PoTRE (Poly-Topological Reasoning Ensembles) framework for complex reasoning through heterogeneous multi-agent architecture inspired by cognitive heterogeneity.
Scanned 9/11/2026
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---
name: potre-cognitive-heterogeneity-reasoning
version: 1.0.0
description: PoTRE (Poly-Topological Reasoning Ensembles) framework for complex reasoning through heterogeneous multi-agent architecture inspired by cognitive heterogeneity.
author: Anmol Kankariya, Sercan Ö. Arık
license: MIT
arxiv_id: 2607.20268v1
tags:
- reasoning
- multi-agent
- heterogeneous
- LLM
- cognitive
---
# PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity
## Overview
PoTRE (Poly-Topological Reasoning Ensembles) is a heterogeneous framework that decouples inference into four specialized agents to handle complex reasoning tasks requiring long-horizon planning and iterative error correction.
## Key Components
### Four-Agent Architecture
1. **Adversarial Refinement Agent**: Critiques and refines solutions through adversarial feedback
2. **Hierarchical Strategic Planning Agent**: Breaks down complex problems into manageable sub-tasks with strategic oversight
3. **Spectrum Search Agent**: Explores solution space through diverse search strategies
4. **Direct Chain Agent**: Provides baseline reasoning through standard chain-of-thought approaches
### Task-Adaptive Aggregation Layer
- Dynamically reconciles perspectives from all four agents
- Employs one of three reconciliation strategies based on task requirements:
- Final candidate selection
- Semantic synthesis
- Neuro-symbolic verification
- Produces robust global solution leveraging architectural heterogeneity
## Implementation Guidelines
### Agent Configuration
1. Configure each agent with appropriate prompting strategies and constraints
2. Implement inter-agent communication protocols for information sharing
3. Set up aggregation layer with task-specific reconciliation logic
4. Calibrate agent weights based on problem domain characteristics
### Performance Optimization
- Achieves improved reasoning performance with similar or fewer inference tokens compared to homogeneous baselines
- Particularly effective on complex benchmarks requiring novel abstractions or rigorous domain constraints
- Can be adapted to various reasoning domains through agent specialization
## Use Cases
- Complex mathematical reasoning (ARC-AGI-2)
- Comprehensive knowledge assessment (Humanity's Last Exam)
- Financial reasoning and analysis (PRBench Finance)
- Multi-step problem solving requiring diverse reasoning strategies
## Activation Keywords
PoTRE, cognitive heterogeneity, multi-agent reasoning, poly-topological ensembles, adversarial refinement, hierarchical planning
## References
- arXiv: [2607.20268v1](https://arxiv.org/abs/2607.20268v1)
- Authors: Anmol Kankariya, Sercan Ö. Arık
- Published: July 22, 2026
- Benchmarks: ARC-AGI-2, Humanity's Last Exam (HLE), PRBench Finance
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