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Jian Liang

11 papers hereh-index 210 citations12 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.CL4
  • cs.AI3
  • cs.CV1
  • cs.LG1
  • cs.SE1
  • cs.SI1
same name
  • Jian Liang — 43 papers, h 17
  • Jian Liang — 34 papers, h 25
  • Jian Liang — 22 papers, h 7
  • Jian Liang — 21 papers, h 23
  • Jian Liang — 16 papers, h 2
  • Jian Liang — 12 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

most citedReassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning

1 citations · 1 across the 10 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Facts Don't Speak Louder than Words: The Behavioral Essence of Long-CoT Distillation

Yongcan Yu, Jian Liang, Lingxiao He +5

While long chain-of-thought (CoT) distillation significantly improves the reasoning abilities of language models, its underlying mechanism remains poorly understood. Current pipeli…

cs.CL2026

DualStake: Dual-Path Confidence Calibration in Deep Research Agents

Yinuo Xu, Yuwei Liang, Jianjie Cheng +4

Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation. However, these agents suffer from severe overconfidence, makin…

cs.CL2026

How to Train Your Deep Research Agent? Prompt, Reward, and Policy Optimization in Search-R1

Yinuo Xu, Shuo Lu, Jianjie Cheng +5

Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation. While reinforcement learning (RL) has been shown to improve pe…

cs.CL2025

DeepResearch-Slice: Bridging the Retrieval-Utilization Gap via Explicit Text Slicing

Shuo Lu, Yinuo Xu, Jianjie Cheng +3

Deep Research agents predominantly optimize search policies to maximize retrieval probability. However, we identify a critical bottleneck: the retrieval-utilization gap, where mode…

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