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Tianqi Zhang

5 accepted papers

2025

Fine-Grained Graph Representation Learning for Heterogeneous Mobile Networks with Attentive Fusion and Contrastive Learning

AAAI 2025technical

AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent network self-driving holds the key, it heavily relies on expert experience and…

2025

Human Simulacra: Benchmarking the Personification of Large Language Models

ICLR 2025poster

Large Language Models (LLMs) are recognized as systems that closely mimic aspects of human intelligence. This capability has attracted the attention of the social science community, who see the potential in leveraging LLMs to replace human participants in experiments, thereby reducing research costs…

2025

Physics-Aware Robotic Palletization With Online Masking Inference

ICRA 2025

The efficient planning of stacking boxes, especially in the online setting where the sequence of item arrivals is unpredictable, remains a critical challenge in modern warehouse and logistics management. Existing solutions often address box size variations, but overlook their intrinsic and physical

Cited by 5SourcecodeScholar
2024

The Earth is Flat because...: Investigating LLMs’ Belief towards Misinformation via Persuasive Conversation

ACL 2024long

Large language models (LLMs) encapsulate vast amounts of knowledge but still remain vulnerable to external misinformation. Existing research mainly studied this susceptibility behavior in a single-turn setting. However, belief can change during a multi-turn conversation, especially a persuasive one.…

Cited by 61SourcePDFScholar
2021

Combinatorial Learning of Graph Edit Distance via Dynamic Embedding

CVPR 2021poster

Graph Edit Distance (GED) is a popular similarity measurement for pairwise graphs and it also refers to the recovery of the edit path from the source graph to the target graph. Traditional A* algorithm suffers scalability issues due to its exhaustive nature, whose search heuristics heavily rely on h…

Cited by 69PDFcodeScholar