most citedAn Empirical Study to Understand How Students Use ChatGPT for Writing Essays

1 citations

5 papers

cs.IR2026

Progressive Alignment of Recommender Foundation Model through Multi-Phase Post-Training

Oseong Choi, Hoeinn Kim, Jihoon Lee +2

Foundation model(FM) for recommendation has shown strong ability to model long-horizon sequential user behavior. In practice, a single pretrained foundation model is often adapted…

cs.HC20261 cited

An Empirical Study to Understand How Students Use ChatGPT for Writing Essays

Andrew Jelson, Daniel Manesh, Alice Jang +3

As large language models (LLMs) advance and become widespread, students increasingly turn to systems like ChatGPT for assistance with writing tasks. Educators are concerned with st…

cs.CR2026

HE-DAP: Homomorphic Encryption-based Dynamic Adaptive Parameter Optimization for Statistical Computation

Yun-Soo Park, Hyunmin Choi, Hyoungshick Kim +1

Homomorphic encryption (HE) enables privacy-preserving analytics but remains hindered by high computational overhead. We find that the inverse square root-a key primitive in many s…

cs.IR2026

Inference-Free Multimodal Learned Sparse Retrieval for Production-Scale Visual Document Search

Gyu-Hwung Cho, Youngjune Lee, Kiyoon Jeong +5

As large-scale visual-document corpora such as arXiv papers and enterprise PDFs continue to grow, visual-document retrieval has gained increasing attention; yet it still lacks a de…

cs.AI2026

Constrained Auto-Bidding via Generative Response Modeling

Eunseok Yang, Xingdong Zuo, Kyung-Min Kim

Auto-bidding systems aim to maximize advertiser value over long horizons under budget constraints and ratio targets such as cost-per-acquisition, yet future traffic and auction dyn…