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Showing papers from University of Washington, Google DeepMind Show all papers

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NeurIPS 2026Paul G. Allen School of ComputerU WashingtonU Washington, Google DeepMindMulti-agent RL

Why Copy Others? Insights into Social Learning from Multi-Agent Reinforcement Learning

Yancheng Liang, Shakti Senthil, Daphne Chen, Simon Du and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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57%Worth a look
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NeurIPS 2026SpotlightU Washington | GoogleU California, BerkeleyDepartment of Computer Science, U WashingtonU California, San DiegoEmbodied agents

Embedded-Arena: Building Hardware-in-the-Loop Coding Agents to Run AI on Microcontrollers

Zhihan Zhang, Alexander Le Metzger, Jiuyang Lyu, Chun-Cheng Chang and 9 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
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45%Niche pick
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NeurIPS 2026U WashingtonPaul G. Allen School of ComputerU Washington, Google DeepMindHuman-AI interaction

Unifying Partner and Environment Diversity to Improve Human-AI Coordination

Patrick Q Zhang, Yancheng Liang, Adrienne Fairhall, Natasha Jaques

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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83%Must read
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NeurIPS 2026YunnanNational University of SingaporeU Washington, Google DeepMindAdversarial robustness

FlowSteer: Prompt-Only Workflow Steering Exposes Planning-Time Vulnerabilities in Multi-Agent LLM Systems

FlowSteer is a prompt-only attack that steers multi-agent LLM workflows to amplify malicious signals via planning-time vulnerabilities, while FlowGuard defensively reduces success by 34%.

Fanxiao Li, Jiaying Wu, Tingchao Fu, Natasha Jaques and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 2/5