The paper introduces JobMate, a persona‑grounded conversational agent that transforms peer job‑seeking posts into interactive dialogues to aid career exploration. In a study with 24 participants, JobMate helped users select relevant cases, ask follow‑up questions, and articulate constraints, contrasting with a static browsing tool that left reconstruction to users. The authors discuss design implications for blending authentic peer experiences with generative AI.
By Pengping Tan, Baoquan Zhao, Shuai Ma, Zhenhui Peng
PersonaMem-v3 is a benchmark and evaluation harness designed to assess omni-platform personal intelligence for AI agents. It is built from over one million anonymized real-world engagement histories, covering social media, chatbots, calendars, and AI companions, and tracks user preferences and habits over time. The benchmark tests agents on personalization, LLM-powered recommendation, proactiveness, agentic tool use, and geo-temporal reasoning, evaluating their ability to infer holistic user understanding, personalize responses, rerank recommendations, follow user steering, and avoid inappropriate personalization.
By Bowen Jiang, Yuan Yuan, Zhuoqun Hao, Yuchen Liu, Maohao Shen, Sihao Chen, Gregory Wornell, Chris Callison-Burch, Lyle Ungar, Dan Roth, Qi Guo, Xiangjun Fan, Camillo J. Taylor, Hanchao Yu
arXiv:2606. 18256v1 Announce Type: cross Abstract: LLM-based chatbots are increasingly applied in interpersonal domains such as counseling and peer support, where establishing human-AI rapport is crucial yet remains challenging.
By Yoonseok Oh, Inseo Jung, Jinkyu Kim, Jungbeom Lee, Minwoo Kang, Suhong Moon
arXiv:2605.14542v2 Announce Type: replace
Abstract: A skilled live-commerce host is not merely a narrator, but a sales agent who converts viewer curiosity into purchase intent through expert product...
By Yuyan Chen
The Living Library is an end‑to‑end framework that converts fragmented digital archives into governed, conversational exhibit experiences. Developed at the Theodore Roosevelt Presidential Library, it digitizes a 300,000‑record collection, enriches it with OCR and metadata, and publishes it to a hybrid dense/semantic index. The system supports curator review via the Archivist App, powers a researcher interface, and runs Talk to TR—a museum exhibit where a digital human embodiment of Theodore Roosevelt answers visitors’ questions using Cross‑Era Analogical Grounding and dual‑path retrieval to keep responses grounded and responsive.
By Pengce Wang, Lucia Ronchi Darre, Matt Briney, Michaell Bakalars, Dan Rutkowski, Ursula Hardy, David Wolf, Laura Hoffman, Allen Kim, Shawn Wright, Juan Lavista Ferres
arXiv:2607. 01034v1 Announce Type: cross Abstract: Large language model (LLM)-based conversational agents (CAs) are now ubiquitous, creating new opportunities for AI-mediated behavior change.
By Hasibur Rahman, Smit Desai
The paper reports the first large‑scale empirical comparison of AI‑agent and human online communities, analyzing 73,899 Moltbook and 189,838 Reddit posts across five matched communities. It finds that Moltbook shows extreme participation inequality (Gini = 0.84 vs. 0.47) and high cross‑community author overlap (33.8% vs. 0.5%). Linguistically, AI‑generated content is emotionally flattened, more assertive than exploratory, and socially detached, leading to community‑level homogenization that is largely a structural artifact of shared authorship. At the individual level, AI agents are more identifiable than human users due to outlier stylistic profiles amplified by their extreme posting volume.
By Agam Goyal, Olivia Pal, Hari Sundaram, Eshwar Chandrasekharan, Koustuv Saha
arXiv:2606. 22748v2 Announce Type: replace-cross Abstract: Some professional authors are beginning to use AI tools to help produce their fiction writing.
By Neel Gupta, Maria Antoniak, Melanie Walsh
arXiv:2607. 26236v1 Announce Type: cross Abstract: AI-generated counterspeech offers a scalable and effective strategy to mitigate online toxicity by promoting more constructive dialogue.
By Lorenzo Cima, Alessio Miaschi, Amaury Trujillo, Marco Avenuti, Felice Dell'Orletta, Stefano Cresci
arXiv:2606. 16475v1 Announce Type: cross Abstract: Many societal decisions are settled by contests of persuasion.
By Kobi Hackenburg, Caroline Wagner, Luke Hewitt, Ben M. Tappin, Ed Saunders, Hannah Rose Kirk, Helen Margetts, Christopher Summerfield
AI‑moderated interviews are a scalable market‑research method that can match human moderation in depth, cover more themes, and recover more customer needs while keeping budgets constant. Participants, however, feel more emotionally engaged with live humans. Digital twins built from AI‑moderated data predict consumer responses better than demographics‑only personas, but the added richness does not improve quantitative predictions over static interviews, and prediction errors stem from differences in thinking styles and data‑distribution gaps.
By Yuting Deng, Jingxuan Liu, Olivier Toubia, Naman Jain
arXiv:2606. 12418v1 Announce Type: cross Abstract: The rapid proliferation of large language models (LLMs) has produced a striking cultural practice: using conversational AI for divination.
By Chuang Li, Lixuan Wang, Yuqi Chen, Ze Hong