmarking suite to evaluate the reasoning capabilities of symbolic and neuro-symbolic AGI systems within the Hyperon framework. Designed specifically for the MeTTa language and MORK hypergraph backend, Benchmind will serve as a standard for measuring progress in reasoning-rich AGI by providing a suite of realistic cognitive tasks, datasets, metrics, and tooling. The project will offer quantitative evaluations across reasoning types — including multi-hop question answering, analogical reasoning, and hypothesis generation — and support diverse reasoning agents (symbolic, probabilistic, and LLM-augmented). Benchmind will accelerate research and development in AGI systems built atop Hyperon by making reasoning performance measurable, comparable, and improvable.

Introduction

As AGI development accelerates, symbolic knowledge representation and reasoning systems — such as OpenCog Hyperon’s Atomspace and the MeTTa language — are playing a central role in organizing and manipulating structured knowledge at scale. While much effort has been focused on building and maintaining these symbolic knowledge graphs (e.g., via MORK), there is currently no standardized, system-aligned framework for evaluating how well such graphs and their associated agents perform in actual reasoning tasks.

Existing KG benchmarks (e.g., for link prediction or triple classification) do not test the kind of advanced, compositional reasoning that AGI systems require. They also rarely align with the semantics or architecture of Hyperon, making them unsuitable for teams working in the MeTTa ecosystem.

This lack of a standard evaluation protocol:

• Slows down iteration and progress in symbolic AGI tooling.

• Obscures bottlenecks in reasoning accuracy, tractability, or coverage.

• Prevents effective comparison of approaches across teams and methods.

Benchmind is designed to fill this gap.

Goals and Scope

Benchmind will develop:

1. A suite of benchmark tasks for symbolic and neuro-symbolic AGI reasoning, designed around the MeTTa/MORK environment and covering a range of reasoning capabilities crucial to AGI development.

2. Task categories and datasets that reflect real-world and AGI-relevant cognitive challenges:

   • Multi-hop factual and logical question answering.

   • Analogical reasoning and pattern completion.

   • Hypothesis generation and abductive reasoning.

3. Evaluation metrics and tooling to automatically assess reasoning performance, path efficiency, accuracy, and robustness.

4. Interfaces and APIs to plug in different reasoning agents, including symbolic engines (e.g., MeTTa queries, PLN), probabilistic solvers, and LLM-augmented modules.

5. End-to-end pipeline allowing other developers to benchmark their knowledge graphs or agents within the Hyperon ecosystem using minimal setup.

Benchmind will act as a “reasoning stress-test” for symbolic AGI agents, offering interpretability, reproducibility, and diagnostic insight into the strengths and weaknesses of different approaches.

Technical Architecture

Benchmind consists of four primary components:

1. Task Library (Cognitive Benchmarks)

A modular task framework where each benchmark task is defined via:

• A knowledge graph scenario or dataset (loaded into MORK).

• A reasoning objective (e.g., answer a multi-hop query, complete a relational analogy, generate a plausible new link).

• Ground-truth answers or expected outputs for evaluation.

Initial task categories:

• Multi-hop QA: Answering questions that require traversing 2–5 relational hops in the graph.

• Analogical reasoning: Given A:B::C:?, identify D such that the relation between C and D mirrors A and B.

• Abductive/hypothesis generation: Given partial or incomplete facts, suggest plausible inferences that complete the knowledge context.

2. Reasoning Agent Interfaces

A flexible plug-in interface to test different types of reasoners:

• Pure MeTTa logic-based agents.

• Probabilistic inference engines (e.g., PLN).

• LLM-augmented retrieval or ranking agents (LLM queries the KG via a prompt-aware interface).

3. Evaluation Engine

For each benchmark task, Benchmind will:

• Track reasoning chains or paths taken.

• Score correctness, path length, response time, and confidence.

• Log failure cases for inspection.

  Evaluation metrics will include:

• Accuracy/precision/recall.

• Average path length to solution.

• Inference steps vs. brute force.

• Coverage and robustness over time.

4. Execution Environment

• Fully integrated with MeTTa/MORK stack.

• CLI interface and config-based task execution.

• Containerized deployment for reproducibility.

Key Features and Deliverables

• MeTTa-native benchmarks: Tasks will be formulated as MeTTa expressions, runnable directly by Hyperon agents.

• MORK-optimized execution: Fast path lookup and atom matching using MORK’s zipper graph operations.

• Hybrid reasoning support: Enable evaluation of agents that combine symbolic inference with neural language models.

• Debugging & analytics: Visual or textual summaries of reasoning chains, failure types, and knowledge gaps.

• Documentation & extensibility: Clear developer documentation, plus support for custom task and agent modules.

Workflow Example

1. A developer loads a knowledge graph into MORK (e.g., biomedical or general-purpose).

2. They choose a task set, e.g., “Analogical Reasoning.”

3. They define or select a reasoning agent (e.g., a MeTTa query runner).

4. Benchmind executes all tasks, logs results, and computes evaluation metrics.

5. The developer receives a diagnostic report showing:

   • Task accuracy and failure cases.

   • Bottlenecks (e.g., missing graph links, inefficient traversals).

   • Comparisons to baseline solvers.

Use Cases and Value

• Benchmarking New Reasoners: Test new symbolic or hybrid inference engines under realistic AGI tasks.

• KG Quality Testing: Compare performance of different KGs on the same task set — useful for graph builders.

• Debugging AGI Agents: Identify why reasoning failed (missing links? ambiguous paths? bad confidence ranking?).

• Community Comparison: Create a shared standard for reasoning performance in the Hyperon/MeTTa ecosystem.

Datasets and Ground Truth

Benchmind will include:

• Curated mini-knowledge graphs with handcrafted reasoning challenges.

• Synthetic datasets for stress testing.

• Converted subsets of public graphs (e.g., ConceptNet, Wikidata) adapted to MORK/MeTTa.

All datasets will be open-source and extensible.

Integration with MORK & MeTTa

Benchmind is explicitly built for:

• MORK: Used as the execution backend for atomspace storage and traversal.

• MeTTa: Tasks encoded as MeTTa expressions. Agents must return MeTTa-compatible output or logs.

This ensures deep, native integration rather than loose interfacing.

Competitive Edge

• First standardized benchmark for reasoning in Hyperon/MeTTa.

• Covers neuro-symbolic scenarios explicitly (e.g., hybrid LLM+KG agents).

• Designed for scale: Supports small experiments and billion-atom KGs via MORK.

• Community-aligned: Easily reusable by other RFP teams and Hyperon researchers.

Open Source Licensing

Background & Experience

Chainscore Labs is a specialist Web3 R&D firm with deep expertise in blockchain infrastructure, distributed systems, and AI. Our team combines seasoned software engineers (Python, Rust, smart contracts) with AI researchers experienced in symbolic reasoning, probabilistic logic networks, and meta-programming.