ARIS: Autonomous Research via Adversarial Multi-Agent Collaboration
From Daily Paper Cast by Jingwen Liang, Gengyu Wang
May 7, 2026 · 24 min · Episode 1836
About this episode
This episode discusses ARIS, an open-source research harness for autonomous research and its architectural features.
🤗 Upvotes: 75 | cs.SE, cs.AI Authors: Ruofeng Yang, Yongcan Li, Shuai Li Title: ARIS: Autonomous Research via Adversarial Multi-Agent Collaboration Arxiv: http://arxiv.org/abs/2605.03042v1 Abstract: This report describes ARIS (Auto-Research-in-sleep), an open-source research harness for autonomous research, including its architecture, assurance mechanisms, and early deployment experience. The performance of agent systems built on LLMs depends on both the model weights and the harness around them, which governs what information to store, retrieve, and present to the model. For long-horizon research workflows, the central failure mode is not a visible breakdown but a plausible unsupported success: a long-running agent can produce claims whose evidential support is incomplete, misreported, or silently inherited from the executor's framing. Therefore, we present ARIS as a research harness that coordinates machine-learning research workflows through cross-model adversarial collaboration as a default configuration: an executor model drives forward progress while a reviewer from a different model family is recommended to critique intermediate artifacts and request revisions. ARIS has…
People in this episode
Hosts: Jingwen Liang, Gengyu Wang
Topics covered
- autonomous research
- multi-agent collaboration
- machine learning
- adversarial systems
- research workflows
Keywords
- ARIS
- autonomous research
- adversarial collaboration
- machine learning
- research workflows
Mentioned in this episode
Organizations: ARIS, LLMs
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