activity
20242026
most citedLook Within, Why LLMs Hallucinate: A Causal Perspective

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2026

SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis

Songhan Wang, Haoang Chi, He Li +6

Spatial and Single-cell transcriptomics are transformative in deciphering cellular dynamics. As the fundamental paradigm for reconstructing cell developmental paths, trajectory inf…

cs.AI2026

MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs

He Li, Haoang Chi, Qizhou Wang +6

Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific co…

cs.CV2026

LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck

Peixi Wu, Biao Yang, Feipeng Ma +7

Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. Despite the strong performance acr…

cs.AI2025★ 1 cited

Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?

Haoang Chi, He Li, Wenjing Yang +5

Causal reasoning capability is critical in advancing large language models (LLMs) toward strong artificial intelligence. While versatile LLMs appear to have demonstrated capabiliti…

stat.ME2025

Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation

He Li, Haoang Chi, Mingyu Liu +3

The real world naturally has dimensions of time and space. Therefore, estimating the counterfactual outcomes with spatial-temporal attributes is a crucial problem. However, previou…

cs.CL2024★ 3 cited

Look Within, Why LLMs Hallucinate: A Causal Perspective

He Li, Haoang Chi, Mingyu Liu +1

The emergence of large language models (LLMs) is a milestone in generative artificial intelligence, achieving significant success in text comprehension and generation tasks. Despit…