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20192026
most citedAdversarial Robustness Guarantees for Classification with Gaussian Processes

8 citations · 23 across the 18 of their papers we have counts for

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5 papers · 1 filter

cs.CL2026

On The Effectiveness-Fluency Trade-Off In LLM Conditioning: A Systematic Study

Iuri Macocco, Pau Rodríguez, Arno Blaas +3

Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet a clear understanding of the involved trade-offs remains elusive. C…

cs.CL2026

Uncertainty Quantification for LLM Function-Calling

Zihuiwen Ye, Lukas Aichberger, Michael Kirchhof +5

Large Language Models (LLMs) are increasingly deployed to autonomously solve real-world tasks. A key ingredient for this is the LLM Function-Calling paradigm, a widely used approac…

cs.LG2026

The Design Space of Tri-Modal Masked Diffusion Models

Louis Bethune, Victor Turrisi, Bruno Kacper Mlodozeniec +21

Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and fine-tuning a base unimodal model for bimodal gen…

cs.CR2026

Sparse Autoencoders are Capable LLM Jailbreak Mitigators

Yannick Assogba, Jacopo Cortellazzi, Javier Abad +3

Jailbreak attacks remain a persistent threat to large language model safety. We propose Context-Conditioned Delta Steering (CC-Delta), an SAE-based defense that identifies jailbrea…

cs.AI2026

GenCtrl -- A Formal Controllability Toolkit for Generative Models

Emily Cheng, Carmen Amo Alonso, Federico Danieli +4

As generative models become ubiquitous, there is a critical need for fine-grained control over the generation process. Yet, while controlled generation methods from prompting to fi…