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README.md

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-<img src="https://huggingface.co/spaces/Osakra/README/resolve/main/osakra_research_logo.png" width="360" alt="Osakra Research Logo" />
+<img src="https://huggingface.co/spaces/Osakra/README/resolve/main/osakra_research_logo.png" width="380" alt="Osakra Research Logo" />
 
 # Osakra Research
-**Independent Computational Cognition & Latent Dynamics**
+**Independent Computational Cognition, Continuous Dynamics & Relational Neuro-Symbolic Architectures**
 
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
-[![Target VRAM](https://img.shields.io/badge/Compute-Local%20%3C%204.5%20GB%20VRAM-informational.svg)](https://huggingface.co/osakra-research/norn-v15)
+[![Compute Target](https://img.shields.io/badge/Compute-Consumer%20Hardware%20%28%3C%205.8%20GB%20VRAM%29-informational.svg)](https://huggingface.co/Osakra)
+[![Architecture](https://img.shields.io/badge/Architecture-N.O.R.N.%20Framework-00c980.svg)](https://huggingface.co/Osakra)
+[![Evaluation Suite](https://img.shields.io/badge/Benchmark%20Suite-1%2C250%20Tasks%20Bundled-orange.svg)](https://huggingface.co/Osakra)
 
 </div>
 
 ---
 
-### About the Project
+### About Osakra Research
 
-Osakra Research is an independent, solo research effort exploring continuous-time dynamical systems, latent recurrence, and vector-symbolic architectures in compact language models[cite: 6, 8]. 
+Osakra Research is an independent computational research effort exploring continuous-time dynamical systems, latent representation manifolds, and vector-symbolic architectures in compact language models.
 
-Rather than training frontier-scale foundation models on massive clusters, this project investigates targeted architectural questions under strict local hardware constraints: can compact open backbones deliberate directly within continuous hidden vector space ($\mathbf{h} \in \mathbb{R}^d$) on a single consumer laptop GPU (< 4.5 GB VRAM)[cite: 6, 8]?
+Rather than scaling parameter counts across massive distributed clusters, this project investigates targeted architectural questions under strict consumer hardware constraints (< 5.8 GB active VRAM on a single consumer GPU): can compact open backbones deliberate directly within continuous hidden vector manifolds ($\mathbf{h} \in \mathbb{R}^d$) and maintain algebraic invariants without relying solely on verbose textual token emission?
+
+This line of inquiry forms the basis of the **N.O.R.N.** architecture (**N**euro-symbolic **O**DE & **R**elational **N**etworks), a dual-tier framework pairing discrete autoregressive deliberation with continuous associative memory engines.
 
 ---
 
-### Research Scope & Limitations
+### Core Research Areas
+
+* **Relational Graph Holographic Reduced Representations (HRR):**
+  Binding topological graphs, causal transition triples, and algebraic structures directly in a 1024-dimensional continuous vector space using complex Fourier-domain circular convolutions ($\mathcal{F}^{-1}(\mathcal{F}(\mathbf{a}) \odot \mathcal{F}(\mathbf{b}))$), mitigating representation drift and attention dispersal across long-horizon deduction.
+
+* **Continuous-Time Dynamical Modulation (LTC-ODEs):**
+  Utilizing Liquid Time-Constant (LTC) ordinary differential equations and leaky integrators to govern continuous latent state evolution between discrete token transitions.
 
-* **Continuous Latent Deliberation:** Investigating whether variable-depth stochastic recurrence ($k \in [2, 16]$) and attractor regularization ($\mathcal{L}_{\text{drift}}$) can stabilize continuous latent Chain-of-Thought reasoning without emitting verbose textual tokens[cite: 6, 8].
-* **Dynamical Systems Modulation:** Experimenting with continuous-time Liquid Time-Constant (LTC) neural ODEs and Holographic Reduced Representations (HRR) for associative memory binding[cite: 5, 6, 8].
-* **Grounded Scope:** This is exploratory, single-author research[cite: 6, 8]. These models do not compete with 70B+ flagship models on broad open-domain knowledge[cite: 6, 8]. The goal is to document empirical findings, trade-offs, and failure modes when compute is spent in latent space rather than token space[cite: 6, 8].
+* **Deterministic Agentic Tool Evaluation:**
+  Auditing model capabilities on live subprocess code execution (the 250-task Agentic Sandbox) to differentiate genuine, typed, modular software engineering from fragile single-line scripting shortcuts that collapse on complex algorithms.
+
+* **Weight-Space Consolidation & Inference Efficiency:**
+  Investigating the mechanics of fused adapter checkpoints to understand why consolidated parameter representations yield sub-second inference speeds (0.38s/task) compared to unmerged deliberation paths.
 
 ---
 
-### Releases & Verification
+### Releases & Research Checkpoints
+
+* **[Project Norn V18 (9B Hybrid HRR)](https://huggingface.co/TheEpiales/Project-Norn-V18-9B):**
+  Our 9B hybrid reasoning checkpoint quantized to Q4_K_M GGUF (< 5.8 GB VRAM, 100% local GPU offload). Integrates a native 4096-dimensional hidden projection, a 1024-dimensional 4-channel Relational Graph HRR engine (`norn_biology_proj_v18.pt`), and evaluation against our bundled 1,250-benchmark multi-pillar suite (81.8% Micro / 80.6% Macro accuracy; 94.0% live agentic tool accuracy).
+
+* **[Project Norn V17 (4.5B Standalone Merged Dual-Tier)](https://huggingface.co/TheEpiales/Project-Norn-V17-4.5B):**
+  A consolidated 4.5B parameter dual-tier cognitive system. Bundles complete FP16 safetensors, 4-bit GGUF, and original LoRA adapter weights, demonstrating ultra-fast 0.38s/task agentic tool execution.
+
+* **[Project Norn V15 (4.45B Auto-Router Prototype)](https://huggingface.co/Osakra/norn-v15):**
+  The foundational proof-of-concept testing continuous latent Chain-of-Thought recurrence ($k \in [2, 16]$) and associative memory seeding on top of a 4-bit quantized base.
+
+---
 
-* **[Project Norn V15](https://huggingface.co/Osakra/norn-v15):** An exploratory 4.45B parameter neuro-symbolic checkpoint testing continuous latent Chain-of-Thought deliberation on top of a 4-bit quantized base[cite: 6, 8].
-* **[Evaluation Suite](https://huggingface.co/Osakra/norn-v15):** Bundles all 250 held-out evaluation samples and scoring harnesses directly within the repository to allow full local reproducibility[cite: 6, 8].
+### Grounded Scope & Reproducibility
 
-All code, adapters, and data are published under the Apache-2.0 license to encourage open experimentation and peer verification[cite: 6, 8].
+* **Single-Author Research:** This is focused, exploratory academic research. These models are designed to probe continuous representations, invariant tracking, and compact reasoning, and do not compete with 70B+ proprietary models on open-domain trivia or massive encyclopedic memorization.
+* **Full Community Replication:** Every release bundles raw evaluation suites, deterministic scoring harnesses, audit runners, and transparent execution traces directly in the repository under the Apache-2.0 license to facilitate independent community replication and mechanistic interpretability analysis.