Xiang Liu

BlindMarket is a zero-trust framework that enables the verifiable, confidential, and traceable distribution of hardware IP cores between vendors and users.

The paper systematically evaluates six OpenClaw-series AI agent frameworks, demonstrating that these agentized systems possess significant security vulnerabilities that are distinct from and more severe than the underlying language models alone.

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.

ESPO is a novel reinforcement learning algorithm that detects trajectory failure in large language models and terminates rollouts early, significantly improving performance on mathematical reasoning benchmarks while reducing computational cost.

This study successfully demonstrates that federated learning can achieve prediction accuracy comparable to centralized modeling for multi-center sepsis prediction while fundamentally preserving patient data privacy.

This paper proposes a four-layer technical architecture for large model inference optimization, including Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion.

This paper proposes a two-stage algorithm, AC-IHT, for high-dimensional regression with contamination, achieving near-optimal estimation and strong oracle property.

This paper proposes Trellis, a data foundation that treats experience graphs from long-horizon agentic tasks as first-class, governed, queryable database state.

This paper proposes CRISP, a model-agnostic framework for source-only medical image segmentation under distribution shift, which uses rank stability of positive regions to derive robust spatial priors.