In recent years, the interplay between AI agents and blockchain has enabled secure and scalable collaboration among multi-agent systems, promoting unprecedented levels of autonomy and interoperability. AI agents play a vital role in facilitating complex decision making and improving operational efficiency in blockchain systems. This collaborative synergy is particularly evident in how multi-agent systems collectively tackle complex tasks to ensure seamless integration within these frameworks. While significant efforts have been made to integrate AI agents and blockchain, most studies overlook the broader potential of AI agents in addressing challenges such as interoperability, scalability, and privacy issues. In this paper, we bridge these gaps by illustrating the interplay between AI agents and blockchain. Specifically, we explore how AI agents enhance decentralized systems and examine blockchain’s role in enabling secure and scalable collaboration. Furthermore, we categorize practical applications across domains, such as Web3, decentralized finance (DeFi), asset management, and autonomous systems, providing practical insights and real-world use cases. Additionally, we identify key research challenges, including the complexities of multi-agent coordination, interoperability across diverse systems, and privacy maintenance in decentralized frameworks. Finally, we offer future directions in terms of governance, sovereignty, computation, and interpretability to promote a secure and responsible ecosystem.

Data annotation serves as a critical foundation for artificial intelligence (AI) and machine learning (ML). Recently, AI agents powered by large language models (LLMs) have emerged as effective solutions to longstanding challenges in data annotation, such as scalability, consistency, cost, and limitations in domain expertise. These agents facilitate intelligent automation and adaptive decision-making, thereby enhancing the efficiency and reliability of annotation workflows across various fields. Despite the growing interest in this area, a systematic understanding of the role and capabilities of AI agents in annotation is still underexplored. This paper seeks to fill that gap by providing a comprehensive review of how LLM-driven agents support advanced reasoning strategies, adaptive learning, and collaborative annotation efforts. We analyze agent architectures, integration patterns within workflows, and evaluation methods, along with real-world applications in sectors such as healthcare, finance, technology, and media. Furthermore, we evaluate current tools and platforms that support agent-based annotation, addressing key challenges such as quality assurance, bias mitigation, transparency, and scalability. Lastly, we outline future research directions, highlighting the importance of federated learning, cross-modal reasoning, and responsible system design to advance the development of next-generation annotation ecosystems.

This paper introduces Historical State Reconstruction, a novel framework for low-latency financial decision-making using Large Language Models. While agentic systems have demonstrated potential in synthesizing complex financial narratives, they typically rely on Retrieval-Augmented Generation or memory-based architectures. These paradigms introduce significant latency and risk look-ahead bias during real-time inference, rendering them unsuitable for high-frequency trading environments where milliseconds determine profitability. This proposed framework resolves this bottleneck by decoupling the heavy computational cost of context acquisition from the latency-sensitive critical path of decision-making. We propose a system that proactively compiles unstructured regulatory filings (10-K, 10-Q, 8-K) into a structured, bitemporal database. By pre-computing complex state facets, such as financial health ratios, governance structures, and insider trading signals offline, the system allows trading agents to “time travel” to a reconstructed state at any historical moment t with $O(1)$ snapshot retrieval plus $O(k)$ delta application complexity. We implement this approach on the top 50 companies in the S&P 500 ranked by market capitalization, processing over 12,000 filings to demonstrate a pipeline that transforms high-dimensional financial narratives into compact, prompt-ready context. Our evaluation shows that the system reduces context retrieval latency by over 97% compared to traditional baselines while achieving a 300:1 compression ratio for financial health data. Furthermore, the bitemporal architecture guarantees strict temporal integrity, eliminating the risk of data leakage in backtesting and satisfying the reproducibility requirements of regulatory frameworks like SR 11-7.