# SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks

arXiv: https://arxiv.org/abs/2605.28764 · HTML: https://mlsanity.com/paper/2605.28764/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 19 of 19)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [The Shapley Value in Machine Learning](https://mlsanity.com/paper/2202.05594/) (2022)
- [Petals: Collaborative Inference and Fine-tuning of Large Models](https://mlsanity.com/paper/2209.01188/) (2022)
- [An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.03052/) (2024)
- [Fair and efficient contribution valuation for vertical federated learning](https://mlsanity.com/paper/2201.02658/) (2022)
- [A Contract Theory based Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2108.05568/) (2021)
- [Distributed Deep Learning Using Volunteer Computing-Like Paradigm](https://mlsanity.com/paper/2103.08894/) (2021)
- [FedToken: Tokenized Incentives for Data Contribution in Federated Learning](https://mlsanity.com/paper/2209.09775/) (2022)
- [AgentMesh: A Cooperative Multi-Agent Generative AI Framework for Software Development Automation](https://mlsanity.com/paper/2507.19902/) (2025)
- [BOINC: A Platform for Volunteer Computing](https://mlsanity.com/paper/1903.01699/) (2019)
- [Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior](https://mlsanity.com/paper/2307.10492/) (2023)
- [Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning](https://mlsanity.com/paper/2405.00839/) (2024)
- [Evolving Neural Networks Reveal Emergent Collective Behavior from Minimal Agent Interactions](https://mlsanity.com/paper/2410.19718/) (2024)
- [Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learning](https://mlsanity.com/paper/2503.07869/) (2025)
- [From Pheromones to Policies: Reinforcement Learning for Engineered Biological Swarms](https://mlsanity.com/paper/2509.20095/) (2025)
- [Lattica: A Decentralized Cross-NAT Communication Framework for Scalable AI Inference and Training](https://mlsanity.com/paper/2510.00183/) (2025)
- [HarnessAPI: A Skill-First Framework for Unified Streaming APIs and MCP Tools](https://mlsanity.com/paper/2605.22733/) (2026)

## Abstract (arXiv)

Vast quantities of compute (GPU cycles on personal workstations, idle inference servers, and edge devices between jobs) go unused because no incentive-aligned protocol exists for their owners to share them safely and profitably. Existing approaches either require a trusted central coordinator (cloud marketplaces), demand heavy blockchain infrastructure (Golem, BrokerChain), or lack an incentive layer entirely (BOINC, Petals). We propose SwarmHarness, a decentralised protocol in which HarnessAPI skill nodes self-organise into a compute swarm without any central authority. SwarmHarness has three interlocking components: a SwarmRegistry built on a Distributed Hash Table (DHT) for peer discovery and capability advertisement; a SwarmRouter that dispatches tasks to nodes using a utility function over capability, load, latency, and trust; and SwarmCredit, an incentive mechanism that attributes compute-credit rewards to contributing nodes via a Shapley-value approximation. Nodes earn credits by serving tasks and spend credits to submit them; idle nodes that never contribute drain credits and lose routing priority, creating a self-regulating participation economy. As nodes specialise toward high-reward skills and routing signals act as digital pheromones, the network exhibits emergent collective intelligence analogous to biological swarms. Beyond compute sharing, SwarmHarness is a foundational primitive for autonomous distributed AI agent networks in which agents hire compute, route subtasks, and settle credits without human intermediation.