mlsanityData to 7 Oct 2026

Cloze: An Open Research Platform for Studying Human-AI Conversations in Mental Health Contexts

arXiv 2606.15033 ↗First posted 13 Jun 2026cs.HC cs.CL cs.CYcited by 0

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Abstract (arXiv)

Cloze is an open-source web platform for conducting controlled, monitored studies of human-AI conversation in mental health research contexts. Consumer large language model (LLM) products such as ChatGPT, Claude, and Gemini are built for individual productivity, and offer researchers little experimental control, inconsistent data export, and no shared safety scaffolding that holds across providers. Cloze gives research teams a single environment in which they configure which models participants converse with, how the AI is instructed, how conversations are scheduled over time, and which safety constraints apply unconditionally, while every message is captured with full provenance (model version, prompt configuration, timing). The platform currently supports OpenAI, Anthropic, Google, and locally hosted open-weight models served through Ollama behind a unified interface, and runs in the cloud or fully on premises so that participant data need never leave an institution. Cloze is research infrastructure for building an evidence base on human-AI interaction in mental health contexts. It is not a therapeutic product.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 19 references that are in the map. First posted on arXiv 13 Jun 2026.

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5 of 19 references.

References · 5

  1. 2025Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providerscited by 67
  2. 2025Exploring User Security and Privacy Attitudes and Concerns Toward the Use of General-Purpose LLM Chatbots for Mental Healthcited by 12
  3. 2025MindBenchAI: An Actionable Platform to Evaluate the Profile and Performance of Large Language Models in a Mental Healthcare Contextcited by 5
  4. 2025Customizable LLM-Powered Chatbot for Behavioral Science Researchcited by 1
  5. 2025Facilitating the Integration of LLMs Into Online Experiments With Simple Chatcited by 1

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