Feng Yan

The paper introduces Neo, an agentic program analysis framework that successfully detects zero-day privilege escalation vulnerabilities in complex, polyglot microservices by combining LLMs with advanced code analysis.

The paper proposes using an auxiliary reconstruction task, specifically one that captures intra-state feature dependencies, to improve the quality of state representations learned by the encoder in neural algorithmic reasoning.

QUBRIC introduces a co-design framework that simultaneously optimizes queries and rubrics, overcoming the bottleneck of vague rubrics derived from open-ended questions, leading to significant gains in RL performance.

This paper introduces a novel full-space quantization-driven architecture (FQA) to create highly efficient and accurate hardware approximations of nonlinear activation functions using piecewise polynomial approximations (PPAs).