1.7k citations · 2.2k across the 42 of their papers we have counts for
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Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets
Gwen Legate, Rahaf Aljundi, Eugene Belilovsky
Large language models (LLMs) trained on vast amounts of data have demonstrated impressive performance over a range of tasks, but future performance gains will require increasing am…
DemoBridge: A Simulation-in-the-Loop Toolkit for Single-View Human Demonstration Retargeting
Zehao Wang, Fabien Despinoy, Sergey Zakharov +2
We present DemoBridge, an toolkit that turns a single-view RGB stereo recording of a human hand demonstration into an executable, physics-validated robot-arm trajectory. Retargetin…
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…
Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models
Simone Caldarella, Davide Talon, Rahaf Aljundi +2
Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assumption that longer reasoning…
From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
Christian Gumbsch, Leonardo Barcellona, Lennard Schünemann +7
Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics. Recent work has explored…
Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations
Andrii Zadaianchuk, Leonardo Barcellona, Lennard Schuenemann +7
Accurately reconstructing complex full multi-object scenes from sparse observations remains a core challenge in computer vision and a key step toward scalable and reliable simulati…
SEM: Sparse Embedding Modulation for Post-Hoc Debiasing of Vision-Language Models
Quentin Guimard, Federico Bartsch, Simone Caldarella +3
Models that bridge vision and language, such as CLIP, are key components of multimodal AI, yet their large-scale, uncurated training data introduce severe social and spurious biase…
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…
OMNI-LEAK: Orchestrator Multi-Agent Network Induced Data Leakage
Akshat Naik, Jay Culligan, Yarin Gal +4
As Large Language Model (LLM) agents become more capable, their coordinated use in the form of multi-agent systems is anticipated to emerge as a practical paradigm. Prior work has…
Evaluating the Impact of Post-Training Quantization on Reliable VQA with Multimodal LLMs
Paul Jonas Kurz, Tobias Jan Wieczorek, Mohamed A. Abdelsalam +2
Multimodal Large Language Models (MLLM) are increasingly deployed in domains where both reliability and efficiency are critical. However, current models remain overconfident, produ…