Meng Han

Yuanjie Liu, Wenpeng Xing, Ye Zhou et al.

The absence of a fully decentralized, verifiable, and privacy-preserving communication protocol for autonomous agents remains a core challenge in decentralized computing. Existing systems often rely on centralized intermediaries, which reintroduce trust bottlenecks, or lack decentralized identity-resolution mechanisms, limiting persistence and cross-network interoperability. We propose the Decentralized Interstellar Agent Protocol (DIAP), a novel framework for agent identity and communication that enables persistent, verifiable, and trustless interoperability in fully decentralized environments. DIAP binds an agent's identity to an immutable IPFS or IPNS content identifier and uses zero-knowledge proofs (ZKP) to dynamically and statelessly prove ownership, removing the need for record updates. We present a Rust SDK that integrates Noir (for zero-knowledge proofs), DID-Key, IPFS, and a hybrid peer-to-peer stack combining Libp2p GossipSub for discovery and Iroh for high-performance, QUIC based data exchange. DIAP introduces a zero-dependency ZKP deployment model through a universal proof manager and compile-time build script that embeds a precompiled Noir circuit, eliminating the need for external ZKP toolchains. This enables instant, verifiable, and privacy-preserving identity proofs. This work establishes a practical, high-performance foundation for next-generation autonomous agent ecosystems and agent-to-agent (A to A) economies.

Wenpeng Xing, Zhipeng Chen, Changting Lin et al.

Invoking external tools enables Large Language Models (LLMs) to perform complex, real-world tasks, yet selecting the correct tool from large, hierarchically-structured libraries remains a significant challenge. The limited context windows of LLMs and noise from irrelevant options often lead to low selection accuracy and high computational costs. To address this, we propose the Hierarchical Gaussian Mixture Framework (HGMF), a probabilistic pruning method for scalable tool invocation. HGMF first maps the user query and all tool descriptions into a unified semantic space. The framework then operates in two stages: it clusters servers using a Gaussian Mixture Model (GMM) and filters them based on the query's likelihood. Subsequently, it applies the same GMM-based clustering and filtering to the tools associated with the selected servers. This hierarchical process produces a compact, high-relevance candidate set, simplifying the final selection task for the LLM. Experiments on a public dataset show that HGMF significantly improves tool selection accuracy while reducing inference latency, confirming the framework's scalability and effectiveness for large-scale tool libraries.

I. M. Son, S. I. Kwak, U. J. Han et al.

This paper presents an original method of fuzzy approximate reasoning that can open a new direction of research in the uncertainty inference of Artificial Intelligence(AI) and Computational Intelligence(CI). Fuzzy modus ponens (FMP) and fuzzy modus tollens(FMT) are two fundamental and basic models of general fuzzy approximate reasoning in various fuzzy systems. And the reductive property is one of the essential and important properties in the approximate reasoning theory and it is a lot of applications. This paper suggests a kind of extended distance measure (EDM) based approximate reasoning method in the single input single output(SISO) fuzzy system with discrete fuzzy set vectors of different dimensions. The EDM based fuzzy approximate reasoning method is consists of two part, i.e., FMP-EDM and FMT-EDM. The distance measure based fuzzy reasoning method that the dimension of the antecedent discrete fuzzy set is equal to one of the consequent discrete fuzzy set has already solved in other paper. In this paper discrete fuzzy set vectors of different dimensions mean that the dimension of the antecedent discrete fuzzy set differs from one of the consequent discrete fuzzy set in the SISO fuzzy system. That is, this paper is based on EDM. The experimental results highlight that the proposed approximate reasoning method is comparatively clear and effective with respect to the reductive property, and in accordance with human thinking than existing fuzzy reasoning methods.