Good Papers

Showing papers from University of Mannheim Show all papers

45%Niche pick
?Niche pickVote to see the score

CRISP: Compositional Reasoning over Images via Stackable Programs for VLMs

Arnas Uselis, Yujin Jeong, Yanpeng Zhao, Alexander Rubinstein and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · 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
74%Highly rated
?Highly ratedVote to see the score

Concepts in Motion: Temporal Concept Bottleneck Model for Interpretable Video Classification

MoTIF uses a transformer over temporally grounded concept sequences with per-concept self-attention and automatic VLM concept discovery to improve interpretable video classification.

Patrick Knab, Sascha Marton, Philipp J Schubert, Drago A Guggiana Nilo and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · Code ★ 6

– 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 4/5
medium 4/10
strict 1/5
74%Highly rated
?Highly ratedVote to see the score

Two Stages of Folding: Convergent Mechanisms in AI Protein Folding Trunks

Protein folding models share a two-stage trunk mechanism initializing biochemical signals then spatial features, with causally steerable, interchangeable representations across architectures.

Kevin Lu, Jannik Brinkmann, Stefan T Huber, Aaron Mueller and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · 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 5/10
strict 1/5
86%Must read
?Must readVote to see the score

TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks

TabPrep is a lightweight feature engineering pipeline that targets structural data patterns to consistently boost tabular model performance across benchmarks.

Andrej Tschalzev, Nick Erickson, Yuyang (Bernie) Wang, Huzefa Rangwala and 3 more

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

– ReadersNo votes yet
14/20 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: 14 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 1/5