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cs.AI2026
Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
Kale-ab Abebe Tessera, Andras Szecsenyi, Cameron Barker +7
As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks. Yet existing evaluations rar…
cs.AI2026
Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents
Davide Paglieri, BartÅomiej CupiaÅ, 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.AI2025
BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games
Davide Paglieri, BartÅomiej CupiaÅ, 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…