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publications
A Survey of Interpretable and Biologically Plausible LLMs
A comprehensive, interdisplinary survey of interpretable and biologically plausible LLMs based on my tutorial delivered at CS 591 BAI seminar. See Talks for more information.
IIT-Based Event-Driven Memory for Self-Evolving Large Language Model Agents
Memory is central to autonomous, self-evolving agents. Leveraging Integrated Information Theory (IIT), we design time-variant memory that integrates past experience via event-driven updates gated by reward, effectively treating consolidation as learning in a standard RL framework.
Modular Network Training via Adjoint Structure Predictor
Learn the LLM’s computational topology instead of hand-engineering it: We propose a structure predictor adjoint to a LLM, which generates a context-dependent computational graph applied to the LLM. This allows the modular structure to emerge with minimal contraints during training.
talks
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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