IRWOZ 2.0 is a refined dialogue dataset for industrial human‑robot interaction, expanding to 390 dialogues across four domains—Assembly, Delivery, Position, and Relocation. The dataset was improved using large language models (Mistral and Claude‑3.5) for generation and quality refinement, including manual corrections and automated typo removal. Benchmark tests show a substantial boost in dialogue state‑tracking performance, with GPT‑2’s BLEU‑4 score rising from 0.1651 to 0.5604 compared to the original IRWOZ.
By Chen Li, Dimitrios Chrysostomou
arXiv:2606. 18747v1 Announce Type: cross Abstract: Expressive gestures are essential for natural and effective communication, complementing speech when verbal cues alone are insufficient (e.
By Chris Lee, Flora Salim, Benjamin Tag, Francisco Cruz
Long-term physical coexistence with intelligent robots requires more than capable robot policies. A persistent robotic assistant must support diverse user-facing interfaces, maintain long-horizon memory of people and preferences, coordinate across robot embodiments, and translate human intent into safe physical execution.
arXiv:2607. 11377v1 Announce Type: cross Abstract: Long-term physical coexistence with intelligent robots requires more than capable robot policies.
By Weiqi Jin, Peijun Tang, Kuncheng Luo, Baifu Huang, Binyan Sun, Haotian Yang, Shangjin Xie, Jianan Wang
arXiv:2607. 18985v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge.
By Jialian Li, Junhong Liu, Yuchen Cao, Weiran Guo, Jiaming Song, Xutao Wang, Yi Zhao, Jiangpin Liu, Jie Chen
The paper introduces Inverse Turing Bench, a benchmark designed to assess how well language models can distinguish between human-only and human-AI dialogues in multi-turn text. It provides paired dialogue transcripts and evaluates models on correctly identifying the type of conversation. Preliminary results show GPTZero, Claude Opus-4.6, and GPT-5.5 achieving the highest accuracies of 89.41%, 77.92%, and 75.94% respectively, highlighting both the strengths and limitations of statistical versus semantic detection approaches.
By William Hager, Ishika Rathi, Masum Hasan, Cameron Jones