💡 The paper introduces ARIS, an open source research harness that enables autonomous research through adversarial multi agent collaboration. The problem addressed is the reliability of long term research outcomes, particularly in cases where AI generated claims may be unsupported or misreported. The central failure mode in long horizon research workflows is not a visible breakdown but rather a plausible unsupported success, where a long running agent can produce claims with incomplete or misreported evidential support.

To address this problem, ARIS uses a cross model adversarial collaboration approach, where an executor model drives forward progress while a reviewer from a different model family critiques intermediate artifacts and requests revisions. The ARIS architecture consists of three layers: the execution layer, which provides reusable skills and model integrations, the orchestration layer, which coordinates end to end workflows, and the assurance layer, which checks the integrity of experimental claims and ensures that they are supported by evidence.

The assurance layer includes a three stage process for checking claims, as well as a five pass scientific editing pipeline, mathematical proof checks, and visual inspection of rendered PDFs. The system also includes a prototype self improvement loop that records research traces and proposes harness improvements, which are adopted only after reviewer approval.

The contributions of the paper are the introduction of the ARIS research harness, which provides a reliable and autonomous way to conduct research, and the demonstration of its effectiveness in ensuring the integrity of research outcomes. The paper also highlights the importance of adversarial collaboration in ensuring the reliability of AI generated research claims. Overall, the paper presents a significant contribution to the field of autonomous research, providing a framework for ensuring the reliability and integrity of research outcomes.