Good Papers

Showing papers from Department of Computer Science, ETHZ - ETH Zurich Show all papers

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NeurIPS 2026Department of Computer Science, Department of Computer Science, Time series foundation models

Temporal-Scale Sensitivity in Time-Series Tokenization and Scale-Robust Token Estimation by Gated Sum

Berken Utku Demirel, Christian Holz

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · 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 2026ETHZ - ETH ZurichETH Agentic Systems Lab & COLudwig-Maximilians-Universität MHarvardDepartment of Computer Science, AI oversight & deception

Claude Coke: Prevent Automated Crime by Agents

Gabor Hollbeck, Baran Peters, Alexander von Recum, Jan Granacher and 3 more

Sydney Poster Session 3, Wed, Dec 9, 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 1/5
medium 1/10
strict 0/5
57%Worth a look
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NeurIPS 2026ETHZ - ETH ZurichDepartment of Computer Science, ETH ZurichMultimodal reasoning

GeoDial: A Multimodal Dialog Tutoring Dataset for Geometry Problem-Solving with Visual Tutor Turns

Sankalan Pal Chowdhury, Junling Wang, Donya Rooein, April Wang and 1 more

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

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
91%Must read
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NeurIPS 2026Department of Computer Science, PrincetonCarnegie MellonU TorontoMetaCode generation

P$^{3}$: Joint Program-and-Proof Planning\\ for Verified Code Generation

P³ plans programs and proofs jointly from specifications before elaboration, outperforming sequential baselines by up to 11.2 points on verified generation benchmarks while reducing cost and time.

Zenan Li, Ziran Yang, Peiyang Song, Zhaoyu Li 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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18/20 AI panelreviewers recommend it

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AI panel: 18 of 20 reviewers recommend it
lenient 5/5
medium 10/10
strict 3/5
83%Must read
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NeurIPS 2026Department of Computer Science, KAISTGoogle, TUMETH ZurichETHZ - ETH ZurichFlow matching

GenRec: Knowing Where to Reconstruct and Where to Generate

GenRec separates reconstruction and generation via observation masks to preserve fidelity in visible regions while synthesizing plausible unobserved content.

Ata Çelen, Jaewoo Jung, Federico Tombari, Marc Pollefeys and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 1/5