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Showing papers from LG AI Research Show all papers

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NeurIPS 2026Chung-angLG AI ResearchKoreaTime series forecasting

Safe Active Learning with Future Viability Guarantees in Time-Series Models

Hyeonjun Park, Whiyoung Jung, Deunsol Yoon, Sunghoon Hong and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
67%Highly rated
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NeurIPS 2026LG CorporationLG AI ResearchAgent benchmarks & environments

From Benchmark to Adoption: Coding Agent Evaluation Needs User-Level Harness

Dasol Hong, Ji Yong Cho, Bumsoo Kang, Moontae Lee

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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2/20 AI panelreviewers recommend it

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AI panel: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
strict 0/5
57%Worth a look
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NeurIPS 2026LG AI ResearchHankuk University of Foreign StuChung-AngQuantization

ASQ: Agent-guided Semantic-aware Quantization for Large Language Models

Seungdong Yoa, Ye Seul Sim, Suhee Yoon, Sanghyu Yoon and 4 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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1/20 AI panelreviewers recommend it

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
88%Must read
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NeurIPS 2026U North Carolina at Chapel HillDepartment of Computer Science, LG AI ResearchFieldAINTU SingaporeVideo generation

PhyMotion: Structured 3D Motion Reward for Physics-Grounded Human Video Generation

PhyMotion evaluates human video motion via physics-simulated 3D trajectory rewards across kinematics, contact, and dynamics, improving RL post-training realism by +68 Elo.

Yidong Huang, Zun Wang, Han Lin, Dong-Ki Kim and 5 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 6 on Hugging Face · Code ★ 49

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 3/5
80%Must read
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NeurIPS 2026KAISTLG AI ResearchKAIST AISeoul NationalGraph generation

MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models

MolHIT uses hierarchical discrete diffusion and decoupled atom encoding to generate molecular graphs with near-perfect validity, surpassing 1D baselines on MOSES.

Hojung Jung, Rodrigo Hormazabal, Jaehyeong Jo, Youngrok Park and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5