arxiv
PublishedJuly 21, 2026 at 4:00 AM
Explaining and Tuning Transformer-based LLMs in Arithmetic Tasks with Human Strategies
Publisher summary· verbatim
arXiv:2607.17166v1 Announce Type: new Abstract: Transformer-based large language models (LLMs) continue to achieve state-of-the-art performance across various natural language processing tasks. However, their subpar performance on seemingly elementary problems, such as basic arithmetic, raises conce
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivCascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades3harxivGibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards3harxivCogDrift-4D: Hierarchical Multimodal Modeling of Temporal Behavioral Decay for MCI Detection3harxivTask-Centric Multimodal Learning for Cognitive Impairment Detection3hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗