01 / Understand
Mechanistic Interpretability
Understanding how internal representations and computations shape model behavior, capabilities, and limitations.
I’m interested in how large language models work, why they behave the way they do, and how this understanding can help us build safe and reliable AI systems. My research connects mechanistic interpretability with AI safety and the evaluation and design of AI systems. I study how internal representations and computations shape model behavior, capabilities, and limitations.
I explore how mechanistic insights and behavioral evidence can support reliable evaluation, risk detection, and intervention, and how system design can make AI behavior more dependable. I also examine what our explanations and evaluations can reliably establish, using evidence from system behavior to test and refine our understanding of models.
I started this blog to think out loud while working as a research engineer at Alibaba Cloud Computing — writing helps me understand things better. If it helps you too, even better.
01 / Research
Three connected perspectives.
01 / Understand
Understanding how internal representations and computations shape model behavior, capabilities, and limitations.
02 / Evaluate
Evaluating and monitoring AI behavior, and testing what the evidence establishes about capability, reliability, and risk.
03 / Design
Developing and testing model interventions and system mechanisms to improve AI safety and reliability.
Explore the papers, projects, and accompanying writing:
02 / Writing
Agent reliability / Aug 2026
Why finding a correct patch does not guarantee delivering one, and what reliable agent repair requires.
Read articleSafety & evaluation / Oct 2026
When activation-monitor scores support real decisions, and where evaluation falls short.
Systems & observability / Aug 2026
Reconstructing skill execution while keeping observations, diagnoses, and verified outcomes distinct.
Mechanistic interpretability / Jun 2026
What concept alignment reveals about representation similarity and transfer across model architectures.
AI & human thinking / Jun 2026
Specification, context, and judgment when AI handles execution.
I deeply admire explorers such as Jacques Piccard and Jill Heinerth—their curiosity, courage, and willingness to venture into the unknown. That spirit resonates with how I approach language models. For me, diving into the dark means exploring their inner workings: looking beneath their outputs, questioning familiar explanations, and seeking understanding grounded in evidence, while remaining open to what we have yet to discover.
For me, exploration also carries responsibility. I want that understanding to become knowledge others can build on, and to help make AI systems safer and more reliable. I care about how these systems affect people, whose needs they serve, and who bears their risks. My hope is to contribute to AI whose benefits are shared more widely and fairly.