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cs.CL2025
Revisiting Funnel Transformers for Modern LLM Architectures with Comprehensive Ablations in Training and Inference Configurations
DongHyun Choi, Lucas Spangher, Chris Hidey +2
Transformer-based Large Language Models, which suffer from high computational costs, advance so quickly that techniques proposed to streamline earlier iterations are not guaranteed…
cs.AI2025
Factored Agents: Decoupling In-Context Learning and Memorization for Robust Tool Use
Nicholas Roth, Christopher Hidey, Lucas Spangher +6
In this paper, we propose a novel factored agent architecture designed to overcome the limitations of traditional single-agent systems in agentic AI. Our approach decomposes the ag…