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NeurIPS 2026AXXXApplied AI InstituteAIRIFlow matching

Unlocking the Duality between Flow and Field Matching

CFM and forward-only IFM are equivalent via a bijection, but general IFM is strictly more expressive, yielding cross-framework techniques.

Daniil Shlenskii, Alexander Varlamov, Nazar Buzun, Aleksandr Korotin

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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8/20 AI panelreviewers recommend it

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lenient 2/5
medium 4/10
strict 2/5
70%Highly rated
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NeurIPS 2026AXXXApplied AI InstituteSkoltechFlow matching

Midpoint Generative Models

Midpoint Generative Models define a midpoint divergence from flow matching symmetry to train one-step generators with competitive results.

Daniil Shlenskii, Nikita Gushchin, Lev Novitskiy, Dmitry V. Dylov 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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5/20 AI panelreviewers recommend it

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AI panel: 5 of 20 reviewers recommend it
lenient 2/5
medium 3/10
strict 0/5
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NeurIPS 2026AXXXApplied AI InstituteApplied AI Institute, Moscow IndInstitute of Numerical MathematiLLM evaluation & benchmarks

TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models

TUBE introduces a variational upper bound with unbiased Monte Carlo estimation to evaluate discrete diffusion model log-likelihoods, revealing that block diffusion and any-order autoregressive models remain below exact autoregressive baselines.

Arseny Ivanov, Sergei Kholkin, Vladislav Gromadskii, Grigoriy Ksenofontov and 2 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · 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 4/5
medium 6/10
strict 2/5