# Mixed-Initiative Context: Structuring and Managing Context for Human-AI Collaboration

arXiv: https://arxiv.org/abs/2604.07121 · HTML: https://mlsanity.com/paper/2604.07121/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap](https://mlsanity.com/paper/2306.01941/) (2023)
- [Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfaces](https://mlsanity.com/paper/2309.14459/) (2023)
- [Graphologue: Exploring Large Language Model Responses with Interactive Diagrams](https://mlsanity.com/paper/2305.11473/) (2023)
- [Creating General User Models from Computer Use](https://mlsanity.com/paper/2505.10831/) (2025)
- [WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code Visualization](https://mlsanity.com/paper/2408.01703/) (2024)
- [Supporting Sensemaking of Large Language Model Outputs at Scale](https://mlsanity.com/paper/2401.13726/) (2024)
- [How Do Analysts Understand and Verify AI-Assisted Data Analyses?](https://mlsanity.com/paper/2309.10947/) (2023)
- [The HaLLMark Effect: Supporting Provenance and Transparent Use of Large Language Models in Writing with Interactive Visualization](https://mlsanity.com/paper/2311.13057/) (2023)
- [Understanding Nonlinear Collaboration between Human and AI Agents: A Co-design Framework for Creative Design](https://mlsanity.com/paper/2401.07312/) (2024)
- [Mutual Theory of Mind for Human-AI Communication](https://mlsanity.com/paper/2210.03842/) (2022)
- [OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models](https://mlsanity.com/paper/2508.21061/) (2025)
- [Alignment-Process-Outcome: Rethinking How AIs and Humans Collaborate](https://mlsanity.com/paper/2603.08017/) (2026)

## Abstract (arXiv)

In the human-AI collaboration area, the context formed naturally through multi-turn interactions is typically flattened into a chronological sequence and treated as a fixed whole in subsequent reasoning, with no mechanism for dynamic organization and management along the collaboration workflow. Yet these contexts differ substantially in lifecycle, structural hierarchy, and relevance. For instance, temporary or abandoned exchanges and parallel topic threads persist in the limited context window, causing interference and even conflict. Meanwhile, users are largely limited to influencing context indirectly through input modifications (e.g., corrections, references, or ignoring), leaving their control neither explicit nor verifiable. To address this, we propose Mixed-Initiative Context, which reconceptualizes the context formed across multi-turn interactions as an explicit, structured, and manipulable interactive object. Under this concept, the structure, scope, and content of context can be dynamically organized and adjusted according to task needs, enabling both humans and AI to actively participate in context construction and regulation. To explore this concept, we implement Contextify as a probe system and conduct a user study examining users' context management behaviors, attitudes toward AI initiative, and overall collaboration experience. We conclude by discussing the implications of this concept for the HCI community.