arXiv AI
Jul 21
Just A Rather Very Intelligent Spoken Agent
arXiv:2607. 16610v1 Announce Type: new Abstract: Long-horizon AI agents are becoming increasingly capable, yet their interaction with users remains surprisingly thin.
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.
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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 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 17
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
arXiv:2607. 14123v1 Announce Type: cross Abstract: Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows.
arXiv Machine Learning
Jun 2
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.
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
Aug 7
Unified Agent: Managing Interactions across Devices
arXiv:2608. 05729v1 Announce Type: new Abstract: As capabilities rapidly increase, AI agents can move from running inside one app to acting across a user's devices over time.
arXiv Machine Learning
Jun 16
LLM-Based Synthetic Ground Truth Generation for Audio-Based Emotion Classification via In-Context Learning
arXiv:2606. 14784v1 Announce Type: cross Abstract: Understanding human states and interaction dynamics is a core goal of human-computer interaction (HCI).
Hugging Face Trending Papers
Jul 4
ProACT: Towards Breakdown-Aware Proactive Agent in Multi-User Collaboration
Conversational agents are increasingly embedded in human collaborative work, yet they remain fundamentally passive and reactive: they respond to explicit user requests rather than proactively recognizing moments when a team would benefit from timely intervention as human collaborators often do. This reactive design substantially limits the use of agents as active participants in multi-user collaboration, where disagreements, ambiguous goals, forgotten constraints, underspecified plans, discussion loops, and imbalanced participation can gradually undermine group progress.
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
Jun 18
Self-Evolving Multi-Agent Systems via Textual Backpropagation
arXiv:2506. 09046v3 Announce Type: replace-cross Abstract: Leveraging multiple Large Language Models (LLMs) has proven effective for addressing complex, high-dimensional tasks, but current approaches often rely on static, manually engineered multi-agent configurations.
arXiv AI
Jun 26
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
arXiv:2606. 27136v1 Announce Type: new Abstract: For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience.