Good Papers: read the good ones

Showing papers from Applied AI Institute Show all papers

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026Beijing Institute of Technology Applied AI Institute Navigation & SLAM

InfoNav: A Unified Value Framework Integrating Semantic Relevance and Information Gain for Zero-Shot Object Goal Navigation

Zhi Yang, Chengpu Yu, Gonzalo Ferrer

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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026HSE Applied AI Institute DS NLP Group Moscow Technical University of I Institute of Numerical Mathemati Sparse autoencoders

Sanity Checks for Sparse Autoencoders: Do SAEs Beat Random Baselines?

Anton Korznikov, Andrey V Galichin, Alexey Dontsov, Oleg Rogov and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026SpotlightApplied AI APPLIED-AI Applied AI Institute Higher School of Economics Skoltech Event sequences

Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective

Dmitry Osin, Egor Surkov, Petr Mokrov, Igor Udovichenko and 4 more

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

72%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026AXXX Applied AI Institute AIRI Flow 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

– ReadersNo votes yet

8/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 8 of 20 reviewers recommend it

lenient 2/5

medium 4/10

strict 2/5

74%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026Applied AI Institute Nebius Skoltech Domain adaptation

Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem

Benchmarking reveals existing continuous Gromov-Wasserstein solvers fail across scenarios, and a new discrete-independent method partially fixes these issues.

Xavier Aramayo-Carrasco, Maksim Nekrashevich, Petr Mokrov, Evgeny Burnaev and 1 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet

9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it

lenient 3/5

medium 6/10

strict 0/5

70%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026AXXX Applied AI Institute Skoltech Flow 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

– ReadersNo votes yet

5/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 5 of 20 reviewers recommend it

lenient 2/5

medium 3/10

strict 0/5

80%Must read

?Must readVote to see the score

NeurIPS 2026AXXX Applied AI Institute Applied AI Institute, Moscow Ind Institute of Numerical Mathemati LLM 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

– ReadersNo votes yet

12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it

lenient 4/5

medium 6/10

strict 2/5