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researcher

Song-Chun Zhu

9 papers hereh-index 684 citations17 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author8
  • last author1

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.LG4
  • cs.AI1
same name
  • Song-Chun Zhu — 167 papers, h 92
  • Song-Chun Zhu — 15 papers, h 15
  • Song-Chun Zhu — 14 papers, h 7
  • Song-Chun Zhu — 10 papers, h 6
  • Song-Chun Zhu — 7 papers, h 5
  • Song-Chun Zhu — 6 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedMindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for Situated Neural Dialogue Generation

1 citations · 1 across the 9 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

GradCuit: Credit-Assigned Gradient Flow Enables Robust and Interpretable Test-Time Latent Reasoning

Zhaoxin Yu, Qi Shen, Hengli Li +4

Optimization-based latent reasoning improves large language model outputs by optimizing instance-specific continuous states at test time while keeping model parameters frozen. Exis…

cs.LG2025

Discrete Markov Bridge

Hengli Li, Yuxuan Wang, Song-Chun Zhu +2

Discrete diffusion has recently emerged as a promising paradigm in discrete data modeling. However, existing methods typically rely on a fixed rate transition matrix during trainin…

cs.LG2025

Seek in the Dark: Reasoning via Test-Time Instance-Level Policy Gradient in Latent Space

Hengli Li, Chenxi Li, Tong Wu +8

Reasoning ability, a core component of human intelligence, continues to pose a significant challenge for Large Language Models (LLMs) in the pursuit of AGI. Although model performa…

cs.LG2024

Mars: Situated Inductive Reasoning in an Open-World Environment

Xiaojuan Tang, Jiaqi Li, Yitao Liang +3

Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Yet, most of them rely on pre-stored knowledge. Inducing new gen…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.