2 citations · 5 across the 34 of their papers we have counts for
49 papers
SMELT: Scaling Laws for Compute-Matched MoE Looped Transformers
Shaowen Wang, Ge Zhang, Kairong Luo +6
Looped Transformers increase effective depth by iterating a shared block of layers, but most evaluations compare at fixed model size, conflating architectural advantage with extra…
Unfolding Scientific Papers into Multi-Turn Generation Trajectories for Continued Pre-Training
Qiankai Xu, Qiguang Chen, Zixin Su +4
A recent line of synthetic-data work reconstructs the thinking behind existing text rather than rewriting the text itself, but it operates on short web passages, recovers only loca…
HarnessDev: Can LLMs Create and Evolve Their Own Agent Harness?
Yuhao Wu, Jingyuan Zhang, Jiajun Shi +16
As agents move from research prototypes to deployed tools, their capability increasingly depends on model-external execution infrastructure, commonly termed the agent harness. Chan…
Aspire: Can Models Self-Evolve from Vague Goals?
Yuhao Wu, Jingyuan Zhang, Jiajun Shi +18
Many important forms of human learning begin with a vague goal, such as "become a better physicist" or "improve at research." Learners must interpret the goal, identify capability…
S3Gym: Can LLMs Turn Self-Testing and Self-Judging into Self-Improvement?
Jiajun Shi, Siyuan Tao, Yuhao Wu +18
Large language models (LLMs) increasingly interact with external environments and accumulate substantial behavioral experience, yet existing agent benchmarks largely evaluate them…
REER-PT: Reverse-Engineered Reasoning for Perplexity-Guided Pre-training Data Augmentation
Haoran Que, Jiajun Shi, Ting Huang +7
As language-model compute continues to scale, high-quality training data is becoming an increasingly important bottleneck. Conventional next-token prediction supervises what follow…