10 papers
EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments
Deyao Zhu, Xin Zhou, Shengling Qin +44
Pretraining scaling laws reveal that model capability improves predictably with data and compute. But learning from real world environments after deployment remains far less unders…
The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms
Jinghan Zhang, Zerui Cheng, Shiqi Chen +5
Traditional evaluations measure a learning algorithm's final performance on an i.i.d. test set, reducing learning to a single aggregate score. This approach obscures a fundamental…
On the Residual Scaling of Looped Transformers: Stability and Transferability
Shaowen Wang, Bingrui Li, Ge Zhang +3
Looped (weight-tied) Transformers apply a shared residual block times (, same at each step), increasing effective depth without adding p…
TabularMath: Evaluating Computational Extrapolation in Tabular Learning via Program-Verified Synthesis
Zerui Cheng, Jiashuo Liu, Jianzhu Yao +3
Standard tabular benchmarks mainly focus on the evaluation of a model's capability to interpolate values inside a data manifold, where models good at performing local statistical s…
VeRA: Verified Reasoning Data Augmentation at Scale
Zerui Cheng, Jiashuo Liu, Chunjie Wu +4
The main issue with most evaluation schemes today is their "static" nature: the same problems are reused repeatedly, allowing for memorization, format exploitation, and eventual sa…
MME-CC: A Challenging Multi-Modal Evaluation Benchmark of Cognitive Capacity
Kaiyuan Zhang, Chenghao Yang, Zhoufutu Wen +19
As reasoning models scale rapidly, the essential role of multimodality in human cognition has come into sharp relief, driving a growing need to probe vision-centric cognitive behav…