We are entering an era in which individuals and organizations increasingly deploy dedicated AI agents that interact and collaborate with other agents.However, the dynamics of multi-agent collaboration under privacy constraints remain poorly understood.In this work, we present PAC-Bench, a benchmark for systematic evaluation of multi-agent collaboration under privacy constraints.Experiments on PAC-Bench show that privacy constraints substantially degrade collaboration performance and make outcomes depend more on the initiating agent than the partner.Further analysis reveals that this degradation is driven by recurring coordination breakdowns, including early-stage privacy violations, overly conservative abstraction, and privacy-induced hallucinations.Together, our findings identify privacy-aware multi-agent collaboration as a distinct and unresolved challenge that requires new coordination mechanisms beyond existing agent capabilities.

Accurately synthesizing 3D scenes from user-provided text descriptions is crucial for developing embodied agents. Despite the importance of scene-description alignment, existing evaluation methods for such text-guided 3D scene synthesis either capture only coarse similarity between the synthesized scene and the user description, or ignore the spatial reasoning for verifying object placement. None of them addressed the fine-grained constraints (e.g., X needs to be in the scene in a Y manner) implied by the description from users. To address this, we introduce LEGO, a benchmark dataset that pairs each user description with human-annotated constraints and a reference scene, and LEGO-EVAL, an evaluation framework that decomposes a description into atomic constraints and verifies each one using tools that ground textual references to 3D objects and reason about their spatial relationships. We show that (i) LEGO-EVAL evaluates misalignment far more accurately than existing methods and (ii) current scene synthesis approaches achieve only at most 10% success rate in LEGO-EVAL. Our code and dataset are publicly available at https://gyeomh.github.io/LEGO-Eval/.