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researcher

Jingren Zhou

34 papers hereh-index 141k citations58 works total

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

author position
  • middle author6
  • last author27

Across the 33 of 34 papers where every author was matched, so the position is known.

fields
  • cs.LG11
  • cs.DB8
  • cs.AI6
  • cs.CL4
  • cs.MA3
  • cs.CR1
same name
  • Jingren Zhou — 94 papers, h 62
  • Jingren Zhou — 52 papers, h 28
  • Jingren Zhou — 50 papers, h 27
  • Jingren Zhou — 18 papers, h 12
  • Jingren Zhou — 14 papers, h 10
  • Jingren Zhou — 8 papers

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
20232026
most citedALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads (Extended)

43 citations · 96 across the 32 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Sparse but Critical: A Token-Level Analysis of Distributional Shifts in RLVR Fine-Tuning of LLMs

Haoming Meng, Kexin Huang, Shaohang Wei +6

Reinforcement learning with verifiable rewards (RLVR) has significantly improved reasoning in large language models (LLMs), yet the token-level mechanisms underlying these improvem…

cs.CL2026

Retrieval Heads are Dynamic

Yuping Lin, Zitao Li, Yue Xing +6

Recent studies have identified "retrieval heads" in Large Language Models (LLMs) responsible for extracting information from input contexts. However, prior works largely rely on st…

cs.CL2025★ 8 cited

XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL

Yifu Liu, Yin Zhu, Yingqi Gao +8

To leverage the advantages of LLM in addressing challenges in the Text-to-SQL task, we present XiYan-SQL, an innovative framework effectively generating and utilizing multiple SQL…

cs.CL2024★ 1 cited

Provable Scaling Laws for the Test-Time Compute of Large Language Models

Yanxi Chen, Xuchen Pan, Yaliang Li +2

We propose two simple, principled and practical algorithms that enjoy provable scaling laws for the test-time compute of large language models (LLMs). The first one is a two-stage…

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