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cs.AI2026
Prototype Transformer: Towards Language Model Architectures Interpretable by Design
Yordan Yordanov, Matteo Forasassi, Bayar Menzat +6
While state-of-the-art language models (LMs) surpass most humans in certain domains, their reasoning remains largely opaque, reducing trust and increasing the risk of deception and…
cs.CL2026
Logic-Oriented Retriever Enhancement via Contrastive Learning
Wenxuan Zhang, Yuan-Hao Jiang, Changyong Qi +2
Large language models (LLMs) struggle in knowledge-intensive tasks, as retrievers often overfit to surface similarity and fail on queries involving complex logical relations. The c…