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Jingrui He

11 papers hereh-index 9240 citations15 works total

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

author position
  • middle author5
  • last author6

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

fields
  • cs.LG5
  • cs.CL4
  • cs.IR2
same name
  • Jingrui He — 32 papers, h 24
  • Jingrui He — 27 papers, h 11
  • Jingrui He — 19 papers, h 9
  • Jingrui He — 13 papers, h 33
  • Jingrui He — 11 papers, h 4
  • Jingrui He — 7 papers, h 6

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 citedLLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation

6 citations · 13 across the 9 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Hybrid Reasoning Agents

Jiaru Zou, Yunzhe Qi, Sirui Chen +5

How can we build an agent that thinks beyond human language, yet still acts through it in the external environment? Modern LLM agents still reason and interact through text, while…

cs.CL2025

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning

Jiaru Zou, Ling Yang, Yunzhe Qi +5

Agentic reinforcement learning has advanced large language models (LLMs) to reason through long chain-of-thought trajectories while interleaving external tool use. Existing approac…

cs.CL2025★ 1 cited

ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought Reasoning in LLMs

Jiaru Zou, Ling Yang, Jingwen Gu +4

Process Reward Models (PRMs) have recently emerged as a powerful framework for supervising intermediate reasoning steps in large language models (LLMs). Previous PRMs are primarily…

cs.CL2025

Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning

Jiaru Zou, Yikun Ban, Zihao Li +4

Large language models are typically adapted to downstream tasks through supervised fine-tuning on domain-specific data. While standard fine-tuning focuses on minimizing generation…

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