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cs.CL2025
An empirical study on the limitation of Transformers in program trace generation
Simeng Sun
We study Transformers on the task \emph{program trace generation} (PTG), where models produce step-by-step execution traces for synthetic programs. Unlike existing algorithmic prob…
cs.CL2025
SWAN-GPT: An Efficient and Scalable Approach for Long-Context Language Modeling
Krishna C. Puvvada, Faisal Ladhak, Santiago Akle Serrano +8
We present a decoder-only Transformer architecture that robustly generalizes to sequence lengths substantially longer than those seen during training. Our model, SWAN-GPT, interlea…
cs.PL2025
L0-Reasoning Bench: Evaluating Procedural Correctness in Language Models via Simple Program Execution
Simeng Sun, Cheng-Ping Hsieh, Faisal Ladhak +3
Complex reasoning tasks often rely on the ability to consistently and accurately apply simple rules across incremental steps, a foundational capability which we term "level-0" reas…