1 citations · 3 across the 8 of their papers we have counts for
8 papers
Tuning Qwen2.5-VL to Improve Its Web Interaction Skills
Alexandra Yakovleva, Henrik Pärssinen, Harri Valpola +2
Recent advances in vision-language models (VLMs) have sparked growing interest in using them to automate web tasks, yet their feasibility as independent agents that reason and act…
Edit Knowledge, Not Just Facts via Multi-Step Reasoning over Background Stories
Ya Gao, Kalle Kujanpää, Pekka Marttinen +2
Enabling artificial intelligence systems, particularly large language models, to update knowledge and flexibly apply it during reasoning remains a central challenge. Existing knowl…
Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization
Minttu Alakuijala, Ya Gao, Georgy Ananov +4
As the general capabilities of artificial intelligence (AI) agents continue to evolve, their ability to learn to master multiple complex tasks through experience remains a key chal…
Efficient Knowledge Injection in LLMs via Self-Distillation
Kalle Kujanpää, Pekka Marttinen, Harri Valpola +1
In many practical applications, large language models (LLMs) need to acquire new knowledge not present in their pre-training data. Efficiently leveraging this knowledge usually rel…
Diffusion models as probabilistic neural operators for recovering unobserved states of dynamical systems
Katsiaryna Haitsiukevich, Onur Poyraz, Pekka Marttinen +1
This paper explores the efficacy of diffusion-based generative models as neural operators for partial differential equations (PDEs). Neural operators are neural networks that learn…
Hybrid Search for Efficient Planning with Completeness Guarantees
Kalle Kujanpää, Joni Pajarinen, Alexander Ilin
Solving complex planning problems has been a long-standing challenge in computer science. Learning-based subgoal search methods have shown promise in tackling these problems, but t…
Suicidal Pedestrian: Generation of Safety-Critical Scenarios for Autonomous Vehicles
Yuhang Yang, Kalle Kujanpaa, Amin Babadi +2
Developing reliable autonomous driving algorithms poses challenges in testing, particularly when it comes to safety-critical traffic scenarios involving pedestrians. An open questi…
Improved Compositional Generalization by Generating Demonstrations for Meta-Learning
Sam Spilsbury, Pekka Marttinen, Alexander Ilin
Meta-learning and few-shot prompting are viable methods to induce certain types of compositional behaviour. However, these methods can be very sensitive to the choice of support ex…