arXiv:2606.14108v1 Announce Type: cross Abstract: We introduce Adelic operation-preserved embeddings (AOE), a training-free representation that captures both a number's real value and its modular (p-adic) signatures. This construction preserves additive and multiplicative structure by design, turning numerical input into embeddings that "speak in the language of mathematics." Unlike prior approaches that rely on task-specific retraining, AOE is plug-and-play and drops seamlessly into existing architectures. On algebraic combinatorics benchmarks, it delivers consistent gains including the first
The continuous push for more efficient and mathematically grounded AI representations drives innovation in foundational machine learning research.
This breakthrough could significantly improve AI's ability to reason with numerical data, unlocking new applications in scientific computing and complex decision-making.
AI models can now seamlessly incorporate numerical values with inherent mathematical properties without extensive bespoke training, accelerating development and deployment.
- · AI researchers
- · Scientific computing
- · Financial modeling platforms
- · Deep learning framework developers
- · Prior task-specific numerical embedding solutions
- · Architectures reliant on complex numerical preprocessing
Easier integration of mathematical reasoning into AI systems across various domains.
Accelerated discovery in fields like physics, chemistry, and materials science through more intelligent data analysis.
Enhanced AI agents capable of higher-level mathematical problem-solving, potentially leading to fully autonomous scientific discovery.
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Read at arXiv cs.AI