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cs.CL2026
Scaling Inherently Interpretable Language Models
Guide Labs Team, Andreas Madsen, Aya Abdelsalam Ismail +7
Interpretability is often treated as a tax on capability: language models are trained as opaque systems, then explained after the fact, with methods whose reliability is difficult…
cs.CL2026
AnchorMem: Anchored Facts with Associative Contexts for Building Memory in Large Language Models
Zhanyu Shen, Sijie Cheng, Zhicheng Guo +3
While large language models have achieved remarkable performance in complex tasks, they still need a memory system to utilize historical experience in long-term interactions. Exist…