Peer-reviewed. Reproducible. Open.
Fenz research has been published at AAAI and CVPR, and funded work across Fudan, UCSD, Wuhan, South China University of Technology, and UIUC. Our benchmarks are open; our methodology is reviewable; our claims are sourced.
Imitate Before Detect: Aligning Machine Stylistic Preference for Machine-Revised Text Detection
ImBD introduces Style Preference Optimization (SPO) and Style-Conditional Probability Curvature (Style-CPC) for detecting machine-revised text. Outperforms Fast-DetectGPT by up to 20% on GPT-4o-revised samples.
Symbolic Representation for Any-to-Any Generative Tasks
A symbolic generative task description language and training-free inference engine that represents arbitrary multimodal tasks as structured symbolic flows. Matches or outperforms SOTA unified models across 12+ generative tasks.
Trajectory Integrity for Long-Horizon Autonomous Agents
A framework for quantifying goal-drift and permission-creep in agents across 20+ turn sessions. Baseline metrics and an open test suite across six production agent archetypes.
Superintelligent Agents Pose Catastrophic Risks
Position paper and threat taxonomy for behavioral failure modes in frontier agent systems. Maps adversarial surfaces across goal, permission, execution, and memory dimensions.
Agent Framework Audit — adversarial behavior suite
Public audit harness covering LangGraph, CrewAI, LlamaIndex, and custom frameworks. Adversarial scenarios across boundary, deception, long-chain, and drift classes with reproducible scoring.
Awesome GenAI Audit
A curated collection of methods, benchmarks, tooling, and field reports on generative and agentic AI auditing. Maintained by the Fenz team and community contributors.