Wenliang Zheng

Existing multi-agent benchmarks primarily test in competitive settings, short-horizon interactions under 20 steps, or simply aggregate individual performance, failing to isolate and highlight genuine collaboration capabilities of LLM-based agents. We introduce AgentWorld, a benchmark of 100 human-annotated tasks (with 100 augmented variants) for evaluating long-horizon, multi-agent collaboration. Tasks span 50+ interaction rounds across a rich MMORPG sandbox and require 3 - 20 agents with asymmetric roles and abilities to coordinate through communication, joint planning, and resource sharing under a blackbox setting where each agent acts independently without access to others' internal states. To quantify collaboration effectiveness in addition to conventional binary task success, we propose Causal Collaboration Effectiveness (CCE), a graph-based metric that traces causal dependencies between agent actions and measures what fraction of a team's effort actually contributed to the outcome. Experiments with Gemini 3 Flash, Claude Haiku 4.5, GPT-5 Mini, and DeepSeek R1-70B show that even the best model achieves only 52.0% task success, with systematic failure modes including communication breakdowns, role confusion, and inability to maintain shared plans across rounds. AgentWorld is fully open-source.

Recent advances in large language models (LLMs) have enabled the large-scale generation of highly fluent and persuasive news-like content. While prior work has often treated fake news detection as a binary classification problem, today's fake news increasingly arises through multi-stage human–AI collaboration, where strategically placed inaccuracies are embedded within otherwise credible narratives. These mixed-truth cases are both more complex and more difficult to detect, yet remain underrepresented in existing benchmarks. To address this gap, we introduce MANYFAKE, a synthetic benchmark containing 6,798 news articles generated through a strategy-driven pipeline that explicitly captures the many ways fake news is constructed and refined. Using this benchmark, we evaluate a range of state-of-the-art fake news detectors, including LLMs with and without explicit reasoning. We find that models achieve high fake-news detection rates on fully fabricated stories, but remain brittle when falsehoods are subtle, optimized, and interwoven with accurate information. These results highlight substantial variation in detector robustness across different forms and degrees of falsity. The dataset is available at https://github.com/XinyuWang1998/MANYFAKE_Benchmark.