6 papers
From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning
Lingjing Kong, Xin Liu, Guangyi Chen +9
Post-training pipelines that combine supervised fine-tuning (SFT) with reinforcement learning (RL) have emerged as the key recipe for transforming large language models (LLMs) into…
Causal Discovery in the Era of Agents
Yujia Zheng, Vishal Verma, Mantej Gill +3
Recent attempts to combine large language models (LLMs) with causal discovery ask models to infer pairwise directions, propose graph structures, or inject language-model outputs as…
MOLAR: Learning Multimodal Molecular Representations from Noisy Labels
Yingxu Wang, Kunyu Zhang, Nan Yin +2
Motivation: Noisy labels are a common challenge in molecular property prediction because molecular annotations are often obtained from assays, curated databases, or weak annotation…
World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry
Yuejiang Liu, Fan Feng, Lingjing Kong +6
General-purpose world models promise scalable policy evaluation, optimization, and planning, yet achieving the required level of robustness remains challenging. Unlike policy learn…
Learning Discrete Concepts in Latent Hierarchical Models
Lingjing Kong, Guangyi Chen, Biwei Huang +3
Learning concepts from natural high-dimensional data (e.g., images) holds potential in building human-aligned and interpretable machine learning models. Despite its encouraging pro…
Adjusting Pretrained Backbones for Performativity
Berker Demirel, Lingjing Kong, Kun Zhang +3
With the widespread deployment of deep learning models, they influence their environment in various ways. The induced distribution shifts can lead to unexpected performance degrada…