Image de couverture de livre

Do Two AI Scientists Agree?

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Fu, Xinghong, Liu, Ziming, Tegmark, Max
Format:	Preprint
Publié:	2025
Sujets:	Artificial Intelligence Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

How Do Transformers "Do" Physics? Investigating the Simple Harmonic Oscillator
par: Kantamneni, Subhash, et autres
Publié: (2024)

A Neural Scaling Law from Lottery Ticket Ensembling
par: Liu, Ziming, et autres
Publié: (2023)

Language Models Use Trigonometry to Do Addition
par: Kantamneni, Subhash, et autres
Publié: (2025)

A Resource Model For Neural Scaling Law
par: Song, Jinyeop, et autres
Publié: (2024)

Neural Thermodynamic Laws for Large Language Model Training
par: Liu, Ziming, et autres
Publié: (2025)

Language Models Represent Space and Time
par: Gurnee, Wes, et autres
Publié: (2023)

Physics of Skill Learning
par: Liu, Ziming, et autres
Publié: (2025)

Investigating Representation Universality: Case Study on Genealogical Representations
par: Baek, David D., et autres
Publié: (2024)

KAN 2.0: Kolmogorov-Arnold Networks Meet Science
par: Liu, Ziming, et autres
Publié: (2024)

OptPDE: Discovering Novel Integrable Systems via AI-Human Collaboration
par: Kantamneni, Subhash, et autres
Publié: (2024)

Are Sparse Autoencoders Useful? A Case Study in Sparse Probing
par: Kantamneni, Subhash, et autres
Publié: (2025)

Towards a Medical AI Scientist
par: Wu, Hongtao, et autres
Publié: (2026)

Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting
par: Fu, Xinghong, et autres
Publié: (2026)

The AI Data Scientist
par: Akimov, Farkhad, et autres
Publié: (2025)

Scaling Laws For Scalable Oversight
par: Engels, Joshua, et autres
Publié: (2025)

The Remarkable Robustness of LLMs: Stages of Inference?
par: Lad, Vedang, et autres
Publié: (2024)

Adapt, Agree, Aggregate: Semi-Supervised Ensemble Labeling for Graph Convolutional Networks
par: Abdolali, Maryam, et autres
Publié: (2025)

KAN: Kolmogorov-Arnold Networks
par: Liu, Ziming, et autres
Publié: (2024)

Harmonic Loss Trains Interpretable AI Models
par: Baek, David D., et autres
Publié: (2025)

MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data
par: Kang, Mingu, et autres
Publié: (2024)

Exploring Nutritional Impact on Alzheimer's Mortality: An Explainable AI Approach
par: Liu, Ziming, et autres
Publié: (2024)

GenEFT: Understanding Statics and Dynamics of Model Generalization via Effective Theory
par: Baek, David D., et autres
Publié: (2024)

AI Scientists Fail Without Strong Implementation Capability
par: Zhu, Minjun, et autres
Publié: (2025)

Are Your Reasoning Models Reasoning or Guessing? A Mechanistic Analysis of Hierarchical Reasoning Models
par: Ren, Zirui, et autres
Publié: (2026)

Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
par: Bengio, Yoshua, et autres
Publié: (2025)

EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs
par: Bensmail, Chama
Publié: (2025)

AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists
par: Pan, Junshu, et autres
Publié: (2026)

Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
par: Liu, Haoyang, et autres
Publié: (2024)

Do Diffusion Models Learn Semantically Meaningful and Efficient Representations?
par: Liang, Qiyao, et autres
Publié: (2024)

Dense SAE Latents Are Features, Not Bugs
par: Sun, Xiaoqing, et autres
Publié: (2025)

The Geometry of Concepts: Sparse Autoencoder Feature Structure
par: Li, Yuxiao, et autres
Publié: (2024)

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
par: Lu, Chris, et autres
Publié: (2024)

Do Transaction-Level and Actor-Level AML Queues Agree? An Empirical Evaluation of Granularity Effects on the Elliptic++ Graph
par: Malik, Ankur
Publié: (2026)

Questionnaire Responses Do not Capture the Safety of AI Agents
par: Hellrigel-Holderbaum, Max, et autres
Publié: (2026)

Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy
par: Tang, Xiangru, et autres
Publié: (2024)

SE(3)-bi-equivariant Transformers for Point Cloud Assembly
par: Wang, Ziming, et autres
Publié: (2024)

Evaluating Sakana's AI Scientist: Bold Claims, Mixed Results, and a Promising Future?
par: Beel, Joeran, et autres
Publié: (2025)

Universal One-third Time Scaling in Learning Peaked Distributions
par: Liu, Yizhou, et autres
Publié: (2026)

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems
par: Geng, Jiayi, et autres
Publié: (2025)

Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows
par: Xu, Wanghan, et autres
Publié: (2025)