Papers with VirtualHome
VestaBench: An Embodied Benchmark for Safe Long-Horizon Planning Under Multi-Constraint and Adversarial Settings (2025.emnlp-industry)
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| Challenge: | Existing safety benchmarks do not represent a diverse range of multi-constraint tasks that require long-horizon planning with a focus on safety. |
| Approach: | They propose a benchmark to assess the safety of embodied AI agents under multiple constraints. |
| Outcome: | The proposed benchmarks show that LLMs perform poorly against their tasks . they also suffer significantly compromised safety outcomes . |
Structured Preference Optimization for Vision-Language Long-Horizon Task Planning (2025.emnlp-main)
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Xiwen Liang, Min Lin, Weiqi Ruan, Rongtao Xu, Yuecheng Liu, Jiaqi Chen, Bingqian Lin, Yuzheng Zhuang, Xiaodan Liang
| Challenge: | Existing vision-language planning methods struggle with long-horizon reasoning in dynamic environments due to the difficulty of training models to generate high-quality reasoning processes. |
| Approach: | They propose a framework that enhances reasoning and action selection for long-horizon task planning through structured evaluation and optimized training. |
| Outcome: | The proposed framework outperforms existing methods on short-horizon tasks but struggles with long-horizon reasoning in dynamic environments. |
OntoGuard: Enforcing Action Admissibility for LLM Agents in Complex Interactive Environments (2026.findings-acl)
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| Challenge: | Existing approaches to large language models (LLMs) are limited by their ability to enforce environmental and behavioral admissibility. |
| Approach: | They propose an ontological framework to guard LLM agents by enforcing environmental and behavioral admissibility. |
| Outcome: | Experiments on ScienceWorld and VirtualHome show that OntoGuard can enforce environmental and behavioral admissibility while preventing invalid actions. |