Type
Nonprofit
Country
United States
US
Documents
164
through any edge
Papers
164
authorship edges
Awards made
—
Named as funder
—
Identifiers
Papers
Documents in the 44B Library with Machine Science on the authorship, newest first.
2026-07-25 · 1 authors here
TopologicalGovernor: A Comprehensive Tutorial Solving Catastrophic Forgetting Through Prime-Anchored Protection
2026-07-25 · 1 authors here
TopologicalGovernor: A Comprehensive Tutorial Solving Catastrophic Forgetting Through Prime-Anchored Protection
2026-07-15 · 4 authors here
Parsing Middle High German: exploring cross-lingual NLP for treebank construction in low-resource historical languages
2026-06-26 · 1 authors here
TOPO-2026: A Prime-Based Topological Framework for Ultra-Efficient Continual Learning
2026-06-26 · 1 authors here
TOPO-2026: A Prime-Based Topological Framework for Ultra-Efficient Continual Learning
2026-06-23 · 1 authors here
Arithmetic Spectral Theory and TOPO-2026: A Unified Framework Solving the Riemann Hypothesis, Quantifying the Green-Tao Theorem, and Eliminating Catastrophic Forgetting in Artificial Intelligence
2026-06-23 · 1 authors here
Arithmetic Spectral Theory and TOPO-2026: A Unified Framework Solving the Riemann Hypothesis, Quantifying the Green-Tao Theorem, and Eliminating Catastrophic Forgetting in Artificial Intelligence
2026-06-21 · 1 authors here
TOPO-2026: The First Universal Solution to Catastrophic Forgetting Empirical Validation Across Five Architectures and Three Continents with Unprecedented NaN Stress Testing
2026-06-21 · 1 authors here
TOPO-2026: The First Universal Solution to Catastrophic Forgetting Empirical Validation Across Five Architectures and Three Continents with Unprecedented NaN Stress Testing
2026-06-19 · 1 authors here
TOPO-2026: Universal Continual Learning via Prime-Anchored Embedding Invariants Empirical Validation Across Five Architectures and Formal Proof via Arithmetic Spectral Theory
2026-06-19 · 1 authors here
TOPO-2026: Universal Continual Learning via Prime-Anchored Embedding Invariants Empirical Validation Across Five Architectures and Formal Proof via Arithmetic Spectral Theory
2026-06-16 · 2 authors here
Measuring What Survives: Pressure-Filtered Persistence and a Masking Index for LLM-Agent Safety Evaluation
2026-06-15 · 2 authors here
The Disagreement Law: A Covariance Decomposition of Architectural Disagreement in Molecular Property Prediction and When It Fails
2026-06-15 · 1 authors here
TRUSTWORTHY MACHINE LEARNING IN COMPUTER ENGINEERING: A REVIEW OF ROBUSTNESS, FAIRNESS, AND INTERPRETABILITY
2026-06-15 · 2 authors here
The Disagreement Law: A Covariance Decomposition of Architectural Disagreement in Molecular Property Prediction and When It Fails
2026-06-15 · 1 authors here
TRUSTWORTHY MACHINE LEARNING IN COMPUTER ENGINEERING: A REVIEW OF ROBUSTNESS, FAIRNESS, AND INTERPRETABILITY
2026-05-24 · 1 authors here
Topological AI: Prime-Anchored Neural Networks Solving Catastrophic Forgetting in Large Language Models
2026-05-24 · 1 authors here
Topological AI: Prime-Anchored Neural Networks Solving Catastrophic Forgetting in Large Language Models
2026-05-22 · 1 authors here
Topological AI: Prime-Anchored Neural Networks Solve Catastrophic Forgetting A Complete Empirical Validation on GPT-OSS-20B
2026-05-22 · 1 authors here
Topological AI: Prime-Anchored Neural Networks Solve Catastrophic Forgetting A Complete Empirical Validation on GPT-OSS-20B
2026-05-21 · 1 authors here
DeepSeek Prime-Anchored Spectral Governor: Solving Catastrophic Forgetting in Large Language Models Using the Sieve of Eratosthenes
2026-05-21 · 1 authors here
DeepSeek Prime-Anchored Spectral Governor: Solving Catastrophic Forgetting in Large Language Models Using the Sieve of Eratosthenes
2026-05-20 · 4 authors here
Dissecting Performative Prediction: A Comprehensive Survey
2026-05-18 · 1 authors here
The Riemann Hypothesis, Hilbert-Pólya Conjecture, and H2E Geometric Governance for Safe Autonomous AI
2026-05-18 · 1 authors here
The Riemann Hypothesis, Hilbert-Pólya Conjecture, and H2E Geometric Governance for Safe Autonomous AI
Funding
Read from the funding tables, never from the document count — that count includes authorship, so it ranks institutions, not funders.
Who funds this work
Funders named on the papers above. The record holds no recipient field, so this is reached through the documents themselves — and the two columns are two different claims, kept apart.
Under a named grant
funding_awards — the award carries a grant number
Listed as funder
doc_funder_links — named, with no grant number