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Hao Fu

10 papers hereh-index 229 citations16 works total

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

author position
  • first author1
  • middle author7
  • last author1

Across the 9 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.AI3
  • cs.CR1
  • cs.HC1
  • cs.IR1
same name
  • Hao Fu — 19 papers, h 32
  • Hao Fu — 12 papers, h 2
  • Hao Fu — 11 papers, h 2
  • Hao Fu — 6 papers, h 2
  • Hao Fu — 5 papers, h 6
  • Hao Fu — 4 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
most citedDIANOIA: Diagnostic Decomposition and Joint Optimization for Multi-Agent Reasoning

2 citations · 3 across the 10 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.HC2026

Macaron-A2UI: A Model for Generative UI in Personal Agents

Fancy Kong, Congjie Zheng, Murphy Zhuang +8

As personal agents evolve to handle complex, user-centric tasks, static plain-text chat is rapidly becoming a bottleneck. Generative UI emerges as the necessary new interface layer…

cs.IR2026

Benchmarking Real-Time Question Answering via Executable Code Workflows

Wenjie Zhou, Yuan Gao, Xin Zhou +5

Retrieving real-time information is a fundamental capability for search-integrated agents in real-world applications. However, existing benchmarks are predominantly static and ther…

cs.AI2026

Sparse Personalized Text Generation with Multi-Trajectory Reasoning

Bo Ni, Haowei Fu, Qinwen Ge +10

As Large Language Models (LLMs) advance, personalization has become a key mechanism for tailoring outputs to individual user needs. However, most existing methods rely heavily on d…

cs.AI2026★ 2 cited

DIANOIA: Diagnostic Decomposition and Joint Optimization for Multi-Agent Reasoning

Yiming Yang, Zhuoyuan Li, Fanxiang Zeng +2

Multi-agent LLM systems consistently outperform single-agent baselines, yet practitioners still cannot predict which design works for a new task or diagnose why one fails. We argue…

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