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researcher

Ping Nie

43 papers hereh-index 12552 citations43 works total

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

author position
  • middle author36

Across the 36 of 43 papers where every author was matched, so the position is known.

fields
  • cs.CV15
  • cs.AI8
  • cs.CL8
  • cs.SE6
  • cs.IR3
  • cs.LG2
same name
  • Ping Nie — 9 papers, h 7
  • Ping Nie — 6 papers, h 2
  • Ping Nie — 4 papers, h 2
  • Ping Nie — 1 paper, h 1
  • Ping Nie — 1 paper
  • Ping Nie — 1 paper, h 54

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 citedBrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent

2 citations · 5 across the 37 of their papers we have counts for

collaborators
Showing 2026 · cs.CLShow all

4 papers · 2 filters

cs.CL2026

Sequential Beats Joint: On the Interplay between On-Policy Distillation and RLVR

Boyan Li, Bingsen Chen, Chenghao Yang +3

Reinforcement learning with verifiable rewards (RLVR) and on-policy distillation (OPD) have emerged as two dominant methods for post-training reasoning LLMs. Prior work uses OPD's…

cs.CL2026

FlexSQL: Flexible Exploration and Execution Make Better Text-to-SQL Agents

Quang Hieu Pham, Yang He, Ping Nie +5

Text-to-SQL over large analytical databases requires navigating complex schemas, resolving ambiguous queries, and grounding decisions in actual data. Most current systems follow a…

cs.CL2026

ClawBench: Can AI Agents Complete Everyday Online Tasks?

Yuxuan Zhang, Yubo Wang, Yipeng Zhu +25

AI agents may be able to assist with emails and documents, but can they reliably complete everyday online workflows on real websites? Everyday online tasks offer a realistic yet un…

cs.CL2026

EvolveCoder: Evolving Test Cases via Adversarial Verification for Code Reinforcement Learning

Chi Ruan, Dongfu Jiang, Huaye Zeng +2

Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving code generation in large language models, but its effectiveness is limited by weak and s…

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