activity
20142026
most citedIs Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization

54 citations · 126 across the 51 of their papers we have counts for

collaborators

58 papers

cs.LG2026

BehaviorFormer: Discovering Behavioral Patterns via Clusterable Next-Token Prediction

Tobias Schneider, Danial Fahrner, Soren Antebi +4

Discovering behavioral patterns in open-ended games is challenging due to the lack of clear objectives or ground-truth annotations. In this work, we extend a next-token prediction…

cs.CL2026

Evaluating and Benchmarking the System One Model Jev

Tobias Deußer, Lorenz Sparrenberg, Rafet Sifa

Jev is a commercial System One model from TypeSafe AI that does not generate text: given a state and typed questions, it returns a choice from fixed options, a position on a rubric…

cs.CL2026

Automated Regulatory Compliance Question Answering in Financial Services with Domain-Adapted Retrieval-Augmented Generation

Tobias Deußer, Abhishek Pillai, Aurelio F. Bariviera +5

Financial institutions operate under dense, frequently amended rulebooks, and answering a compliance question correctly requires not only fluency but verifiable grounding in the au…

cs.CL2026

On the Impact of Anonymization on the Performance of Large Language Models

Tobias Deußer, Max Hahnbück, Lorenz Sparrenberg +3

As large language models are increasingly deployed in sensitive domains, anonymizing input data to protect personally identifiable information has become a critical practice. Howev…

cs.LG2026

Accuracy is Not Enough: A Divergence-Based Approach to Evaluate Fidelity Loss in Quantized LLMs

Shahzeb Qamar, Lorenz Sparrenberg, Christian Bauckhage +5

Deployment of Large Language Models (LLMs) on memory-constrained edge devices relies heavily on aggressive post-training quantization. However, evaluating these models is largely b…

cs.AI2026

The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs

Baha Rababah, Shahzeb Qamar, Lorenz Sparrenberg +4

Post-Training Quantization has become widely used to compress large language models to make them deployable on resource-constrained devices. However, the evaluation of quantization…