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Kai Xiong

Research Center for Social Computing and Information Retrieval

13 papers hereh-index 9406 citations33 works total

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

author position
  • first author3
  • middle author9
  • last author1

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.AI4
  • cs.CL4
  • cs.LG4
  • cs.SE1
affiliations
  • Research Center for Social Computing and Information Retrieval
same name
  • Kai Xiong — 9 papers, h 5
  • Kai Xiong — 7 papers, h 12
  • Kai Xiong — 2 papers, h 2
  • Kai Xiong — 1 paper, h 1
  • Kai Xiong — 1 paper, h 2
  • Kai Xiong — 1 paper, h 1

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
collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

DeepTool: Scaling Interleaved Deliberation in Tool-Integrated Reasoning via Process-Supervised Reinforcement Learning

Yang He, Xiao Ding, Bibo Cai +5

Tool-Integrated Reasoning (TIR) extends LLM capabilities by leveraging external environments. However, existing methods lack the deliberation during sequential tool invocation requ…

cs.AI2026

PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data

Kai Xiong, Yanwei Huang, Rongjunchen Zhang +3

High-quality mathematical and logical datasets with verifiable answers are essential for strengthening the reasoning capabilities of large language models (LLMs). While recent data…

cs.AI2026

GR-Ben: A General Reasoning Benchmark for Evaluating Process Reward Models

Zhouhao Sun, Xuan Zhang, Xiao Ding +10

Currently, process reward models (PRMs) have exhibited remarkable potential for test-time scaling. Since large language models (LLMs) regularly generate flawed intermediate reasoni…

cs.AI2026

Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration

Yang Zhao, Yangou Ouyang, Xiao Ding +8

While Hybrid Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become the standard paradigm for training LLM agents, effective mechanisms for data allocation…

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