7 citations · 7 across the 3 of their papers we have counts for
5 papers
ImplicitHop: Evaluating knowledge integration across context and memory
Alexander Detkov, Dashiel Karish, Matthew Oleynikov +1
The ability to integrate new information and deduce its consequences is central to learning, understanding, and reasoning in large language models (LLMs). Whether information arriv…
Do World Models Learn Global Understanding?
Alexander Detkov, Matt Thomson
AI systems often feel brittle and fragmented. A large language model (LLM) may correctly explain a concept but fail to apply it, or follow safety instructions in one context but no…
Prompt Baking
Aman Bhargava, Cameron Witkowski, Alexander Detkov +1
Two primary ways to change LLM behavior are prompting and weight updates (e.g., fine-tuning). Prompting LLMs is simple and effective, specifying the desired changes explicitly in n…
What's the Magic Word? A Control Theory of LLM Prompting
Aman Bhargava, Cameron Witkowski, Shi-Zhuo Looi +1
Prompt engineering is crucial for deploying LLMs but is poorly understood mathematically. We formalize LLM systems as a class of discrete stochastic dynamical systems to explore pr…
Generating counterfactual explanations of tumor spatial proteomes to discover effective strategies for enhancing immune infiltration
Zitong Jerry Wang, Alexander M. Xu, Aman Bhargava +1
The tumor microenvironment (TME) significantly impacts cancer prognosis due to its immune composition. While therapies for altering the immune composition, including immunotherapie…