COSPLAY co-evolves an LLM decision agent with a skill bank agent to improve long-horizon game performance, reporting over 25.1% average reward gains versus frontier LLM baselines on single-player benchmarks.
AVA: Attentive VLM Agent for Mastering StarCraft II
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abstract
We introduce AVACraft, a multimodal StarCraft II benchmark supporting both Multi-Agent Reinforcement Learning (MARL) and Vision-Language Model (VLM) paradigms. Unlike SMAC-family environments that rely on abstract state representations and exclude VLMs, AVACraft provides RGB visuals, natural language observations, and structured state information, enabling systematic comparison between training-based and zero-shot methods across 21 scenarios spanning micromanagement, coordination, and strategic planning. We establish comprehensive baselines: six MARL algorithms (IQL, QMIX, QTRAN, VDN, MAPPO, IPPO) with Swin-Transformer backbones trained for 5M steps, and multiple VLMs including proprietary (GPT-4o) and open-source (Qwen3-VL) models. Results reveal complementary strengths-MARL peaks at 19.3% win rate after 5M steps, while VLMs achieve 75-90% zero-shot with human-aligned decisions-exposing trade-offs between training efficiency, performance ceilings, interpretability, and deployment cost. Code: https://github.com/camel-ai/VLM-Play-StarCraft2.
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EgoEsportsQA is a new egocentric video QA benchmark from esports matches that shows state-of-the-art Video-LLMs reach only 71.58% accuracy and struggle more with tactical reasoning than basic perception.
Closed-loop VLM agents using multi-view reasoning, object-centered visualization, and single-axis rotation prediction achieve superior text-guided 6D pose rearrangement for target objects in scenes.
citing papers explorer
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Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks
COSPLAY co-evolves an LLM decision agent with a skill bank agent to improve long-horizon game performance, reporting over 25.1% average reward gains versus frontier LLM baselines on single-player benchmarks.
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EgoEsportsQA: An Egocentric Video Benchmark for Perception and Reasoning in Esports
EgoEsportsQA is a new egocentric video QA benchmark from esports matches that shows state-of-the-art Video-LLMs reach only 71.58% accuracy and struggle more with tactical reasoning than basic perception.
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Text-Guided 6D Object Pose Rearrangement via Closed-Loop VLM Agents
Closed-loop VLM agents using multi-view reasoning, object-centered visualization, and single-axis rotation prediction achieve superior text-guided 6D pose rearrangement for target objects in scenes.