arXiv AI

DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow

arXiv AI

Jun 10

T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains

arXiv:2606. 11070v1 Announce Type: cross Abstract: Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems.

By Genta Indra Winata, Amartya Chakraborty, Yuzhen Lin, Swasthi P Rao, Shikhhar Siingh, Houhan Lu, Nadia Bathaee, Sriharsha Hatwar, Paresh Dashore, Anmol Jain, Kshitij Tayal, Xiuzhu Lin, Anirban Das, Sambit Sahu, Shi-Xiong Zhang

llms agents benchmarks

arXiv AI

Jul 13

Embodied Multi-Agent Coordination by Aligning World Models Through Dialogue

arXiv:2605. 12920v3 Announce Type: replace-cross Abstract: Effective collaboration between embodied agents requires more than acting in a shared environment; it demands communication grounded in each agent's evolving understanding of the world.

By Vardhan Dongre, Dilek Hakkani-T\"ur

llms agents robotics benchmarks safety

arXiv AI

Jun 6

Humans' ALMANAC: A Human Collaboration Dataset of Action-Level Mental Model Annotations for Agent Collaboration

arXiv:2606. 06388v1 Announce Type: new Abstract: Recent advances in LLM agents have enabled complex cognitive capabilities, such as multi-step reasoning, planning, and tool use, that increasingly position these agents as human collaborators.

By Jiaju Chen, Yuxuan Lu, Jiayi Su, Chaoran Chen, Songlin Xiao, Zheng Zhang, Yun Wang, Yunyao Li, Jian Zhao, Tongshuang Wu, Toby Jia-Jun Li, Dakuo Wang, Bingsheng Yao

llms agents benchmarks

arXiv AI

Jun 9

Aligned but Not Partner-Specific: Distinguishing How Multimodal LLM Agents Succeed in Reference Games Without Human-Like Conventions

arXiv:2606. 08081v1 Announce Type: cross Abstract: Repeated reference games test whether interlocutors replace their initially long descriptions with shorter, partner-specific conventions grounded in shared interaction history.

By Po-Ya Angela Wang, Chinmaya Mishra, Asl{\i} \"Ozy\"urek, Paula Rubio-Fern\'andez, Esam Ghaleb

llms agents multimodal safety

arXiv AI

Jul 7

AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations

arXiv:2504. 09662v4 Announce Type: replace-cross Abstract: Multi-agent large language model simulations have the potential to model complex human behaviors and interactions.

By Jenny Ma, Riya Sahni, Karthik Sreedhar, Lydia B. Chilton

llms agents safety

arXiv AI

Jun 26

HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualization

arXiv:2606. 26614v1 Announce Type: cross Abstract: Large language model (LLM) agents enable natural language interaction for scientific visualization (SciVis).

By Kuangshi Ai, Patrick Phuoc Do, Chaoli Wang

llms agents robotics

arXiv AI

Jul 7

Evaluating Generative Agents with Actions Grounded in Socially Distributed Task Environments using Incognita

arXiv:2607. 02975v1 Announce Type: new Abstract: Effective agency in social environments depends on when an agent seeks knowledge, when it acts, and whether its actions are justified by acquired information.

By Dan C. Hsu, Luke Lu

agents benchmarks

arXiv AI

Jul 21

Interactive Task Alignment as a POMDP

arXiv:2607. 16412v1 Announce Type: new Abstract: Current benchmarks for language models primarily evaluate execution on fully specified tasks.

By Andy Dai, Zexue He, Zhenyu Zhang, Alex Pentland, Jiaxin Pei

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Sep 1

Lies We Can See: Joint Verbal and Non-Verbal Deception by VLM Agents in Embodied Social Interactions

arXiv:2608.30428v1 Announce Type: cross Abstract: Strategic deception by LLM and VLM agents has emerged as a central AI alignment and safety concern. Social-deduction games (where each player holds a...

By Jaewoo Ahn, Junseo Kim, Hyunseo Kim, Heeseung Yun, Jaehyeon Son, Zsolt Kira, Gunhee Kim

llms agents robotics multimodal benchmarks safety

arXiv Computation and Language

Sep 1

Beyond Surface Alignment: Grounding the Dynamics of Situational Understanding and Generative Control in LLMs

arXiv:2608.29610v1 Announce Type: new Abstract: The current alignment tuning paradigm for Large Language Models (LLMs) prioritizes surface-level behaviors -- fluency, safety, and tonal consistency. W...

By Chenghao Yang

llms agents reinforcement-learning benchmarks safety

arXiv AI

Jun 10

A Sober Look at Agentic Misalignment in Automated Workflows

arXiv:2605. 24197v2 Announce Type: replace Abstract: We study a class of emergent misalignment in multi-agent systems (MAS), with a focus on automated workflows, which we refer to agentic misalignment.

By Wenqian Ye, Bo Yuan, Zhichao Xu, Yijun Tian, Yawei Wang, Henry Kautz, Aidong Zhang

agents safety

arXiv AI

Sep 1

HumanStudy-Bench: Towards AI Agent Design for Participant Simulation

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...

By Xuan Liu, Haoyang Shang, Zizhang Liu, Xinyan Liu, Yunze Xiao, Yiwen Tu, Haojian Jin

llms agents

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