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| Main Author: | |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2606.01863 |
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| _version_ | 1866917553677271040 |
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| author | Khilar, Snigdha Chandan |
| author_facet | Khilar, Snigdha Chandan |
| contents | Continual learning struggles to balance retaining past knowledge with absorbing new tasks. Stefan-CL elegantly resolves this stability-plasticity dilemma through the physics of melting. It frames consolidated knowledge as a protected "solid" and unused capacity as an adaptable "liquid." As the network learns, this boundary expands, governed by a "latent heat" tuning dial. By mathematically freezing the learned interior, Stefan-CL cuts forgetting to near zero, matching memory-heavy baselines without storing raw data, forging a beautiful, physics-grounded path for AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01863 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Continual Learning as a Multiphase Moving-Boundary Problem Khilar, Snigdha Chandan Machine Learning Mathematical Physics Continual learning struggles to balance retaining past knowledge with absorbing new tasks. Stefan-CL elegantly resolves this stability-plasticity dilemma through the physics of melting. It frames consolidated knowledge as a protected "solid" and unused capacity as an adaptable "liquid." As the network learns, this boundary expands, governed by a "latent heat" tuning dial. By mathematically freezing the learned interior, Stefan-CL cuts forgetting to near zero, matching memory-heavy baselines without storing raw data, forging a beautiful, physics-grounded path for AI. |
| title | Continual Learning as a Multiphase Moving-Boundary Problem |
| topic | Machine Learning Mathematical Physics |
| url | https://arxiv.org/abs/2606.01863 |