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Han Wang

University of North Carolina at Chapel Hill

14 papers hereh-index 115.6k citations16 works total

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

author position
  • first author6
  • middle author5

Across the 11 of 14 papers where every author was matched, so the position is known.

fields
  • cs.CL11
  • cs.LG2
  • cs.CV1
affiliations
  • University of North Carolina at Chapel Hill
Homepage
same name
  • Han Wang — 43 papers, h 32
  • Han Wang — 31 papers, h 11
  • Han Wang — 12 papers, h 5
  • Han Wang — 10 papers, h 9
  • Han Wang — 10 papers, h 29
  • Han Wang — 10 papers, h 5

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
20212026
most citedMultitask Prompted Training Enables Zero-Shot Task Generalization

563 citations · 597 across the 13 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CL2026

Who is the Agent to Blame? Localizing Faithfulness and Citation Mistakes in Agentic Deep Research

Eran Hirsch, David Wan, Han Wang +3

Deep research (DR) systems produce long-form cited reports by orchestrating multiple agents that search and synthesize information from the web. Citations are the primary mechanism…

cs.CL2026

Stabilizing Efficient Reasoning with Step-Level Advantage Selection

Han Wang, Xiaodong Yu, Jialian Wu +4

Large language models (LLMs) achieve strong reasoning performance by allocating substantial computation at inference time, often generating long and verbose reasoning traces. While…

cs.CL2026

MERRIN: A Benchmark for Multimodal Evidence Retrieval and Reasoning in Noisy Web Environments

Han Wang, David Wan, Hyunji Lee +6

Motivated by the underspecified, multi-hop nature of search queries and the multimodal, heterogeneous, and often conflicting nature of real-world web results, we introduce MERRIN (…

cs.CL2026

Multimodal Fact-Level Attribution for Verifiable Reasoning

David Wan, Han Wang, Ziyang Wang +3

Multimodal large language models (MLLMs) are increasingly used for real-world tasks involving multi-step reasoning and long-form generation, where reliability requires grounding mo…

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