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Hang Gao

Rutgers, The State University of New Jersey

5 papers hereh-index 4712 citations12 works total

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

author position
  • first author2
  • middle author3

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.AI1
affiliations
  • Rutgers, The State University of New Jersey
HomepageORCID 0009-0003-9458-7497
same name
  • Hang Gao — 9 papers, h 12
  • Hang Gao — 6 papers
  • Hang Gao — 3 papers
  • Hang Gao — 3 papers, h 14
  • Hang Gao — 3 papers
  • Hang Gao — 2 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
20242026
collaborators

5 papers

cs.CL2026

Beyond Explicit Edges: Robust Reasoning over Noisy and Sparse Knowledge Graphs

Hang Gao, Dimitris N. Metaxas

GraphRAG is increasingly adopted for converting unstructured corpora into graph structures to enable multi-hop reasoning. However, standard graph algorithms rely heavily on static…

cs.AI2025

LiteCUA: Computer as MCP Server for Computer-Use Agent on AIOS

Kai Mei, Xi Zhu, Hang Gao +2

We present AIOS 1.0, a novel platform designed to advance computer-use agent (CUA) capabilities through environmental contextualization. While existing approaches primarily focus o…

cs.CL2025

A-MEM: Agentic Memory for LLM Agents

Wujiang Xu, Zujie Liang, Kai Mei +3

While large language model (LLM) agents can effectively use external tools for complex real-world tasks, they require memory systems to leverage historical experiences. Current mem…

cs.CL2024

Auto-Prompt Generation is Not Robust: Prompt Optimization Driven by Pseudo Gradient

Zeru Shi, Zhenting Wang, Yongye Su +5

While automatic prompt generation methods have recently received significant attention, their robustness remains poorly understood. In this paper, we introduce PertBench, a compreh…

cs.CL2024

Task-Aligned Tool Recommendation for Large Language Models

Hang Gao, Yongfeng Zhang

By augmenting Large Language Models (LLMs) with external tools, their capacity to solve complex problems has been significantly enhanced. However, despite ongoing advancements in t…

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