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Showing papers from Telecom Paris, IP Paris Show all papers

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NeurIPS 2026KoreaTelecom Paris, IP ParisKorea Institute of Science and TRepresentation & contrastive learning

Rethinking Attention in Depth for Operator Learning

Jaehyeon Lee, Kiwon Um, JungHyun Han, Min-Koo Kang

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026UT AustinEPFLEcole Normale Supérieure P-SSchool of Computer and CommunicaTelecom Paris, IP ParisEfficient inference & serving

TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning

Terminator learns optimal early-exit points for chain-of-thought reasoning to cut token lengths by 14%-55% and boost inference speed over 2x with minimal accuracy loss.

Alliot Nagle, Jakhongir Saydaliev, Dhia Garbaya, Michael Gastpar and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 19 on Hugging Face

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AI panel: 14 of 20 reviewers recommend it
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strict 1/5