Update README.md

README.md

@@ -16,78 +16,4 @@ Neural Imprint lets a model learn from local experience while base weights stay
 
 🌐 [atomgradient.com](https://atomgradient.com)
 
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-## Research
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-### [Prism — Cross-Domain Personal Data Integration on Consumer Hardware](https://atomgradient.github.io/Prism/)
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-Integrating finance, diet, mood, and reading data entirely on consumer Apple Silicon, producing emergent cross-domain insights with zero data leakage.
-
-- 📈 **1.48x** cross-domain insight emergence (IIR)
-- 🔒 **125.5x** federation compression, zero data leakage
-- ⚡ **49.9 TPS** real-time inference (35B on M2 Ultra)
-
-[[GitHub]](https://github.com/AtomGradient/Prism) · [[Paper]](https://atomgradient.github.io/Prism/)
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-
-### [ANE Batch Prefill — On-Device Parallel LLM Inference](https://atomgradient.github.io/hybird-batch-prefill-on-ane/)
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-Fused matrix-vector kernels enabling concurrent ANE batch prefill + GPU decode on Apple Silicon for Qwen3.5 models.
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-- 🚀 **11.3x** ANE batch prefill speedup (268 tok/s)
-- 🔋 **79%** power reduction for prefill component
-- ⏱️ **<30 ms** state transfer overhead
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-[[GitHub]](https://github.com/AtomGradient/hybird-batch-prefill-on-ane) · [[Paper]](https://atomgradient.github.io/hybird-batch-prefill-on-ane/)
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-### [hybrid-ane-mlx-bench — Disaggregated LLM Inference on Apple Silicon](https://atomgradient.github.io/hybrid-ane-mlx-bench/)
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-Benchmarking CoreML ANE prefill + MLX GPU decode for Qwen3.5 on Apple Silicon, with four inference strategies compared.
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-- 🔄 ANE prefill matches GPU at **~410 tokens**
-- 🔋 **282x** GPU power reduction during prefill
-- 📊 4 inference pipelines benchmarked
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-[[GitHub]](https://github.com/AtomGradient/hybrid-ane-mlx-bench) · [[Paper]](https://atomgradient.github.io/hybrid-ane-mlx-bench/)
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-### [swift-qwen3-tts — On-Device Text-to-Speech](https://atomgradient.github.io/swift-qwen3-tts/)
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-Native Swift implementation of Qwen3 TTS 0.6B for real-time, on-device speech synthesis.
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-- 📦 **67%** model compression (2.35 GB → 808 MB)
-- 🎙️ Real-time synthesis (**RTF 0.68x**)
-- 🌍 12 languages supported
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-[[GitHub]](https://github.com/AtomGradient/swift-qwen3-tts) · [[Paper]](https://atomgradient.github.io/swift-qwen3-tts/)
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-### [Gemma-Prune — On-Device Vision Language Model](https://atomgradient.github.io/swift-gemma-cli/)
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-Multi-stage compression pipeline for deploying Gemma 3 4B VLM on consumer hardware.
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-- 📦 **25%** model compression (2.8 GB → 2.1 GB)
-- 📝 **110 tok/s** text generation
-- 🖼️ **3.4x** image processing speedup
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-[[GitHub]](https://github.com/AtomGradient/swift-gemma-cli) · [[Paper]](https://atomgradient.github.io/swift-gemma-cli/)
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-### [OptMLX — MLX Memory Optimization Research](https://atomgradient.github.io/OptMLX/)
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-Exploring memory optimization techniques for the MLX framework on Apple Silicon.
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-- ⚡ Up to **20x** faster mmap loading
-- 🔄 Zero-copy model loading
-- 📊 Comprehensive benchmarks
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-[[GitHub]](https://github.com/AtomGradient/OptMLX) · [[Paper]](https://atomgradient.github.io/OptMLX/)
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