Vibe Reasoning: Eliciting Frontier AI Mathematical Capabilities -- A Case Study on IMO 2025 Problem 6
- URL: http://arxiv.org/abs/2512.19287v1
- Date: Mon, 22 Dec 2025 11:30:19 GMT
- Title: Vibe Reasoning: Eliciting Frontier AI Mathematical Capabilities -- A Case Study on IMO 2025 Problem 6
- Authors: Jiaao Wu, Xian Zhang, Fan Yang, Yinpeng Dong,
- Abstract summary: We introduce Vibe Reasoning, a human-AI collaborative paradigm for solving complex mathematical problems.<n>We demonstrate this paradigm through IMO 2025 Problem 6, a optimization problem where autonomous AI systems publicly reported failures.
- Score: 28.84243696489176
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce Vibe Reasoning, a human-AI collaborative paradigm for solving complex mathematical problems. Our key insight is that frontier AI models already possess the knowledge required to solve challenging problems -- they simply do not know how, what, or when to apply it. Vibe Reasoning transforms AI's latent potential into manifested capability through generic meta-prompts, agentic grounding, and model orchestration. We demonstrate this paradigm through IMO 2025 Problem 6, a combinatorial optimization problem where autonomous AI systems publicly reported failures. Our solution combined GPT-5's exploratory capabilities with Gemini 3 Pro's proof strengths, leveraging agentic workflows with Python code execution and file-based memory, to derive both the correct answer (2112) and a rigorous mathematical proof. Through iterative refinement across multiple attempts, we discovered the necessity of agentic grounding and model orchestration, while human prompts evolved from problem-specific hints to generic, transferable meta-prompts. We analyze why capable AI fails autonomously, how each component addresses specific failure modes, and extract principles for effective vibe reasoning. Our findings suggest that lightweight human guidance can unlock frontier models' mathematical reasoning potential. This is ongoing work; we are developing automated frameworks and conducting broader evaluations to further validate Vibe Reasoning's generality and effectiveness.
Related papers
- The AI Research Assistant: Promise, Peril, and a Proof of Concept [0.0]
We provide empirical evidence through a detailed case study.<n>The collaboration revealed both remarkable capabilities and critical limitations.<n>Our experience suggests that, when used with appropriate skepticism and verification protocols, AI tools can meaningfully accelerate mathematical discovery.
arXiv Detail & Related papers (2026-02-26T10:29:05Z) - Towards Autonomous Mathematics Research [48.29504087871558]
We introduce Aletheia, a math research agent that iteratively generates, verifies, and revises solutions end-to-end in natural language.<n>Specifically, Aletheia is powered by an advanced version of Gemini Deep Think for challenging reasoning problems.<n>We demonstrate Aletheia from Olympiad problems to PhD-level exercises and most notably, through several distinct milestones in AI-assisted mathematics research.
arXiv Detail & Related papers (2026-02-10T18:50:15Z) - Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction [26.396483988509956]
We present a large-scale empirical analysis of Human-AI Difficulty Alignment for over 20 models across diverse domains.<n>Our findings reveal a systematic misalignment where scaling up model size is not reliably helpful.<n>We observe that high performance often impedes accurate difficulty estimation, as models struggle to simulate the capability limitations of students.
arXiv Detail & Related papers (2025-12-21T20:41:36Z) - FormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming [19.576944188747166]
FormulaOne is a benchmark for graph theory, logic, and algorithms.<n>Our problems are incredibly demanding, requiring an array of reasoning steps.<n>Remarkably, state-of-the-art models like OpenAI's o3 fail entirely on FormulaOne.
arXiv Detail & Related papers (2025-07-17T17:53:55Z) - AGI Is Coming... Right After AI Learns to Play Wordle [4.2909314120969855]
multimodal agents, in particular, OpenAI's Computer-User Agent (CUA), trained to control and complete tasks through a standard computer interface, similar to humans.<n>We evaluated the agent's performance on the New York Times Wordle game to elicit model behaviors and identify shortcomings.
arXiv Detail & Related papers (2025-04-21T20:58:58Z) - Formal Mathematical Reasoning: A New Frontier in AI [60.26950681543385]
We advocate for formal mathematical reasoning and argue that it is indispensable for advancing AI4Math to the next level.<n>We summarize existing progress, discuss open challenges, and envision critical milestones to measure future success.
arXiv Detail & Related papers (2024-12-20T17:19:24Z) - Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA [43.116608441891096]
Humans outperform AI systems in knowledge-grounded abductive and conceptual reasoning.
State-of-the-art LLMs like GPT-4 and LLaMA show superior performance on targeted information retrieval.
arXiv Detail & Related papers (2024-10-09T03:53:26Z) - Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI [116.8199519880327]
Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial General Intelligence (AGI)<n>In this survey, we give a comprehensive exploration of the latest advancements in Embodied AI.
arXiv Detail & Related papers (2024-07-09T14:14:47Z) - Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision [98.97575836717931]
Current AI alignment methodologies rely on human-provided demonstrations or judgments.<n>This raises a challenging research question: How can we keep improving the systems when their capabilities have surpassed the levels of humans?
arXiv Detail & Related papers (2024-03-14T15:12:38Z) - Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL)
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
arXiv Detail & Related papers (2023-12-22T17:57:57Z) - MacGyver: Are Large Language Models Creative Problem Solvers? [87.70522322728581]
We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting.<n>We create MACGYVER, an automatically generated dataset consisting of over 1,600 real-world problems.<n>We present our collection to both LLMs and humans to compare and contrast their problem-solving abilities.
arXiv Detail & Related papers (2023-11-16T08:52:27Z) - Principle-Driven Self-Alignment of Language Models from Scratch with
Minimal Human Supervision [84.31474052176343]
Recent AI-assistant agents, such as ChatGPT, rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback to align the output with human intentions.
This dependence can significantly constrain the true potential of AI-assistant agents due to the high cost of obtaining human supervision.
We propose a novel approach called SELF-ALIGN, which combines principle-driven reasoning and the generative power of LLMs for the self-alignment of AI agents with minimal human supervision.
arXiv Detail & Related papers (2023-05-04T17:59:28Z) - Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and
Successes in the XAI Program [17.52385105997044]
Deep neural network driven models have surpassed human level performance in benchmark autonomy tasks.
The underlying policies for these agents, however, are not easily interpretable.
This paper discusses the origins of these takeaways, provides amplifying information, and suggestions for future work.
arXiv Detail & Related papers (2021-06-10T05:21:10Z) - Machine Common Sense [77.34726150561087]
Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI)
This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
arXiv Detail & Related papers (2020-06-15T13:59:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.