Weaving the Cosmos: WASM-Powered Interchain Communication for AI Enabled Smart Contracts
- URL: http://arxiv.org/abs/2502.17604v1
- Date: Mon, 24 Feb 2025 19:44:28 GMT
- Title: Weaving the Cosmos: WASM-Powered Interchain Communication for AI Enabled Smart Contracts
- Authors: Rabimba Karanjai, Lei Xu, Weidong Shi,
- Abstract summary: This paper introduces an innovative framework that integrates blockchain technology, particularly the Cosmos SDK, to facilitate on-chain AI inferences.<n>This system, built on WebAssembly (WASM), enables interchain communication and deployment of WASM modules executing AI inferences across multiple blockchain nodes.
- Score: 4.780973517287942
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In this era, significant transformations in industries and tool utilization are driven by AI/Large Language Models (LLMs) and advancements in Machine Learning. There's a growing emphasis on Machine Learning Operations(MLOps) for managing and deploying these AI models. Concurrently, the imperative for richer smart contracts and on-chain computation is escalating. Our paper introduces an innovative framework that integrates blockchain technology, particularly the Cosmos SDK, to facilitate on-chain AI inferences. This system, built on WebAssembly (WASM), enables interchain communication and deployment of WASM modules executing AI inferences across multiple blockchain nodes. We critically assess the framework from feasibility, scalability, and model security, with a special focus on its portability and engine-model agnostic deployment. The capability to support AI on-chain may enhance and expand the scope of smart contracts, and as a result enable new use cases and applications.
Related papers
- Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach [35.05793485239977]
We propose AgentNet, a novel framework for supporting interaction, collaborative learning, and knowledge transfer among AI agents.
We consider two application scenarios, digital-twin-based industrial automation and metaverse-based infotainment system, to describe how to apply AgentNet.
arXiv Detail & Related papers (2025-03-20T00:48:44Z) - Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities [148.601430677814]
This paper presents a comprehensive overview of AI and communication for 6G networks.<n>We first review the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G.<n>The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks.
arXiv Detail & Related papers (2024-12-19T05:36:34Z) - Transforming the Hybrid Cloud for Emerging AI Workloads [81.15269563290326]
This white paper envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads.
The proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness.
This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms.
arXiv Detail & Related papers (2024-11-20T11:57:43Z) - Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents.
We propose the Internet of Agents (IoA), a novel framework that addresses these limitations.
IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
arXiv Detail & Related papers (2024-07-09T17:33:24Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large
Language Models [6.867309936992639]
Large language models (LLMs) serve people in the form of AI-generated content (AIGC)
It is difficult to guarantee the authenticity and reliability of AIGC learning data.
There are also hidden dangers of privacy disclosure in distributed AI training.
arXiv Detail & Related papers (2023-10-10T03:18:26Z) - Optimizing Large Language Models to Expedite the Development of Smart
Contracts [0.0]
We introduce MazzumaGPT, a large language model that has been optimised to generate smart contract code.
We outline the optimisation and fine-tuning parameters, evaluate the model's performance on functional correctness and address the limitations and broader impacts of our research.
arXiv Detail & Related papers (2023-10-08T14:29:33Z) - Federated Learning-Empowered AI-Generated Content in Wireless Networks [58.48381827268331]
Federated learning (FL) can be leveraged to improve learning efficiency and achieve privacy protection for AIGC.
We present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content.
arXiv Detail & Related papers (2023-07-14T04:13:11Z) - Large Language Models Empowered Autonomous Edge AI for Connected
Intelligence [51.269276328087855]
Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence.
This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements.
arXiv Detail & Related papers (2023-07-06T05:16:55Z) - Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains [31.080896878139402]
We propose the concept of AI chain and introduce the best principles and practices that have been accumulated in software engineering for decades into AI chain engineering.
We also develop a no-code integrated development environment, Prompt Sapper, which embodies these AI chain engineering principles and patterns naturally in the process of building AI chains.
arXiv Detail & Related papers (2023-06-21T05:31:00Z) - Prompt Sapper: LLM-Empowered Software Engineering Infrastructure for
AI-Native Services [37.05145017386908]
Prompt Sapper is committed to support the development of AI-native services by AI chain engineering.
It creates a large language model (LLM) empowered software engineering infrastructure for authoring AI chains through human-AI collaborative intelligence.
This article will introduce the R&D motivation behind Prompt Sapper, along with its corresponding AI chain engineering methodology and technical practices.
arXiv Detail & Related papers (2023-06-04T01:47:42Z) - The Internet of Senses: Building on Semantic Communications and Edge
Intelligence [67.75406096878321]
The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human receptors'
We elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms may satisfy the requirements of IoS use cases.
arXiv Detail & Related papers (2022-12-21T03:37:38Z) - Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering.
In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
arXiv Detail & Related papers (2022-03-06T10:12:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.