arXiv AI
Jul 31
Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork
arXiv:2607. 27177v1 Announce Type: new Abstract: Effective collaboration with novel and diverse partners is a crucial skill for autonomous agents.
NestRL: A Nested Training Regime for Mutual Adaptation in Human-AI Teaming
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.
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arXiv AI
Jun 17
Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models
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 AI
Jul 7
Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales
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 AI
Aug 5
Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian Adaptation
arXiv:2403. 16178v2 Announce Type: replace-cross Abstract: For effective human-agent teaming, robots and other artificial intelligence (AI) agents must infer their human partner's abilities and behavioral response patterns and adapt accordingly.
arXiv AI
Aug 10
Evaluating XAI Support From A Hierarchical Reinforcement Learning Policy in Human-Agent Collaboration
arXiv:2608. 06381v1 Announce Type: cross Abstract: Explainable AI (XAI) has shown promise for human-agent collaboration, yet results rely on hand-crafted policies in custom environments, limiting generalizability to state-of-the-art teaming research.
arXiv AI
Jun 10
Human-AI Teaming Through the Lens of Calibration
arXiv:2606. 10906v1 Announce Type: cross Abstract: We study models for human-AI teaming through the lens of statistical calibration.
arXiv AI
Jun 9
Unsupervised Partner Design Enables Robust Ad-hoc Teamwork
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 Machine Learning
Jun 16
Probing Dec-POMDP Reasoning in Cooperative MARL
arXiv:2602. 20804v2 Announce Type: replace Abstract: Cooperative multi-agent reinforcement learning (MARL) is typically framed as a decentralised partially observable Markov decision process (Dec-POMDP), a setting whose hardness stems from two key challenges: partial observability and decentralised coordination.
arXiv AI
Jul 16
Learning to Learn-at-Test-Time: Language Agents with Learnable Adaptation Policies
arXiv:2604. 00830v3 Announce Type: replace-cross Abstract: Test-Time Learning (TTL) enables language agents to iteratively refine their performance through repeated interactions with the environment at inference time.
arXiv AI
Aug 11
The Collaboration Gap: Exploration and Benchmarking of Open-World Agentic Cooperation
arXiv:2511. 02687v2 Announce Type: replace Abstract: The trajectory of AI development suggests that we will increasingly rely on agent-based systems powered by language models, composed of independently developed agents with different information, privileges, and tools.
arXiv Machine Learning
Jun 3
Multi$^2$: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments
arXiv:2606. 03698v1 Announce Type: new Abstract: A central goal of large language model (LLM) research is to build agentic systems that can plan, act, and adapt through sustained interaction with dynamic environments.
arXiv AI
Jun 9
Learning to Attack and Defend: Adaptive Red Teaming of Language Models via GRPO
arXiv:2606. 09701v1 Announce Type: cross Abstract: AI red teaming must continually adapt to evolving attackers and defenders.