Tootfinder

The Alignment Bottleneck
Wenjun Cao
https://arxiv.org/abs/2509.15932 https://arxiv.org/pdf/2509.15932

The Alignment Bottleneck
Large language models improve with scale, yet feedback-based alignment still exhibits systematic deviations from intended behavior. Motivated by bounded rationality in economics and cognitive science, we view judgment as resource-limited and feedback as a constrained channel. On this basis, we model the loop as a two-stage cascade $U \to H \to Y$ given $S$, with cognitive capacity $C_{\text{cog}|S}$ and average total capacity $\bar{C}_{\text{tot}|S}$. Our main result is a capacity-cou…

Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
Yuanchen Wu, Yubai Yuan
https://arxiv.org/abs/2509.18484 https://arxiv.org/pdf/2…

Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
Estimating causal effects on networks is important for both scientific research and practical applications. Unlike traditional settings that assume the Stable Unit Treatment Value Assumption (SUTVA), interference allows an intervention/treatment on one unit to affect the outcomes of others. Understanding both direct and spillover effects is critical in fields such as epidemiology, political science, and economics. Causal inference on networks faces two main challenges. First, causal effects are…

Deep learning based doubly robust test for Granger causality
Yongchang Hui, Chijin Liu, Xiaojun Song
https://arxiv.org/abs/2509.15798 https://arxiv.org/pdf…

Deep learning based doubly robust test for Granger causality
Granger causality is popular for analyzing time series data in many applications from natural science to social science including genomics, neuroscience, economics, and finance. Consequently, the Granger causality test has become one of the main concerns of the econometrician for decades. Taking advantage of the theoretical breakthroughs in deep learning in recent years, we propose a doubly robust Granger causality test (DRGCT). Our method offers several key advantages. The first and most direc…