Yao Zhang

This study uses a BERT-based LLM to analyze Discord sentiment and combines it with financial data to build a multi-modal model that significantly improves the prediction of Decentraland's MANA token price.

The paper introduces C-MADF, a causally constrained multi-agent framework that significantly reduces false positives in autonomous cyber defense by restricting response actions to structurally consistent transitions.

AgenticVM is a multi-agent framework that uses LLMs and specialized tools to automate and drastically reduce the volume of software vulnerabilities into actionable, prioritized queues.

The paper introduces Quantum Futures Interactive, a live, interdisciplinary demonstration platform designed to educate participants and facilitate dialogue on transitioning blockchain systems from classical to quantum-resilient security.

CachePrune introduces a privacy-aware, fine-grained KV cache sharing mechanism that allows LLM inference systems to safely reuse cache entries across users' requests, significantly improving efficiency while eliminating side-channel leakage.

SolarChain is a platform that ensures verifiable trust in decentralized solar energy markets by anchoring digital energy credits to the hard physical limits of solar yield, thereby preventing data manipulation and speculative trading.

QSignAI is an open-source platform that integrates quantum-randomness-seeded identity signatures into a conversational AI social messaging system, demonstrating a practical bidirectional link between AI and quantum science.

The paper introduces Agora, a domain-aware multi-agent framework that successfully detects deep, previously unknown logic bugs in complex consensus protocols, outperforming existing LLM-based analysis methods.

This paper introduces the concept of Budget-Aware Agents (BAGEN), showing that current LLM agents often fail to manage resources proactively, and proposes that incorporating early stop and interval estimation significantly improves efficiency.

ProactiveLLM introduces a novel framework that enables streaming LLMs to actively decide when to interact with incoming data by leveraging the model's internal states, significantly reducing latency while maintaining quality.

The paper introduces Humanoid-GPT, a large-scale generative Transformer model that achieves robust zero-shot motion tracking and control by training on a massive, unified corpus of motion data.