Ge Liu

This paper analyzes label inference attacks in Vertical Federated Learning (VFL), demonstrating that existing attacks rely on feature-label distribution alignment, and proposes a zero-overhead defense via layer adjustment.

ExpGraph is a model-agnostic framework that uses a self-evolving experience graph to enable LLM agents to reuse past successful strategies and failure lessons, significantly improving performance across diverse tasks.

ElasticMem introduces a novel framework that treats memory as an elastic latent resource, allowing LLM agents to adaptively manage and inject variable-budget memories for improved performance in long-term reasoning tasks.

ExpWeaver introduces a novel framework for LLM agents to learn from past experiences using latent retrieval-augmented generation, achieving state-of-the-art performance while significantly improving token efficiency.

Introduces AlphaOracle, a human-workflow-inspired framework for deciphering undeciphered oracle bone script characters using a large digitized corpus, reducing analysis time and agreeing with expert interpretations.