4arXiv cs.AI (Artificial Intelligence)·
Taxonomy of human-AI team types derived from analysis of 53 papers
A new arXiv preprint analyzes 53 papers on human-AI teaming and proposes a five-cluster taxonomy grounded in psychological teaming frameworks: AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity. The authors argue that disparate team types are currently studied under a single shared definition, raising concerns about cross-paper generalizability of findings. The paper concludes with a reporting checklist and guidance for field synthesis.
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Taxonomy and governance gap analysis for AI contributors in open-source software
A preprint from arXiv analyzes how open-source organizations are handling AI-generated and agent-driven contributions, comparing policies across six major projects (SymPy, LLVM, matplotlib, OpenInfra, Apache Software Foundation, Linux Foundation). The authors develop a six-dimensional taxonomy covering disclosure, responsibility, human oversight, licensing, enforcement, and maintainer workload, and score each organization's policy maturity. The paper maps documented agent incidents onto governance gaps and identifies misalignments with emerging regulatory frameworks including the EU AI Act, NIST AI RMF, and ISO/IEC 42001, proposing a harmonized tiered framework.
Human capital traits, not model benchmarks, predict effective human-AI collaboration in forecasting
A pilot study using Polymarket as an externally resolved benchmark finds that the value of human-AI collaboration in forecasting is highly individual-dependent, with a trimodal distribution: most users either defer to the model or rubber-stamp prior beliefs, while a minority engage in genuine complementary reasoning that matches or beats market accuracy. Collaborative traits—perspective-taking, intellectual humility, and curiosity—predicted who reached the high-performance mode, while raw cognitive ability and model benchmark scores did not. The results challenge the common practice of reporting human-AI collaboration effects as a single average, and a pre-registered replication is in preparation.
Taxonomy of conceptual alignment in human-robot dialogue with dialogue act schema
A preprint introduces a taxonomy for characterizing conceptual alignment in human-robot interaction, framing it as a bidirectional, co-constructive process rather than a unidirectional one. The authors define what triggers alignment initiation and what levels of conceptual understanding are involved, and provide a dialogue act schema as an operational tool for analyzing alignment moves. The work aims to give researchers and designers a structured foundation for comparing and building conceptual alignment systems in HRI.
Theoretical analysis of calibration preservation in human-AI teaming frameworks
A new arXiv paper examines human-AI teaming through the lens of statistical calibration, analyzing both combination and delegation frameworks. The authors show that existing combination methods fail to preserve the human's calibration, while delegation methods shift the calibration burden to a rejector meta-model that must be calibrated finely enough to identify where each party excels. This demand grows with human expertise and becomes unattainable when the human uses information unavailable to the system.
Conceptual framework for analyzing dialogue dynamics in human-AI and multi-agent collaborative problem-solving
A new arXiv preprint proposes a hierarchical two-layer coding scheme for analyzing dialogue in collaborative problem-solving, integrating cognitive and metacognitive dimensions. The framework is validated across nine datasets spanning multiple domains and is positioned to apply to both human-AI and multi-agent collaboration contexts. A key finding is that metacognitive regulation is a strong discriminator of deeper collaboration quality.
Making AI Work: Leadership, Lab, and Crowd
This commentary from One Useful Thing proposes a framework for organizational AI adoption centered on three elements: leadership commitment, structured experimentation (lab), and distributed employee engagement (crowd). The piece offers practical guidance for companies navigating AI integration. As a tier-2 commentary source, it reflects practitioner thinking on enterprise AI deployment patterns rather than reporting new technical developments.
MIT Technology Review: Leadership challenges in hybrid human-AI enterprises
MIT Technology Review examines how leadership teams are adapting to a projected 300% surge in AI agent adoption over the next two years. The piece focuses on the organizational and managerial implications of AI agents that autonomously coordinate complex tasks across tools and environments, distinguishing them from prior automation paradigms. The article addresses strategic and workforce management questions for enterprises integrating agentic AI.
AI persuasive framing boosts cooperation in collective dilemmas but antisocial effects are larger and more persistent
A preprint reports a 1,283-participant experiment using AI assistants to nudge behavior in iterated Collective Risk Games. Personalized prosocial framing (matched to Social Value Orientation profiles) increased cooperation and group success, but effects faded within a few rounds. Critically, when the same AI system was reconfigured to promote selfish behavior, the negative effects were larger and substantially more persistent — revealing an asymmetry that underscores dual-use risks of AI-driven behavioral influence.