"Nobody Did This": Contribution, Originality, and Accountability in Agent-Mediated Collaboration

• URL: http://arxiv.org/abs/2607.26387v1
• Date: Wed, 29 Jul 2026 01:41:29 GMT
• Title: "Nobody Did This": Contribution, Originality, and Accountability in Agent-Mediated Collaboration
• Abstract summary: This workshop brings together researchers and practitioners to confront what we call contribution dissolution.<n>We argue that this dissolution begins before collaboration itself, in the individual worker's own uncertainty about what is genuinely theirs.<n>Our goal is to produce a shared research agenda and the foundations of an infrastructural response to contribution dissolution in collaborative knowledge work.
• Score: 24.416650940558437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Emergence of cooperation: A reputation-modulated reinforcement learning [19.357969561684982]
  Reputation is widely recognized as a key mechanism for sustaining cooperation.<n>We propose a spatial prisoner's dilemma game grounded in the reinforcement learning paradigm.<n>Our results reveal that reputation-modulated learning significantly promotes the emergence of cooperative behavior.
  arXiv  Detail & Related papers  (2026-08-20T13:27:41Z)
• Flout at Your Own Risk: LLMs Struggle with Pragmatic Cooperativity Under Epistemic Asymmetry [10.532912977406811]
  We investigate the pragmatic reasoning capabilities of LLMs in a multi-party collaborative task under partial information conditions.<n>We empirically assess various LLMs' abilities to act cooperatively as both speakers and listeners, including both prompting and post-training strategies.<n>Our results show that while LLMs exhibit certain pragmatic capabilities in collaborative settings, and these can be elicited through prompting and post-training, they still face challenges in pragmatic communication with incomplete information.
  arXiv  Detail & Related papers  (2026-07-13T03:31:56Z)
• Humans' ALMANAC: A Human Collaboration Dataset of Action-Level Mental Model Annotations for Agent Collaboration [54.18136393879849]
  We present ALMANAC, a dataset of action-level mental model ANnotations for Agent Collaboration.<n>ALMANAC contains 2,987 collaboration actions, each paired with theory-informed mental model annotations.<n>Our results demonstrate ALMANAC's utility in evaluating models' ability to simulate human collaborative behaviors.
  arXiv  Detail & Related papers  (2026-06-04T16:56:12Z)
• The Deliberative Illusion: Diagnosing Factual Attrition and Stance Homogenization in Multi-Agent LLM Deliberation [68.98390038721963]
  We show that multi-agent discussion erases up to 72% of issue-critical facts.<n>This loss is consequential: retained evidence can reconstruct the issue misleadingly, final stances remain anchored in base-model priors, and a single malicious agent can inject misinformation into the shrinking shared context.<n>We call for evaluations that measure which facts, uncertainties, and legitimate disagreements survive interaction.
  arXiv  Detail & Related papers  (2026-06-02T02:15:40Z)
• The Collaboration Gap in Human-AI Work [11.663456969895462]
  This poster introduces a conceptual framework for understanding why such collaboration remains fragile.<n>We argue that stable collaboration depends not only on model capability but on the interaction's grounding conditions.
  arXiv  Detail & Related papers  (2026-04-20T11:14:40Z)
• Agents of Chaos [50.53354213047402]
  We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment.<n>Twenty AI researchers interacted with the agents under benign and adversarial conditions.<n>Our findings establish the existence of security-, privacy-, and governance-relevant vulnerabilities in realistic deployment settings.
  arXiv  Detail & Related papers  (2026-02-23T16:28:48Z)
• Cooperation After the Algorithm: Designing Human-AI Coexistence Beyond the Illusion of Collaboration [0.0]
  We argue that stable human-AI coexistence is an institutional achievement that depends on governance infrastructure capable of distributing residual risk.<n>The paper provides a theoretical foundation and practical toolkit for designing human-AI systems that can sustain accountable, trustworthy cooperation over time.
  arXiv  Detail & Related papers  (2026-02-23T09:17:12Z)
• Completion $\neq$ Collaboration: Scaling Collaborative Effort with Agents [48.95020665909723]
  We argue for a shift from building and assessing task completion agents to developing collaborative agents.<n>We introduce collaborative effort scaling, a framework that captures how an agent's utility grows with increasing user involvement.
  arXiv  Detail & Related papers  (2025-10-29T17:47:18Z)
• Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games [87.5673042805229]
  How large language models balance self-interest and collective well-being is a critical challenge for ensuring alignment, robustness, and safe deployment.<n>We adapt a public goods game with institutional choice from behavioral economics, allowing us to observe how different LLMs navigate social dilemmas.<n>Surprisingly, we find that reasoning LLMs, such as the o1 series, struggle significantly with cooperation.
  arXiv  Detail & Related papers  (2025-06-29T15:02:47Z)
• Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration [50.657070334404835]
  Collaborative Gym is a framework enabling asynchronous, tripartite interaction among agents, humans, and task environments.<n>We instantiate Co-Gym with three representative tasks in both simulated and real-world conditions.<n>Our findings reveal that collaborative agents consistently outperform their fully autonomous counterparts in task performance.
  arXiv  Detail & Related papers  (2024-12-20T09:21:15Z)
• Resonating Minds -- Emergent Collaboration Through Hierarchical Active Inference [0.0]
  We investigate how efficient, automatic coordination processes at the level of mental states (intentions, goals) can lead to collaborative situated problem-solving. We present a model of hierarchical active inference for collaborative agents (HAICA) We show that belief resonance and active inference allow for quick and efficient agent coordination, and thus can serve as a building block for collaborative cognitive agents.
  arXiv  Detail & Related papers  (2021-12-02T13:23:44Z)
• Beyond Privacy Trade-offs with Structured Transparency [3.5087540566347513]
  We argue that many of these concerns reduce to 'the copy problem' We find that while the copy problem is not solvable, aspects of these amplifying problems have been addressed in a variety of disconnected fields. We propose a five-part framework which groups these efforts into specific capabilities and offers a foundation for their integration into an overarching vision we call "structured transparency"
  arXiv  Detail & Related papers  (2020-12-15T15:03:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.