"Nobody Did This": Contribution, Originality, and Accountability in Agent-Mediated Collaboration
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Collaborative knowledge work is changing in ways that go beyond disclosure or transparency. LLM agents are now embedded in how teams research, design, write, and decide: mediating between members, synthesizing inputs, reformulating ideas, and drafting shared outputs. They do not only facilitate collaboration; they operate within the workflow at the moment contributions are being formed. In doing so, they risk undermining the social conditions under which contributions can be witnessed, attributed, and held accountable. This workshop brings together researchers and practitioners to confront what we call contribution dissolution: the blurring of attribution, originality, and accountability in agent-mediated collaborative work. We argue that this dissolution begins before collaboration itself, in the individual worker's own uncertainty about what is genuinely theirs, and propagates through collaborative relationships, collapsing the reliability that makes productive intellectual exchange possible. Through position statements, mapping exercises, and a hands-on activity, participants will surface how framing accountability as a documentation problem (e.g., AI use statements, watermarking, provenance logs) overlooks the conditions under which accountability is produced. Our goal is to produce a shared research agenda and the foundations of an infrastructural response to contribution dissolution in collaborative knowledge work.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 6 of its 32 references that are in the map. First posted on arXiv 29 Jul 2026.
Builds on
6 of 32 references.
References · 6
- 2025Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wildcited by 24
- 2023The AI Ghostwriter Effect: When Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authorscited by 22
- 2025When Teams Embrace AI: Human Collaboration Strategies in Generative Prompting in a Creative Design Taskcited by 8
- 2025Product Manager Practices for Delegating Work to Generative AI: "Accountability must not be delegated to non-human actors"cited by 3
- 2025AI Failure Loops in Devalued Work: The Confluence of Overconfidence in AI and Underconfidence in Worker Expertisecited by 1
- 2026AI-Mediated Hiring and the Job Search of Blind and Low-Vision Individualscited by 1
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- 2026Working with Agentic `Teammates': When a New Organizational Actor Collides with the Human Ecosystem of Work
- 2026Read the Paper, Write the Code: Agentic Reproduction of Social-Science Results
- 2026Beyond Memory: A Templated Substrate for Heterogeneous Collaborative Knowledge Work with LLM Agents
- 2026How Researchers Navigate Accountability, Transparency, and Trust When Using AI Tools in Early-Stage Research: A Think-Aloud Study
- 2026Toward an Organizational Science of Multi-Agent LLM Systems: Decoupling Who, How, and Which Algorithm
- 2026To LLM, or Not to LLM: How Designers and Developers Navigate LLMs as Tools or Teammates