ARIS: Autonomous ML Research via Adversarial Multi-Agent Collaboration
Shanghai Jiao Tong University
ARIS is an open-source research harness for autonomous ML research addressing 'plausible unsupported success' — where long-running agent claims lack proper evidential grounding. The system pairs an executor model with a reviewer from a different model family (adversarial cross-model collaboration) and adds a three-stage assurance layer: integrity check, results-to-claims mapping, and manuscript audit against raw evidence. 65+ reusable research skills cover the full experiment lifecycle.
Why it matters
Adversarial cross-model collaboration for quality control addresses the core reliability concern of long-horizon LLM research agents; 8k+ GitHub stars and 99 HF upvotes signal strong community traction.
Importance: 3/5
99 HF trending upvotes; 8k+ GitHub stars; adversarial multi-model quality assurance for autonomous research addresses the plausible-but-unsupported-success failure mode.