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Research PaperResearchia:202610.01051

Cogentic: Multi-Agent Orchestration for Automated Proof Discovery

Yang Cai

Abstract

We present Cogentic, a multi-agent harness for automated proof discovery on open research problems. While frontier language models can generate strong mathematical ideas in a single shot, single-shot generation is often insufficient for open problems that require exploring multiple competing conjectures, overcoming subtle technical obstructions, and retaining intermediate progress over a long horizon. Cogentic addresses these challenges through an iterative prove--verify loop in which an orchest...

Submitted: October 1, 2026Subjects: AI; Artificial Intelligence

Description / Details

We present Cogentic, a multi-agent harness for automated proof discovery on open research problems. While frontier language models can generate strong mathematical ideas in a single shot, single-shot generation is often insufficient for open problems that require exploring multiple competing conjectures, overcoming subtle technical obstructions, and retaining intermediate progress over a long horizon. Cogentic addresses these challenges through an iterative prove--verify loop in which an orchestrator allocates a population of independent provers across distinct proof directions, subjects their output to adversarial verification by several specialized components, and promotes confirmed intermediate results into a persistent verified ledger that later rounds build on. The harness is designed to be able to solve research-level math and theoretical computer science problems. Using Gemini as the base model, Cogentic produced novel results on five open problems across online learning, auction theory, and mechanism design. Each result was independently verified by domain experts and is developed in full in companion papers. We list these results, and new ones as they are verified, at https://sites.google.com/view/cogentic .


Source: arXiv:2609.40324v1 - http://arxiv.org/abs/2609.40324v1 PDF: https://arxiv.org/pdf/2609.40324v1 Original Link: http://arxiv.org/abs/2609.40324v1

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Submission Info
Date:
Oct 1, 2026
Topic:
Artificial Intelligence
Area:
AI
Comments:
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