11 papers · 1 filter
U-Space: Uncovering When and Why Uncertainty Arises in Language Models
Tobias Braun, Nils Loose, Alexander Herzog +4
Large language models are informing decisions with ever-higher stakes. As the consequences of their errors grow, a central question becomes harder to ignore: how much can we trust…
The Poisoned Conversation: Privacy-Leaking Watermarks in Unified Multimodal Models
Tobias Braun, Jonas Henry Grebe, Emil Sivic +4
Multimodal models are increasingly shifting toward unified architectures that understand and generate text, images, and other modalities within a shared conversational context. Thi…
Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating
Tobias Braun, Jonas Grebe, Louis Rethfeld +1
The widespread adoption of generative AI enables students to outsource cognitive effort to increasingly capable assistants, creating an illusion of competence while undermining the…
VETO: Towards Protecting Images From Frontier AI Editing
Jonas Grebe, Hossein Shakibania, Tobias Braun +2
The rise of powerful, accessible image-editing models such as FLUX.2 has brought high-fidelity editing within broad reach. Their capabilities now extend beyond localized modificati…
Obliviate: Erasing Concepts from Autoregressive Image Generation Models
Hossein Shakibania, Jonas Henry Grebe, Tobias Braun +4
The widespread adoption of generative AI models has intensified concerns about misuse, including the creation of unsafe or disturbing imagery. To mitigate such issues, several conc…
GEM: Geometric Erasure by Contrastive Velocity Matching in Rectified Flows
Jonas Henry Grebe, Tobias Braun, Anna Rohrbach +1
While the rapid adoption of multimodal generative models offers immense potential, it has also increased the risks of harmful content synthesis, deepfakes, and copyright infringeme…
Token by Token, Compromised: Backdoor Vulnerabilities in Unified Autoregressive Models
Tobias Braun, Jonas Henry Grebe, Hossein Shakibania +2
Unified autoregressive models (UAMs) are transformer models that generate text as well as image tokens within a single autoregressive pass. Shared parameters and a multimodal vocab…
SIEVES: Selective Prediction Generalizes through Visual Evidence Scoring
Hector G. Rodriguez, Marcus Rohrbach
Multimodal large language models (MLLMs) achieve ever-stronger performance on visual-language tasks. Even as traditional visual question answering (VQA) benchmarks approach saturat…
Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents
Nishad Singhi, Christian Bialas, Snehal Jauhri +4
Building generalist embodied agents capable of solving complex real-world tasks remains a fundamental challenge in AI. Multimodal Large Language Models (MLLMs) have significantly a…
HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models
Reihaneh Zohrabi, Hosein Hasani, Akshita Gupta +3
Large vision-language models can produce object hallucinations in image descriptions, highlighting the need for effective detection and mitigation strategies. Prior work commonly r…
VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checking
Mark Rothermel, Marcus Kornmann, Marcus Rohrbach +1
The growing scale of online misinformation urgently demands Automated Fact-Checking (AFC). Existing benchmarks for evaluating AFC systems, however, are largely limited in terms of…