We introduce PhysLadder, a hierarchical Sim-Real benchmark for evaluating physical interaction capabilities in contact-rich manipulation. Its 16 household tasks span three cumulative levels: contact selection and geometric alignment (L1), force regulation within sustained contact (L2), and coordinated contact reconfiguration and force regulation (L3). Each task is instantiated in physically and visually matched simulated and real-world environments, with shared task definitions and success criteria. We evaluate GPT-6 Astra zero-shot with three trials per task in each environment. Real-world success rates decline from 80.0% at L1 to 47.6% at L2 and 0.0% at L3, with corresponding simulation rates of 66.7%, 47.6%, and 0.0%. Ten of the 16 tasks have identical success counts across environments; the remaining six differ by one success out of three trials. Across both environments, recurring difficulties in spatial reasoning, contact selection, force regulation, and coordination often prevent local physical progress from accumulating into task completion. These findings characterize Astra's observed capability boundary across increasing interaction demands and, for this model, support matched simulation as a repeatable, scalable testbed for studying the sustained, coordinated physical interactions that limit reliable manipulation.
Runhuai Chen, Dian Shen, Dandan Zhang, Kaihong Huang, Linghui Meng, Beilun Wang Southeast University
Text-attributed graphs (TAGs) require jointly modeling relational structure and node-level text. Existing GNN-LLM approaches perform by incorporating large language models at inference time for processing the text attributes, resulting in costly deployment. More fundamentally, LLM knowledge is typically used in a sample-wise manner, leading to inefficient utilization across graph instances. In this work, we study how interactions with LLM embedding spaces affect graph representations, and show that projecting into the LLM space can learn better GNNs. That is to say, the knowledge encoded in LLM embeddings can be compressed into graph representations. Based on this insight, we propose a framework that internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. Our framework employs a hierarchical Proxy-Purifier module with distribution-level regularization, using LLM embeddings only as training-time guidance. With this module, the model operates TAGs without invoking LLMs, achieving high efficiency as standard GNNs without LLMs. Notably, experiments on five popular TAG tasks further demonstrate that our method can also achieve consistent performance gains, in comparison to existing GNN-LLM approaches.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表