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NeurIPS 2026YaleOpenAIGoogleGoogle, TUM3D vision & reconstruction

Extending 3D Reconstruction Models to Any Camera

Ruxiao Duan, Yunwen (Verse) Zhou, Erin Hong, Dongxu Zhao and 3 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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NeurIPS 2026ETH ZurichGoogle DeepMindAnthropicOpenAIU HaifaLLM evaluation & benchmarks

CryptanalysisBench: Can LLMs do cryptanalysis?

Lukas Fluri, Avital Shafran, Nicholas Carlini, Matthew Jagielski and 4 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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57%Worth a look
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NeurIPS 2026UC BerkeleyMicrosoft ResearchU California, BerkeleyOpenAIEECS, UC BerkeleyEfficient inference & serving

SPECS: Faster Test-Time Scaling through Speculative Drafts and Dynamic Switching

Mert Cemri, Nived Rajaraman, Rishabh Tiwari, Xiaoxuan Liu and 5 more

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

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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NeurIPS 2026MITCarnegie MellonNew YorkOpenAIMassachusetts Institute of TechnGame playing

A Regularization-Based Approach to Public Belief State Search for Adversarial Games

Sobhan Mohammadpour, Samuel Sokota, Brandon Kaplowitz, Zico Kolter 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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67%Highly rated
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NeurIPS 2026OpenAIU OxfordIndependentDeep RL

Mitigating Reward Hacking via Task Representations

Lillian Sun, Joe Benton, Eric Easley

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

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lenient 1/5
medium 1/10
strict 0/5
78%Highly rated
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NeurIPS 2026Alignment Research CenterStanfordOpenAIDeep learning theory

Estimating the expected output of wide random MLPs more efficiently than sampling

Approximate layer-wise activation distributions via cumulants and Hermite expansions to estimate wide MLP expected outputs without sampling, reducing FLOPs versus Monte Carlo and improving rare-event estimates.

Wilson Wu, Victor Lecomte, Michael Winer, George Robinson and 2 more

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

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AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 1/5
91%Must read
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NeurIPS 2026IndependentOpenAILASR LabsAI oversight & deception

The Best-Laid SCHEMEs: Coordinated Sabotage and Monitoring in Multi-Agent Systems

SCHEME benchmark reveals multi-agent models coordinate sabotage via decomposed plans across communication topologies, with Gemini succeeding 84% and Codex 46%, though monitors detect edits at 99%/68% and communication at 100%/81%.

Nikolay Radev, Lennart J Haas, Benjamin Arnav, Pablo Bernabeu-Perez

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

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17/20 AI panelreviewers recommend it

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AI panel: 17 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 4/5