We introduce AVACraft — the first multimodal benchmark environment for complex decision-making in StarCraft II, supporting both traditional Multi-Agent Reinforcement Learning (MARL) and modern Vision-Language Model (VLM) paradigms. Existing StarCraft II environments like SMAC rely on abstract state representations that deviate from human perception and lack support for emerging VLM-based decision-making. AVACraft mitigates these limitations via a unified framework, which provides RGB visual inputs, natural language observations and structured state information, enabling systematic comparisons between training-based and zero-shot decision-making methods. Our benchmark features 21 carefully designed scenarios covering micromanagement, coordination and strategic planning, with standardized evaluation protocols for both paradigms. We establish comprehensive baselines using four MARL algorithms (IQL, QMIX, QTRAN, VDN) and multiple state-of-the-art VLMs (GPT-4o, Qwen-VL, etc.). Experimental results reveal their complementary strengths: MARL methods achieve up to 27.1% win rate after 1M training steps in complex scenarios, while VLMs deliver superior zero-shot performance (75–81% win rate) and human-aligned decision processes without any training. Systematic analysis (including expert human evaluation) also identifies key trade-offs between training efficiency, performance ceilings and interpretability across the two paradigms. Our implementation is available at https://anonymous.4open.science/r/VLM-Play-StarCraft2-70C4 .

The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and thecomplexities of constructing tasks and evaluators. To overcome these limitations, we introduce CRAB, the first cross-environment agent benchmark framework, incorporating a graph-based fine-grained evaluation method and an efficient task generation method. Our framework supports multiple devices and can be easily extended to any environment with a Python interface. Leveraging CRAB, we develope CRAB Benchmark-v0 comprising 120 tasks in computer desktop and mobile phone environments. We evaluated 6 advanced MLMs using different single and multi-agent system configurations on this benchmark. The experimental results demonstrate that the single agent with GPT-4o achieves the best completion ratio of 38.01%.

Stepping from sentence-level to document-level, the research on relation extraction (RE) confronts increasing text length and more complicated entity interactions. Consequently, it is more challenging to encode the key information sources—relevant contexts and entity types. However, existing methods only implicitly learn to model these critical information sources while being trained for RE. As a result, they suffer the problems of ineffective supervision and uninterpretable model predictions. In contrast, we propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for RE. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately so as to enhance interpretability. By assessing model uncertainty, SAIS further boosts the performance via evidence-based data augmentation and ensemble inference while reducing the computational cost. Eventually, SAIS delivers state-of-the-art RE results on three benchmarks (DocRED, CDR, and GDA) and outperforms the runner-up by 5.04% relatively in F1 score in evidence retrieval on DocRED.