# Can Artificial Intelligence solve the blockchain oracle problem? Unpacking the Challenges and Possibilities

arXiv: https://arxiv.org/abs/2507.02125 · HTML: https://mlsanity.com/paper/2507.02125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 147 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning](https://mlsanity.com/paper/2205.01992/) (2022)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Data Poisoning in Deep Learning: A Survey](https://mlsanity.com/paper/2503.22759/) (2025)
- [Anomaly Detection in Blockchain Networks: A Comprehensive Survey](https://mlsanity.com/paper/2112.06089/) (2021)
- [A Survey on Blockchain Interoperability: Past, Present, and Future Trends](https://mlsanity.com/paper/2005.14282/) (2020)
- [Consensus in the Age of Blockchains](https://mlsanity.com/paper/1711.03936/) (2017)
- [SoK: Oracles from the Ground Truth to Market Manipulation](https://mlsanity.com/paper/2106.00667/) (2021)
- [Witnet: A Decentralized Oracle Network Protocol](https://mlsanity.com/paper/1711.09756/) (2017)
- [Augur: a decentralized oracle and prediction market platform](https://mlsanity.com/paper/1501.01042/) (2015)
- [A Study of Blockchain Oracles](https://mlsanity.com/paper/2004.07140/) (2020)
- [AiRacleX: Automated Detection of Price Oracle Manipulations via LLM-Driven Knowledge Mining and Prompt Generation](https://mlsanity.com/paper/2502.06348/) (2025)
- [ACon2: Adaptive Conformal Consensus for Provable Blockchain Oracles](https://mlsanity.com/paper/2211.09330/) (2022)
- [A Trust-Aware and Cost-Optimized Blockchain Oracle Selection Model with Deep Reinforcement Learning](https://mlsanity.com/paper/2502.16133/) (2025)
- [A First Look into DeFi Oracles](https://mlsanity.com/paper/2005.04377/) (2020)
- [Wrapping trust for interoperability. A study of wrapped tokens](https://mlsanity.com/paper/2109.06847/) (2021)

## Abstract (arXiv)

The blockchain oracle problem, which refers to the challenge of injecting reliable external data into decentralized systems, remains a fundamental limitation to the development of trustless applications. While recent years have seen a proliferation of architectural, cryptographic, and economic strategies to mitigate this issue, no one has yet fully resolved the fundamental question of how a blockchain can gain knowledge about the off-chain world. In this position paper, we critically assess the role artificial intelligence (AI) can play in tackling the oracle problem. Drawing from both academic literature and practitioner implementations, we examine how AI techniques such as anomaly detection, language-based fact extraction, dynamic reputation modeling, and adversarial resistance can enhance oracle systems. We observe that while AI introduces powerful tools for improving data quality, source selection, and system resilience, it cannot eliminate the reliance on unverifiable off-chain inputs. Therefore, this study supports the idea that AI should be understood as a complementary layer of inference and filtering within a broader oracle design, not a substitute for trust assumptions.