arXiv:2602. 17737v2 Announce Type: replace-cross Abstract: Mutual adaptation is a central challenge in human-AI teaming, as humans naturally adjust their strategies in response to an AI agent's behavior.
The paper introduces XPM-WM, a framework that uses learned world models to generate simulated trajectories for Cross-play Minimization (XPM) in Zero-Shot Coordination. By replacing costly Mixed-Play sampling with model-generated data, XPM-WM improves sample efficiency and scales linearly with population size, producing diverse partner agents that match the performance of existing methods. The approach also bootstraps training for subsequent agents and can be extended to other population‑based training techniques.
arXiv:2607. 04972v1 Announce Type: cross Abstract: Deploying robot teams in the real world requires simultaneous adaptation to unseen environments, unknown partners, and varying team sizes, yet existing approaches often address these challenges in isolation under the closed-world assumption of fixed teammates.
arXiv:2606. 10906v1 Announce Type: cross Abstract: We study models for human-AI teaming through the lens of statistical calibration.
The paper introduces ROTATE, a regret-driven open‑ended training framework that jointly improves an Ad Hoc Teamwork (AHT) agent and an adversarial teammate generator. Unlike traditional two‑stage pipelines, ROTATE alternates between enhancing the agent and generating teammates that specifically probe its collaboration weaknesses. Experiments on Overcooked and Level‑Based Foraging show that ROTATE outperforms existing baselines on unseen teammates, setting a new benchmark for robust, generalizable teamwork.
arXiv:2504. 03991v2 Announce Type: replace-cross Abstract: Understanding how humans collaborate and communicate in teams is essential for improving human-agent teaming and AI-assisted decision-making.
arXiv:2609.39727v1 Announce Type: new Abstract: Cooperative language-model agents must coordinate over long horizons and adapt to changing environments and to partners with unfamiliar conventions, ye...
arXiv:2508. 06336v2 Announce Type: replace-cross Abstract: We introduce Unsupervised Partner Design (UPD), a population-free multi-agent reinforcement learning method for robust ad-hoc teamwork.
arXiv:2606. 05793v1 Announce Type: cross Abstract: While LLM-based agents excel at individual tasks, effective collaboration with realistic human partners remains challenging.
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.
The paper introduces ICRL4AHT, a large-scale benchmark for evaluating In-Context Reinforcement Learning (ICRL) in Ad-Hoc Teamwork (AHT) scenarios using Overcooked-V2. It provides a diverse teammate suite, a reproducible pipeline, and evaluates history-conditioned ICRL algorithms such as Algorithm Distillation and Decision-Pretrained Transformer. The results show that these methods often perform worse than random baselines and do not improve with longer horizons, underscoring the difficulty of strategic inference under partial observability in AHT.
UnifiedPlayers is a cooperative framework that jointly adapts planning, execution, and evaluation for tool-integrated reinforcement learning agents. It consists of a Planning Player that generates tasks, an Execution Player that creates multi-turn trajectories with Python tool calls, and an Evaluation Player that builds executable verifiers, all coordinated by role‑specific rewards under GRPO. The approach outperforms prior baselines on mathematical and general reasoning benchmarks and yields a verifier with high adversarial detection accuracy and more discriminative reward signals.