In the news
SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resolution 3D Generation
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- SILSA generates high-resolution 3D shapes using compact sliding-window slice latents instead of expensive voxel tokens, preserving topology through persistence diagram supervision.
- Why it matters
- Matters for engineers building 3D generative models who need faster inference, lower memory use, and better structural fidelity for complex shapes.
- Watch out
- Paper is recent preprint accepted at NeurIPS 2026; real-world performance on diverse shape categories and comparison to production systems remains to be validated.
- rag
- serving
- token
The patterns behind this
- Sliding Window Management
- Agentic Context Engineering (Evolving Playbook)
- Agent Context Preservation and Recovery
Each one covers how the technique works, when it earns its cost, and where it breaks.
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