The paper introduces Qwen-GuidePlay-2B, a 2B‑parameter language model fine‑tuned for dialogue‑game interaction. The training pipeline consists of three stages: supervised fine‑tuning on successful game trajectories, weighted turn‑level fine‑tuning, and teacher‑guided fine‑tuning that corrects formatting and evaluates examples. The resulting model achieves a clemscore of 57.12 and a statscore of 42.68 on Playpen’s public validation, ranking second in the official challenge and demonstrating that careful curation can outperform more aggressive procedural methods.
By Syed Mahbubul Huq, Pranava Madhyastha
arXiv:2607. 17191v1 Announce Type: new Abstract: Human-like private chat requires more than fluent response generation: a system must preserve persona, relationship, memory, bounded knowledge, medium-specific timing, and a coherent multi-turn arc.
By Wentao Liu, Siyu Song, Xi Chen, Youjia Li, Xiaokun Wang, Min Ji, Ji Wang
The paper introduces Evaluation-as-Search (EaS), a feedback‑driven method that adaptively probes LLM‑powered meeting assistants by focusing on natural questions likely to reveal grounding failures. Using EaS, the authors build MeetingProbe, a benchmark of over 3,000 annotated question‑answer pairs from 20 transcripts across three meeting genres and three assistants. Ablation studies show that adaptive search uncovers 2.5× more failures than random probing, revealing a capability gradient and eight recurring failure categories dominated by discourse‑pragmatic challenges.
By Sami Khairy, Yasaman Hosseinkashi, Vishak Gopal, Ross Cutler
arXiv:2606. 24589v1 Announce Type: new Abstract: Scaling adversarial evaluation of large language models requires both a method for generating hard inputs and a reliable way to confirm that resulting failures are real.
By Khanak Khandelwal (Indian Institute of Technology Jodhpur)
arXiv:2608. 11236v1 Announce Type: cross Abstract: Roleplay evaluation should do more than assign a single score: it should reveal which role requirements were tested, which failed, and which dialogue evidence supports the judgment.
By Jiahui Zhang, Ziwei Zhang, Yipeng Wang, Yibo Liu, Haozhou Pang, Yikai Hu, Hongyan Ren, Lan Zhou, Qi Gan, Kai Sheng
arXiv:2607. 11363v1 Announce Type: cross Abstract: Text-based evaluations of Theory of Mind (ToM) in Large Language Models (LLMs) often involve cognitive tests akin to the Sally-Anne task that can be gamed due to exposure to relevantly similar tasks in pre-training and do not obviously test models' functional ToM abilities in ways that generalize to naturalistic settings.
By Roberta Rocca, Sami Boukortt, Geoff Keeling, Winnie Street