u/Griffith-07

Hey everyone,

Standard transformers operate strictly in flat Euclidean space with continuous activations. While effective, this creates challenges when modeling hierarchical tree-structured data and incurs significant energy overhead.

I built Hyper-transformer to explore a hybrid architecture integrating non-Euclidean geometry and neuromorphic spiking dynamics directly into transformer layers.

Key Technical Details:

• Hyperbolic Space Representation: Uses Poincaré and Lorentz manifold projections to capture hierarchical relationships with lower embedding dimensions.

• Spiking Neural Dynamics: Employs Leaky Integrate-and-Fire (LIF) spiking neurons for sparse, event-driven activation states.

• Hybrid Attention: Combines Riemannian distance metrics with attention mechanisms to handle mixed-geometry representations.

• Clean, modular PyTorch implementation designed for ablation studies.

I will drop the full GitHub repository link in the comments below! Would love to hear thoughts from anyone experimenting with non-Euclidean deep learning, manifold learning, or SNNs.

repo link : https://github.com/Griffith-7/Hyper-transformer.git

* Hey everyone, I wanted to share a research implementation I built focusing on non-generative, latent-space language modeling: **H-JEPA-LM**. Instead of predicting tokens autoregressively in surface vocabulary space, H-JEPA-LM leverages a Joint-Embedding Predictive Architecture (JEPA) to predict abstract representations directly in latent space. **Key Features:** • **Latent-Space Prediction:** Focuses on semantic representation learning rather than token generation. • **Hierarchical Abstraction:** Multi-level latent dynamics for both short-term syntax and long-term planning. • **Action-Conditioned Rollouts:** Supports world-model-style latent planning for downstream tasks. • Modular PyTorch implementation designed for experimentation. *I will drop the full GitHub repository link in the comments section below! I'd love to hear thoughts from anyone experimenting with JEPA, latent-space models, or world models.*

repo link : [https://github.com/Griffith-7/H-JEPA-LM.git\](https://github.com/Griffith-7/H-JEPA-LM.git)

• Hey everyone, I wanted to share a research implementation I built focusing on non-generative, latent-space language modeling: H-JEPA-LM. Instead of predicting tokens autoregressively in surface vocabulary space, H-JEPA-LM leverages a Joint-Embedding Predictive Architecture (JEPA) to predict abstract representations directly in latent space. Key Features: • Latent-Space Prediction: Focuses on semantic representation learning rather than token generation. • Hierarchical Abstraction: Multi-level latent dynamics for both short-term syntax and long-term planning. • Action-Conditioned Rollouts: Supports world-model-style latent planning for downstream tasks. • Modular PyTorch implementation designed for experimentation. I will drop the full GitHub repository link in the comments section below! I'd love to hear thoughts from anyone experimenting with JEPA, latent-space models, or world models.

repo link : https://github.com/Griffith-7/H-JEPA-LM.git

• Hey everyone, I wanted to share a project I've been developing focused on hardware-level AI acceleration: a high-performance C++20 simulator for Optical Neural Networks (ONNs).
• Optical computing executes matrix multiplication using light interference through Mach-Zehnder Interferometer (MZI) meshes, offering extreme speed and efficiency, but simulating physical hardware constraints is critical before chip fabrication.
• Key Features: • MZI Mesh Architectures: Simulates Clements and Reck designs for unitary matrix transformations. • SVD Weight Mapping: Decomposes weight matrices into optical phase shifts. • Physical Error Modeling: Simulates thermal phase drift, laser power fluctuations, and photodetector shot noise. • Modern C++20 design leveraging Eigen3 for linear algebra.
• I will drop the full GitHub repository link in the comments section below! I'd love to hear your thoughts on the numerical modeling approach or C++ optimization techniques.

repo link : https://github.com/Griffith-7/photonic-computing-simulation.git

Hey everyone,

I wanted to share a project I've been developing focused on hardware-level AI acceleration: a high-performance C++20 simulator for Optical Neural Networks (ONNs).

Optical computing executes matrix multiplication using light interference through Mach-Zehnder Interferometer (MZI) meshes, offering extreme speed and efficiency, but simulating physical hardware constraints is critical before chip fabrication.

Key Features: • MZI Mesh Architectures: Simulates Clements and Reck designs for unitary matrix transformations. • SVD Weight Mapping: Decomposes weight matrices into optical phase shifts. • Physical Error Modeling: Simulates thermal phase drift, laser power fluctuations, and photodetector shot noise. • Modern C++20 design leveraging Eigen3 for linear algebra.

I will drop the full GitHub repository link in the comments section below! I'd love to hear your thoughts on the numerical modeling approach or C++ optimization techniques.

repo link : https://github.com/Griffith-7/photonic-computing-simulation.git

Hey everyone,

RLHF alignment using standard Proximal Policy Optimization (PPO) can be notoriously tricky to stabilize during LLM post-training due to policy collapse and high sensitivity to hyperparameters.

I built Tpo-torch to explore Target Policy Optimization (TPO) as a cleaner, more stable alternative for preference alignment directly in PyTorch.

Key Focus Areas:

• Mitigating policy collapse without requiring aggressive KL-divergence penalties.

• Modular, lightweight, and readable implementation designed for research and custom fine-tuning pipelines.

• Integrated stability benchmarks comparing policy drift against standard PPO.

I'll drop the GitHub repository link in the comments below! I'd love to hear feedback from anyone experimenting with alignment, preference optimization, or RLHF.

Repo link : https://github.com/Griffith-7/Tpo-torch.git

Hey everyone,

RLHF alignment using standard Proximal Policy Optimization (PPO) can be notoriously tricky to stabilize during LLM post-training due to policy collapse and high sensitivity to hyperparameters.

I built Tpo-torch to explore Target Policy Optimization (TPO) as a cleaner, more stable alternative for preference alignment directly in PyTorch.

Key Focus Areas:

• Mitigating policy collapse without requiring aggressive KL-divergence penalties.

• Modular, lightweight, and readable implementation designed for research and custom fine-tuning pipelines.

• Integrated stability benchmarks comparing policy drift against standard PPO.

I'll drop the GitHub repository link in the comments below! I'd love to hear feedback from anyone experimenting with alignment, preference optimization, or RLHF.

Repo link : [https://github.com/Griffith-7/Tpo-torch.git\](https://github.com/Griffith-7/Tpo-torch.git)

Hey everyone,

RLHF alignment using standard Proximal Policy Optimization (PPO) can be notoriously tricky to stabilize during LLM post-training due to policy collapse and high sensitivity to hyperparameters.

I built Tpo-torch to explore Target Policy Optimization (TPO) as a cleaner, more stable alternative for preference alignment directly in PyTorch.

Key Focus Areas:

• Mitigating policy collapse without requiring aggressive KL-divergence penalties.

• Modular, lightweight, and readable implementation designed for research and custom fine-tuning pipelines.

• Integrated stability benchmarks comparing policy drift against standard PPO.

I'll drop the GitHub repository link in the comments below! I'd love to hear feedback from anyone experimenting with alignment, preference optimization, or RLHF.

Repo link : https://github.com/Griffith-7/Tpo-torch.git