3 papers
cs.CL2026
LoGo: Token-Level Dynamic Local-Global Attention
Yuqi Pan, Zheng Li, Bohao Tang +2
As context lengths scale, attention increasingly becomes a primary computational bottleneck in large language models. Standard Transformers remain powerful but computationally inef…
cs.AI2026
TsuGO: Probing Search Efficiency in LLM Reasoning via Go Life-and-Death Problems
Shunwen Bai, Ziping Ma, Chaoyang Zhang +4
The evaluation of LLM reasoning is moving from final-answer accuracy to process-level assessment, yet existing methods still fail to capture how models plan reasoning paths and all…
cs.LG2026
Modular TTT: Rethinking Test-Time Training as Composable Modules
Bohao Tang, Zhen Qin, Yuqi Pan +3
Test-time training (TTT) views sequence modeling as an online learning problem in which fast weights are updated by an internal learning rule. Despite the growing number of TTT var…