15 papers
Beyond Perplexity: A Behavioral Evaluation Framework for Deployment-Memory Claims in LLM Test-Time Training
Xiangchen Song, Zhenhao Chen, Lingjing Kong +4
Large language model test-time training (TTT) is often evaluated through local proxy metrics: models are updated on recent tokens, retrieved context, target-domain data, or verifia…
Score-Based Causal Discovery of Latent Variable Causal Models
Ignavier Ng, Xinshuai Dong, Haoyue Dai +3
Identifying latent variables and the causal structure involving them is essential across various scientific fields. While many existing works fall under the category of constraint-…
The Power of Order: Fooling LLMs with Adversarial Table Permutations
Xinshuai Dong, Haifeng Chen, Xuyuan Liu +5
Large Language Models have achieved remarkable success and are increasingly deployed in critical applications involving tabular data, such as Table Question Answering. However, the…
Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
Xinshuai Dong, Ignavier Ng, Haoyue Dai +4
Identifying the structure of a partially observed causal system is essential to various scientific fields. Recent advances have focused on constraint-based causal discovery to solv…
Causal Representation Learning from General Environments under Nonparametric Mixing
Ignavier Ng, Shaoan Xie, Xinshuai Dong +2
Causal representation learning aims to recover the latent causal variables and their causal relations, typically represented by directed acyclic graphs (DAGs), from low-level obser…
Advancing Reasoning in Diffusion Language Models with Denoising Process Rewards
Shaoan Xie, Lingjing Kong, Xiangchen Song +4
Diffusion-based large language models offer a non-autoregressive alternative for text generation, but enabling them to perform complex reasoning remains challenging. Reinforcement…