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cs.LG2026
Learning to Compute Is Not Learning to Retain or Reuse in Looped Transformers
Peilong Zhou, Hongtao Zhang, Yulong Huang +3
Looped transformers compute by applying one block repeatedly, so they can run for more loops than they were trained with. Recurrent reasoners are known to overthink: iterating past…
cs.LG2026
Alpha-RTL: Test-Time Training for RTL Hardware Optimization
Peilong Zhou, Zhirong Chen, Cangyuan Li +4
Large language models (LLMs) have shown increasing promise in generating functionally correct register-transfer-level (RTL) hardware designs. Recent systems improve further through…
cs.LG2026
Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL
Siyang Cai, Cangyuan Li, Haoyu Gao +3
Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual Property (IP) and costly…