activity
20232026
most citedBALROG: Benchmarking Agentic LLM and VLM Reasoning On Games

2 citations · 5 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Up and Down the Abstraction Ladder: Code-Based Skills for Language Agents

Bartłomiej Cupiał, Jens Tuyls, Maciej Wołczyk +5

Language agents struggle to act and learn in environments that require long sequences of low-level actions. Code-based abstractions can make these agents more productive by letting…

cs.AI2026

What Drives Interactive Improvement from Feedback?

Bartłomiej Cupiał, Jan Łojek, Mikołaj Garstecki +3

We study when natural-language feedback produces improvement beyond the gains obtainable from repeated attempts alone. In multi-turn language agent setting, higher final accuracy c…

cs.LG2026

Learning Multi-Agent Coordination via Sheaf-ADMM

Jeffrey Seely, Bartłomiej Cupiał, Llion Jones

We present a differentiable optimization framework for multi-agent coordination. An input is decomposed into overlapping local views, each processed by an agent that solves a conve…

cs.AI2025

Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents

Davide Paglieri, Bartłomiej Cupiał, Jonathan Cook +6

Training large language models (LLMs) to reason via reinforcement learning (RL) significantly improves their problem-solving capabilities. In agentic settings, existing methods lik…

cs.AI2024★ 2 cited

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games

Davide Paglieri, Bartłomiej Cupiał, Samuel Coward +10

Large Language Models (LLMs) and Vision Language Models (VLMs) possess extensive knowledge and exhibit promising reasoning abilities, however, they still struggle to perform well i…

cs.LG2024★ 2 cited

Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem

Maciej Wołczyk, Bartłomiej Cupiał, Mateusz Ostaszewski +5

Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models. How…

eess.IV2023★ 1 cited

MeVGAN: GAN-based Plugin Model for Video Generation with Applications in Colonoscopy

Łukasz Struski, Tomasz Urbańczyk, Krzysztof Bucki +4

Video generation is important, especially in medicine, as much data is given in this form. However, video generation of high-resolution data is a very demanding task for generative…