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cs.AI2026
TMAS: Scaling Test-Time Compute via Multi-Agent Synergy
George Wu, Nan Jing, Qing Yi +7
Test-time scaling has become an effective paradigm for improving the reasoning ability of large language models by allocating additional computation during inference. Recent struct…
cs.AI2026
IQuest-Coder-V1 Technical Report
Jian Yang, Wei Zhang, Shawn Guo +35
In this report, we introduce the IQuest-Coder-V1 series-(7B/14B/40B/40B-Loop), a new family of code large language models (LLMs). Moving beyond static code representations, we prop…
cs.AI2025
Universal Reasoning Model
Zitian Gao, Lynx Chen, Yihao Xiao +5
Universal transformers (UTs) have been widely used for complex reasoning tasks such as ARC-AGI and Sudoku, yet the specific sources of their performance gains remain underexplored.…