Khai Pham

Khai Pham

Biography

Khai Pham is a rising sophomore at Rice University’s George R. Brown School of
Engineering and Computing, where he is pursuing a Bachelor of Science in Computer
Science. Originally from Sugar Land, Texas, Khai developed an early interest in mathematics
and technology, which led him to pursue research in machine learning. This summer, Khai
is conducting research at MIT’s Computer Science and Artificial Intelligence Laboratory
(CSAIL) through MIT’s Summer Research Program. Under the mentorship of Professor Jacob
Andreas in the Language and Intelligence Group, Khai’s work investigates out-of-context
reasoning in large language models, specifically whether LLMs can infer and apply latent
structure such as hidden relationships encoded in graph-based data. This line of research
carries significant implications for AI safety and our understanding of what language models
silently learn.

Can LLMs Read Between the Lines?
Khai Pham1, Joshua West2, Laura Ruis3, Jacob Andreas3
1Department of Computer Science, Rice University
2Department of Electrical and Computer Engineering, North Carolina Agricultural and Technical
State University
3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
Trained on internet data, text-based AI models can generate coherent responses. These AI models,
called Large Language Models (LLMs), now drive innovation across nearly every industry. However,
that training data may contain traces of concealed information that the model may reconstruct on its
own. Prior research has tested whether models can recover hidden information from simple clues.
However, real-world knowledge often involves complex webs of relationships rather than isolated
facts. This research investigated whether an LLM can infer a network of connections (a graph) that
was never explicitly described in training. We fine-tuned Qwen3-4B on synthetic directed acyclic
graph (DAG) datasets before asking it relational questions about pairs of nodes not explicitly
described in training. Even when trained on sparse graph data, an LLM can often identify whether
two nodes are unrelated or share an ancestral relationship in DAGs. However, the model’s reliability
varies with the complexity of the graph structure it was trained on. These findings suggest that LLMs
may be capable of piecing together complex relational knowledge that was never explicitly present in
their training data. This raises concerns for companies, policymakers, and AI researchers working to
understand what a model may silently learn.