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Jiaqi Lv

15 papers hereh-index 12864 citations29 works total

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

author position
  • first author3
  • middle author12

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.LG10
  • cs.CV2
  • cs.CL1
  • cs.DC1
  • cs.SE1
same name
  • Jiaqi Lv — 6 papers, h 2
  • Jiaqi Lv — 3 papers, h 1
  • Jiaqi Lv — 1 paper, h 4
  • Jiaqi Lv — 1 paper
  • Jiaqi Lv — 1 paper

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
20202026
most citedProvably Consistent Partial-Label Learning

53 citations · 113 across the 15 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CL2026

Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making

Guangfeng Cai, Kaibing Yang, Shuo He +4

Recent studies on world modeling for Large Language Model (LLM) agents typically formulate the learning objective as next-observation prediction. However, this objective ties super…

cs.LG2026

A Unified Framework for Knowledge Transfer in Bidirectional Model Scaling

Jianlu Shen, Fu Feng, Jiaze Xu +3

Transferring pre-trained knowledge from a source model to a target model of a different architectural size is a key challenge for flexible and efficient model scaling. However, cur…

cs.SE2026

iScript: A Domain-Adapted Large Language Model and Benchmark for Physical Design Tcl Script Generation

Ning Xu, Zhaoyang Zhang, Senlin Shu +10

Modern EDA flows rely heavily on Tcl scripting, yet general LLMs perform poorly in this domain due to extreme data scarcity, domain-specific semantics, and the high reliability req…

cs.LG2026

Breaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform

Jianlu Shen, Fu Feng, Yucheng Xie +2

Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coup…

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