Good Papers

Showing papers from University of Illinois, Urbana-Champaign Show all papers

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NeurIPS 2026U Illinois Urbana-ChampaignU Illinois, Urbana-ChampaignUIUCFinance & economics

The Stability of Data Exchange in Competitive Markets

Yuanchen Brian Tang, Jiaxin Song, Bhaskar Ray Chaudhury, Ruta Mehta

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026U Illinois at Urbana-ChampaignIndependentU Illinois, Urbana-ChampaignNorthwesternColumbiaSparse autoencoders

Crosscoding Through Time: Sparse Feature Discovery Across Sequence Positions

Dmitry Manning-Coe, Han Xuanyuan, Aniket Deshpande, Andrii Shportko and 1 more

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

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57%Worth a look
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NeurIPS 2026U Illinois, Urbana-ChampaignU Illinois, Urbana-champaignUIUCU Illinois Urbana-ChampaignVideo understanding

PIVOT: A Unified Agentic Framework for Streaming Long-Video Understanding

Qiushi Lyu, Qianlan Yang, Ziqi Pang, Yu-Xiong Wang and 1 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Illinois, Urbana-ChampaignBetterdataSingapore University of TechnoloNational University of SingaporeTime series foundation models

FracTS: Hierarchical and Autoregressive Time Series Generation

Jiayu Li, Umair Afzal, Zilong Zhao, Milad Abdollahzadeh and 2 more

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

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78%Highly rated
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NeurIPS 2026U OxfordGoogleStanfordU Illinois, Urbana-ChampaignMetaLLM pretraining & scaling laws

An Empirical Study on Noisy Data and LLM Pretraining Loss Divergence

Synthetic noise in pretraining data causes LLM loss divergence with probability scaling by noise type, amount, and model size, exhibiting activation patterns distinct from high-learning-rate failures.

Qizhen (Irene) Zhang, Ankush Garg, Jakob Foerster, Niladri S. Chatterji and 2 more

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

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 2/5