arXiv AI
Jul 3
What Types of Human-AI Teams Exist?
arXiv:2607. 02198v1 Announce Type: cross Abstract: Human-AI teaming has received increasing attention in the literature.
AutoAnalysis: An automated, self-updating meta-analysis of human-AI collaboration
AutoAnalysis is an automated, self‑updating meta‑analysis that aggregates evidence on human‑AI collaboration. Its latest run includes 1,955 effect sizes from 420 papers, showing that human‑AI combinations outperform humans alone on average but still lag behind the best of either humans or AI alone. The tool continuously incorporates new studies, offering cumulative insights into when and how human‑AI synergy occurs across different agents, tasks, and processes.
Related stories
arXiv AI
Jun 10
CollabSkill: Evaluating Human-Agent Collaboration On Real-World Tasks
arXiv:2606. 09833v1 Announce Type: cross Abstract: AI agents are reshaping the workspace, leading to drastic change of how humans work.
arXiv AI
Oct 16
Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows
Kolab is a new dataset comprising over 115 hours of screen recordings from 50 professionals across 40 occupations who collaborate with AI agents on realistic, long‑horizon tasks. The dataset includes participants’ judgments of collaboration quality and output, revealing varied coordination patterns and the need for agents to anticipate how experts will use or modify their contributions. Analysis shows current state‑of‑the‑art models overestimate collaboration success and struggle to differentiate high‑ from low‑quality interactions, indicating a gap in understanding long‑horizon human‑agent teamwork.
arXiv AI
Sep 15
Atria Dawn: The Dawn of Agentic Superintelligence
The paper introduces Atria Dawn Preview, a foundation agentic language model aimed at scientific research and engineering workflows. Trained through a Verifiable Experience Pipeline, it performs competitively across 16 real‑world benchmarks, achieving the highest scores on five. The authors also present a detailed case study of human–AI collaboration, showing that while AI proposes methods and revisions, humans retain final decision‑making and guide the research direction.
arXiv AI
Oct 19
Collaborating with AI Agents: Field Experiments on Teamwork, Productivity, and Performance
The study examined how AI agents affect teamwork, productivity, and performance using a large-scale experiment on the Pairit platform. Human-AI teams produced 50% more ads per worker and higher text quality, while human-human teams produced higher image quality. The results showed that task-oriented communication and delegation in human-AI teams improved productivity and quality across all dimensions, whereas interpersonal communication benefited human-human teams.
arXiv AI
Sep 4
Efficient Test-Time Adaptation through Human-AI Interaction
The paper introduces Test-Time Adaptation through Human‑Agent Interaction (TAHI), a method that uses iterative human feedback to adapt AI agents to individual users’ criteria. By integrating cross‑session interaction data into agent context and weights, and building an evolving rubric module, the authors demonstrate that agents can improve task success by 4.5–20.9% after only a few interactions. The evolving rubric also serves as a scalable annotation tool, detecting 16.0–22.3% more failures than language models or humans alone, and personalized agents can even generalize improvements up to 8.8% across users.
arXiv AI
Oct 31
Aligning Autoresearch: Co-Improvement between Agents and Humans
The paper investigates how autoresearch agents—AI systems that conduct research autonomously—benefit from interacting with human researchers, a process called co‑improvement. It presents empirical evidence that humans remain crucial during the ideation phase of benchmark creation, introduces HackBench to expose alignment gaps in current agents, and proposes two training methods: using human simulators to enhance agent interaction and applying reinforcement learning with expert‑aligned rubrics to improve communication. These approaches aim to make autoresearch agents more capable and aligned with human expectations.
arXiv AI
Aug 11
The Scaling Paradox in Human-AI Collaboration
arXiv:2608. 00818v2 Announce Type: replace Abstract: The discovery of scaling laws has highlighted the extraordinary potential of AI systems with a striking empirical pattern: as AI systems scale, their capabilities tend to improve predictably.
arXiv AI
Jun 18
Searching for Synergy in Shared Workspace Human-AI Collaboration
arXiv:2606. 18413v1 Announce Type: new Abstract: Automated AI agents are increasingly capable, yet many scientific and professional tasks require human judgment and contextual expertise.
arXiv AI
Jul 21
AI for Auto-Research: Roadmap & User Guide
arXiv:2605. 18661v2 Announce Type: replace Abstract: AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draft manuscripts, and simulate critique with minimal human input.
arXiv AI
Jun 2
The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace
arXiv:2606. 00182v1 Announce Type: cross Abstract: Human-AI collaboration is considered the most promising way to incorporate AI in the workplace.
arXiv AI
Sep 10
PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI
PersonaTeaming introduces a workflow that incorporates personas into adversarial prompt generation for generative AI, achieving higher attack success rates than the state‑of‑the‑art RainbowPlus while preserving prompt diversity. The system is extended into a user‑facing playground that lets red‑teamers create their own personas and collaborate with AI to refine prompts, fostering diverse strategies. A user study with 11 industry practitioners found the playground produced useful outputs and encouraged out‑of‑the‑box thinking, even when suggestions were not strictly followed.