collaborators

7 papers

cs.CL2026

LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure

Fanfei Li, Jana Zeller, Manuel Prada-Corral +4

Modern language models are trained on heterogeneous web-scale text corpora. Consequently, studying knowledge and skill acquisition is difficult, as prior exposure to related conten…

cs.CV2026

Visual prompt engineering for video models

Robert Geirhos, Yuxuan Li, Thaddäus Wiedemer +7

In the age of foundation models, a model is only as good as its prompt. For this reason, prompt engineering has become an essential technique for improving language model performan…

cs.MM2026

VGGSounder: Audio-Visual Evaluations for Foundation Models

Daniil Zverev, Thaddäus Wiedemer, Ameya Prabhu +3

The emergence of audio-visual foundation models underscores the importance of reliably assessing their multi-modal understanding. The VGGSound dataset is commonly used as a benchma…

cs.CV2026

A Very Big Video Reasoning Suite

Maijunxian Wang, Ruisi Wang, Juyi Lin +53

Rapid progress in video models has largely focused on visual quality, leaving their reasoning capabilities underexplored. Video reasoning grounds intelligence in spatiotemporally c…

cs.LG2025

MATH-Beyond: A Benchmark for RL to Expand Beyond the Base Model

Prasanna Mayilvahanan, Ricardo Dominguez-Olmedo, Thaddäus Wiedemer +1

With the advent of DeepSeek-R1, a new wave of reinforcement learning (RL) methods has emerged that seem to unlock stronger mathematical reasoning. However, a closer look at the ope…

cs.LG2025

Video models are zero-shot learners and reasoners

Thaddäus Wiedemer, Yuxuan Li, Paul Vicol +6

The remarkable zero-shot capabilities of Large Language Models (LLMs) have propelled natural language processing from task-specific models to unified, generalist foundation models.…