From the 1 of 11 linked papers with an AI index.
11 papers
From Reasoning Depth to Reasoning Breadth: Evaluating Multi-Point Associative Reasoning in Large Language Models
Si'an Xie, Jiaxun Liu, Biao Yang +4
Large language models (LLMs) have made substantial progress on reasoning tasks that require increasingly long and complex inferential chains. This progress primarily reflects reaso…
LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck
Peixi Wu, Biao Yang, Feipeng Ma +7
The paper introduces LaME, a multimodal embedding model that performs reasoning in a compact latent space using learnable tokens and an information‑bottleneck objective, eliminatin…
How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
Julia Kostin, Kasra Jalaldoust, Elias Bareinboim +2
Machine learning models often degrade when they are deployed on a target distribution that differs from the source distributions they were trained on. Recent work in causality-base…
Rep2Text: Decoding Full Text from a Single LLM Token Representation
Haiyan Zhao, Zirui He, Yiming Tang +4
Large language models (LLMs) have achieved remarkable progress across diverse tasks, yet their internal mechanisms remain largely opaque. In this work, we investigate a fundamental…
KnowThyself: An Agentic Assistant for LLM Interpretability
Suraj Prasai, Mengnan Du, Ying Zhang +1
We develop KnowThyself, an agentic assistant that advances large language model (LLM) interpretability. Existing tools provide useful insights but remain fragmented and code-intens…
FaithLM: Towards Faithful Explanations for Large Language Models
Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang +7
Large language models (LLMs) increasingly produce natural language explanations, yet these explanations often lack faithfulness, and they do not reliably reflect the evidence the m…