新基准暴露LLM规划隐藏盲区
SIMMER是一个基于人类策划的厨房领域符号世界模型的新基准，包含77个动作、262个独特物体和约46,800种语义真实的交互。实验在六个LLM上进行，前沿模型错误率最高仅17%，最多56%的计划包含潜在失败，其中多数导致不可逆后果。通过反事实预测模拟，潜在失败可减少72%，不可逆情况减少75%。该基准揭示了现有评估忽略的关键失败类型。
SIMMER: Benchmarking Latent Failures in LLM Executable Planning with a World Model
Large language models (LLMs) are increasingly deployed as planners for autonomous agents in household environments. While existing benchmarks evaluate whether LLM-generated plans execute successfully, they overlook a critical type of failure: latent failures. Unlike immediate failures that trigger instant feedback at execution time and enable timely correction, latent failures do not immediately halt plan execution but silently compromise goal achievement. In severe cases, they cause irreversible harm. To address this gap, we introduce SIMMER, a benchmark for evaluating latent failures in LLM planning through a human-curated symbolic world model grounded in the kitchen domain. SIMMER defines a world model comprising 77 actions, 262 unique objects, and approximately 46,800 possible interactions that are semantically realistic, derived from real-world cooking scripts. It then leverages a state machine executor that validates plans against the world model and detects immediate precondition violations, latent hazards, and irreversible failures. Experiments across six LLMs show that even frontier models achieve at most 17% error-free plans. Moreover, up to 56% of plans contain latent failures, the majority of which lead to irreversible consequences. We further demonstrate that explicit state reasoning via counterfactual foresight simulation can reduce latent failures by up to 72% and irreversible cases by up to 75%, suggesting a promising direction for more robust LLM planners.