Publications

Towards Assessing Deep Learning Test Input Generators

Seif Mzoughi

Ahmed Haj Yahmed

Mohamed Elshafei

Foutse Khomh

Diego Elias Costa

2025-04-03

ArXiv (prépublication)

arxiv.org

Why do LLMs attend to the first token?

Federico Barbero

'Alvaro Arroyo

Xiangming Gu

Christos Perivolaropoulos

Michael M. Bronstein

Petar Velivckovi 'c

Razvan Pascanu

2025-04-03

ArXiv (prépublication)

arxiv.org

DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning

Sara Vera Marjanovi'c

Arkil Patel

Vaibhav Adlakha

Milad Aghajohari

Parishad BehnamGhader

Amirhossein Kazemnejad

Gaurav Kamath

Marius Mosbach

Karolina Stanczak

Siva Reddy

Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an ans… (voir plus)wer for a given input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly"thinking"about a problem before providing an answer. This reasoning process is publicly available to the user, creating endless opportunities for studying the reasoning behaviour of the model and opening up the field of Thoughtology. Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning, our analyses on DeepSeek-R1 investigate the impact and controllability of thought length, management of long or confusing contexts, cultural and safety concerns, and the status of DeepSeek-R1 vis-\`a-vis cognitive phenomena, such as human-like language processing and world modelling. Our findings paint a nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning, where extra inference time can impair model performance. Furthermore, we find a tendency for DeepSeek-R1 to persistently ruminate on previously explored problem formulations, obstructing further exploration. We also note strong safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning counterpart, which can also compromise safety-aligned LLMs.

2025-04-02

ArXiv (prépublication)

arxiv.org