Skip to content
Log in
Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

A. Emre Cetin

View on Semantic Scholar

4 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#edge computing Open access Sep 2026

Accelerating Neural Signed Distance Field Ray Marching via Idempotent Curvature Contraction and In-Situ Empty Space Culling

Neural Signed Distance Fields (Neural SDFs) provide compact, continuous, and topologically flexible geometric representations for inverse rendering, generative 3D modeling, and scientific visualization. However, real-time ray casting through neural implicit volumes remains severely constrained by computational cost: un...

A. Emre Cetin · 0 citations
#edge computing Open access Sep 2026

Accelerating Neural Signed Distance Field Ray Marching via Idempotent Curvature Contraction and In-Situ Empty Space Culling

Neural Signed Distance Fields (Neural SDFs) provide compact, continuous, and topologically flexible geometric representations for inverse rendering, generative 3D modeling, and scientific visualization. However, real-time ray casting through neural implicit volumes remains severely constrained by computational cost: un...

A. Emre Cetin · 0 citations
#large language models Open access Sep 2026

Zero-Copy In-Place Compaction and Idempotent Associative Routing of Dynamic Key-Value Cache Tensors in Deep Learning Accelerators

The serving of autoregressive Large Language Models (LLMs) is severely constrained by the computational and memory capacity limits of the Key-Value (KV) cache, commonly referred to as the Memory Wall. While dynamic context pruning algorithms mitigate memory expansion by discarding tokens of low attention mass, conventi...

A. Emre Cetin · 0 citations
#large language models Open access Sep 2026

Zero-Copy In-Place Compaction and Idempotent Associative Routing of Dynamic Key-Value Cache Tensors in Deep Learning Accelerators

The serving of autoregressive Large Language Models (LLMs) is severely constrained by the computational and memory capacity limits of the Key-Value (KV) cache, commonly referred to as the Memory Wall. While dynamic context pruning algorithms mitigate memory expansion by discarding tokens of low attention mass, conventi...

A. Emre Cetin · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.