arXiv AI August 10, 2026 By Mateus Levi Sim\~oes Fernandes, Alberto Sardinha

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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.

By Chen Chen, Zhehuai Chen

llms agents benchmarks

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.

By Tim R. Davidson, Adam Fourney, Saleema Amershi, Robert West, Eric Horvitz, Ece Kamar

llms agents benchmarks

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.

By Siddharth Srikanth, Varun Bhatt, Boshen Zhang, Werner Hager, Charles Michael Lewis, Katia P. Sycara, Aaquib Tabrez, Stefanos Nikolaidis

llms agents reinforcement-learning

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.

By Michal Moshkovitz, Suraj Srinivas, Lesia Semenova, Nave Frost, Cyrus Rashtchian, Valentyn Boreiko, Shichang Zhang, Himabindu Lakkaraju, Cynthia Rudin, Jennifer Wortman Vaughan

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.

By Upasana Biswas, Durgesh Kalwar, Subbarao Kambhampati, Sarath Sreedharan

agents reinforcement-learning benchmarks

arXiv AI

Sep 25

Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork

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.

By Yuheng Jing, Kai Li, Ziwen Zhang, Jiajun Zhang, Zeyao Ma, Jiaxi Yang, Lei Zhang, Zhe Wu, Jinmin He, Junliang Xing, Jian Cheng

llms agents reinforcement-learning efficiency benchmarks

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Related stories

Just A Rather Very Intelligent Spoken Agent

The Collaboration Gap: Exploration and Benchmarking of Open-World Agentic Cooperation

Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models

Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods

NestRL: A Nested Training Regime for Mutual Adaptation in Human-AI Teaming

Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork