# Self-Supervised Inference of Agents in Trustless Environments

arXiv: https://arxiv.org/abs/2409.08386 · HTML: https://mlsanity.com/paper/2409.08386/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 8 of 8)

- [Scaling up Trustless DNN Inference with Zero-Knowledge Proofs](https://mlsanity.com/paper/2210.08674/) (2022)
- [HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture](https://mlsanity.com/paper/2106.00038/) (2021)
- [opML: Optimistic Machine Learning on Blockchain](https://mlsanity.com/paper/2401.17555/) (2024)
- [Proof of Quality: A Costless Paradigm for Trustless Generative AI Model Inference on Blockchains](https://mlsanity.com/paper/2405.17934/) (2024)
- [A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine](https://mlsanity.com/paper/2409.00636/) (2024)
- [Memory-Efficient Deep Learning Inference in Trusted Execution Environments](https://mlsanity.com/paper/2104.15109/) (2021)
- [Decentralized AI: Permissionless LLM Inference on POKT Network](https://mlsanity.com/paper/2405.20450/) (2024)
- [V3rified: Revelation vs Non-Revelation Mechanisms for Decentralized Verifiable Computation](https://mlsanity.com/paper/2408.07177/) (2024)

## Built on by (most cited first, 1 of 1)

- [Fortytwo: Swarm Inference with Peer-Ranked Consensus](https://mlsanity.com/paper/2510.24801/) (2025)

## Abstract (arXiv)

In this paper, we propose a novel approach where agents can form swarms to produce high-quality responses effectively. This is accomplished by utilizing agents capable of data inference and ranking, which can be effectively implemented using LLMs as response classifiers. We assess existing approaches for trustless agent inference, define our methodology, estimate practical parameters, and model various types of malicious agent attacks. Our method leverages the collective intelligence of swarms, ensuring robust and efficient decentralized AI inference with better accuracy, security, and reliability. We show that our approach is an order of magnitude faster than other trustless inference strategies reaching less than 125 ms validation latency.