arXiv AI
Jul 2
A Category Theory Account of AI Identity
arXiv:2607. 00220v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are routinely modified after deployment through retraining and changes in their environments.
Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence
arXiv:2606. 01444v1 Announce Type: new Abstract: Scientific discovery is not only answer generation but revision of the representational regime in which evidence, artifacts, operations, and verifiers are typed.
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arXiv AI
Jul 16
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
arXiv:2607. 13608v1 Announce Type: new Abstract: Automatic scientific discovery has long been a goal of computational scholars - a machine that can discover nature's secrets on its own, moving computational systems beyond data-fitting tools toward the generation and refinement of mechanistic models of the universe.
Hugging Face Trending Papers
Jul 15
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
Automatic scientific discovery has long been a goal of computational scholars - a machine that can discover nature's secrets on its own, moving computational systems beyond data-fitting tools toward the generation and refinement of mechanistic models of the universe. Recent advances in symbolic regression (SR) and large-language-model (LLM)-based agents suggest that such systems can recover equations from data, incorporate domain priors, and automate parts of the research workflow.
arXiv AI
Jun 12
A Three-Layer Framework for AI in Scientific Discovery
arXiv:2606. 13566v1 Announce Type: new Abstract: Current discussions of AI in scientific discovery are often dominated by two visible capabilities: search over existing knowledge and execution through optimization, simulation, and automation.
arXiv AI
1d ago
Constitutive Priors for Machine Intelligence: A Legitimacy Theory of the Artificial Physical World
arXiv:2608. 15147v1 Announce Type: new Abstract: Machine intelligence has conquered the symbolic world but stalled at the physical one.
arXiv AI
Jun 2
Civilizational Metamaterials: Engineering Coordination Under Capability Gradients and Structural Turbulence
arXiv:2606. 00235v1 Announce Type: cross Abstract: We argue that governance must transition from a normative discipline to an engineering discipline, and we develop a formal framework, inspired by the physics of metamaterials, to make this transition quantitative and testable.
arXiv AI
Jul 15
From Observation to Insight: Mechanistic World Models and the Quest for Autonomous Discovery
arXiv:2607. 12474v1 Announce Type: new Abstract: Recent advances in foundation models have transformed AI for Science, enabling remarkably accurate predictive performance across domains ranging from protein folding to weather forecasting.
arXiv AI
Jun 15
Under What Conditions Can a Machine Be Called Genuinely Creative?
arXiv:2606. 13196v2 Announce Type: replace Abstract: Recent AI systems can generate texts, software architectures, hypotheses, designs, and scientific workflows that appear creative.
arXiv AI
Jun 9
Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery
arXiv:2606. 08728v1 Announce Type: new Abstract: Mathematical reasoning has long served as a stringent test of machine intelligence; over the past decade, it has moved from a niche problem within NLP to one of the most consequential AI frontiers.
arXiv AI
Jun 12
Under What Conditions Can a Machine Become Genuinely Creative?
arXiv:2606. 13196v1 Announce Type: new Abstract: Recent AI systems can generate texts, software architectures, hypotheses, designs, and scientific workflows that appear creative.
arXiv AI
Jun 2
Science Earth: Towards A Planet-Scale Operating System for AI-Native Scientific Discovery
arXiv:2606. 01316v1 Announce Type: new Abstract: Scientific discovery demands intelligence, perseverance, and serendipity across vast search spaces.
arXiv AI
Jul 24
Knowledge Graph Re-engineering Along the Ontological Continuum (extended version)
arXiv:2605. 22093v3 Announce Type: replace Abstract: Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG modelling practices, from lightweight vocabularies to richly axiomatised ontologies, makes integration and reuse expensive and brittle.