8 papers
UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams
Siyu Xia, Chenheng Zhang, Yanting Wu +8
Memory is essential for LLM agents to accumulate task experience and reuse task-specific execution strategies. However, real-world deployment over boundary-agnostic and evolving ta…
OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale
Dihong Jiang, Ruoqi Cao, Zhiyuan Dang +8
While traditional tree-based ensemble methods have long dominated tabular tasks, deep neural networks and emerging foundation models have challenged this primacy, yet no consensus…
Rethinking Personalization in Large Language Models at the Token Level
Chenheng Zhang, Yijun Lu, Lizhe Fang +7
With large language models (LLMs) now performing strongly across diverse tasks, there is growing demand for them to personalize outputs for individual users. Personalization is typ…
Partial Feedback Online Learning
Shihao Shao, Cong Fang, Zhouchen Lin +1
We study a new learning protocol, termed partial-feedback online learning, where each instance admits a set of acceptable labels, but the learner observes only one acceptable label…
On the Limitations and Capabilities of Position Embeddings for Length Generalization
Yang Chen, Yitao Liang, Zhouchen Lin
In Transformers, Position Embeddings (PEs) significantly influence Length Generalization (LG) performance, yet their fundamental role remains unclear. In this work, we investigate…
Low-Dimension-to-High-Dimension Generalization And Its Implications for Length Generalization
Yang Chen, Long Yang, Yitao Liang +1
Low-Dimension-to-High-Dimension (LDHD) generalization is a special case of Out-of-Distribution (OOD) generalization, where the training data are restricted to a low-dimensional sub…
Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective
Yang Chen, Cong Fang, Zhouchen Lin +1
Foundation Models (FMs) have demonstrated remarkable insights into the relational dynamics of the world, leading to the crucial question: how do these models acquire an understandi…
DIGIC: Domain Generalizable Imitation Learning by Causal Discovery
Yang Chen, Yitao Liang, Zhouchen Lin
Causality has been combined with machine learning to produce robust representations for domain generalization. Most existing methods of this type require massive data from multiple…