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cs.AI2026
Thomson: Continual Learning of Frontier Models for SovereignAI
Shengzhuang Chen, Jerrod Parker, Yejin Bang +23
The development of frontier models is commonly perceived to be the exclusive remit of a small number of heavily funded players, creating an information, economic and power asymmetr…
cs.AI2026
Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models
John Scoville, Shengzhuang Chen, Yejin Bang +2
Recent meta-reasoning frameworks improve LLM reasoning by wrapping chain-of-thought generation in an iterative control loop, allowing more effective backtracking, termination of re…
cs.AI2025
Scales++: Compute Efficient Evaluation Subset Selection with Cognitive Scales Embeddings
Andrew M. Bean, Nabeel Seedat, Shengzhuang Chen +1
The prohibitive cost of evaluating large language models (LLMs) on comprehensive benchmarks necessitates the creation of small yet representative data subsets (i.e., tiny benchmark…