9 papers
Dissecting Training-Free Uncertainty Estimation in Multimodal Large Language Models
Soroush Seifi, Vaggelis Dorovatas, Lin Li +2
Multimodal Large Language Models (MLLMs) are increasingly deployed in safety-critical settings, yet estimating when their answers can be trusted remains underexplored. We systemati…
Continual Self-Improvement with Lightweight Experiential Latent Memories
Vaggelis Dorovatas, Nancy Kalaj, Rahaf Aljundi
Large language models achieve strong reasoning performance by scaling inference-time compute, yet remain fundamentally stateless, discarding the rich, self-produced reasoning trace…
Ego: Embedding-Guided Personalization of Vision-Language Models
Soroush Seifi, Simon Gardier, Vaggelis Dorovatas +2
AI assistants that support humans in daily life are becoming increasingly feasible, driven by the rapid advancements in multimodal language models. A key challenge lies in overcomi…
Position: Modular Memory is the Key to Continual Learning Agents
Vaggelis Dorovatas, Malte Schwerin, Andrew D. Bagdanov +21
Foundation models have transformed machine learning through large-scale pretraining and increased test-time compute. Despite surpassing human performance in several domains, these…
Recurrent Attention-based Token Selection for Efficient Streaming Video-LLMs
Vaggelis Dorovatas, Soroush Seifi, Gunshi Gupta +1
Video Large Language Models (Video-LLMs) excel at understanding videos in-context, provided they have full access to the video when answering queries. However, these models face ch…
Online In-Context Distillation for Low-Resource Vision Language Models
Zhiqi Kang, Rahaf Aljundi, Vaggelis Dorovatas +1
As the field continues its push for ever more resources, this work turns the spotlight on a critical question: how can vision-language models (VLMs) be adapted to thrive in low-res…
Masked Diffusion Language Models with Frequency-Informed Training
Despoina Kosmopoulou, Efthymios Georgiou, Vaggelis Dorovatas +2
We present a masked diffusion language modeling framework for data-efficient training for the BabyLM 2025 Challenge. Our approach applies diffusion training objectives to language…
Auto-Compressing Networks
Vaggelis Dorovatas, Georgios Paraskevopoulos, Alexandros Potamianos
Deep neural networks with short residual connections have demonstrated remarkable success across domains, but increasing depth often introduces computational redundancy without cor…
Personalization Toolkit: Training Free Personalization of Large Vision Language Models
Soroush Seifi, Vaggelis Dorovatas, Matteo Cassinelli +3
Personalization of Large Vision-Language Models (LVLMs) involves customizing models to recognize specific users or object instances and to generate contextually tailored responses.…