Pairit is an online platform that enables researchers to design, test, and deploy live experiments on human-AI collaboration. Using a single YAML configuration file, users can specify an executable experiment graph—including pages, routing, randomization, matchmaking, chat, shared workspaces, server-hosted agents, surveys, timers, and custom HTML components—and combine any number of humans and AI agents in real-time sessions. The platform has been validated through multiple live deployments, including peer-reviewed studies, and captures high-resolution process traces of communication, negotiation, and collaborative work in human-AI dyads.
arXiv:2607. 12180v1 Announce Type: cross Abstract: An AI teammate's design properties (personality, communication style, when it speaks) can shape a team's trust, coordination, and decisions.
arXiv:2607. 20773v1 Announce Type: cross Abstract: Large language models (LLMs) have shifted human--computer interaction from `traditional'' interface journeys toward more conversational exchanges.
Recent advances in persistent personal-agent frameworks are making human-centered agent networks realistic deployment targets: each user can be served by an AI agent that acts on the user's behalf, maintains state, and communicates with other agents through social and task relations. In these networks, everyday tool use becomes multi-party owned-agent collaboration over personal workspaces, where files, records, tools, and policies are not directly visible across owners.
arXiv:2608. 03499v1 Announce Type: new Abstract: Recent advances in persistent personal-agent frameworks are making human-centered agent networks realistic deployment targets: each user can be served by an AI agent that acts on the user's behalf, maintains state, and communicates with other agents through social and task relations.
arXiv:2602.00685v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as simulated participants in social science experiments, but their behavior is often unstable an...
arXiv:2606. 08200v1 Announce Type: new Abstract: Evaluating LLM-powered interactive social agents is challenging because socially relevant behaviors depend not only on isolated outputs, but also on prior interactions, social roles, and downstream actions.
ChatDev 2.0, also called DevAll, is a no-code platform that lets users build, run, and inspect heterogeneous multi‑agent systems (MAS) powered by large language models. It combines a declarative executable graph abstraction with a cycle‑aware execution engine, enabling representation and execution of dynamic, cyclic interactions among diverse agents. The integrated visual interface allows users to author, monitor, and inspect MAS—including human‑in‑the‑loop steps—without writing code, and experiments show it matches state‑of‑the‑art MAS performance across three tasks.
arXiv:2606. 09751v1 Announce Type: new Abstract: Foundation models are moving from response generation into operational roles.
MOSAIC is an open‑source platform that allows agents from different decision‑making paradigms—such as reinforcement learning policies, large language models, vision‑language models, and human operators—to operate together in shared reinforcement learning environments. It achieves this through an IPC‑based worker protocol that isolates each agent’s training and inference logic, an operator abstraction that maps any agent to a minimal universal interface, and a deterministic evaluation framework offering both manual lock‑step and automated script modes for reproducible experiments.
arXiv:2606. 30246v1 Announce Type: new Abstract: Existing autonomous research agents can support parts of the research process, but most systems still treat research as either an isolated assistant task or a closed workflow.
arXiv:2606. 13722v1 Announce Type: new Abstract: This paper introduces YeasierAgent, an application-building paradigm based on symbiotic agents, narrative worlds, and scene-aware interaction.
The paper reports an in‑situ qualitative study of a persistent, proactive AI teammate deployed across multiple teams in a large technology company. It finds that the human‑agent workplace is in flux, with breakdowns and negotiations emerging around tacit workflow rules, the relational boundaries of the non‑human actor, and the redistribution of trust and human agency. These micro‑negotiations are used to propose a new research, design, and organizational agenda that seeks to preserve human agency when sharing workspaces with non‑human actors.