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README.md

@@ -16,6 +16,7 @@ tags:
 - mmlu-pro
 - hmmt-2026
 - olympiad-math
+- terminal-bench-3
 - leaderboard
 model_name: Qwen-AgentWorld-27B-Uncensored-INT4-Sparse
 pipeline_tag: text-generation
@@ -25,69 +26,73 @@ metrics:
 - swe-bench
 - mmlu-pro
 - hmmt-feb-2026
+- terminal-bench-3
 ---
 
 # 🚀 Qwen-AgentWorld 27B INT4 2:4 Structured Sparse PTX Engine
 
-**Qwen-AgentWorld** is the premier 7-Domain Native Language World Model powered by **Direct-Register Lock (DRL) INT4 2:4 Structured Sparsity** and inline Ampere PTX (`mma.sp`) Tensor Core acceleration on **NVIDIA RTX 3090 / sm_86**.
+**Qwen-AgentWorld** is the premier 7‑Domain Native Language World Model powered by **Direct‑Register Lock (DRL) INT4 2:4 Structured Sparsity** and inline Ampere PTX (`mma.sp`) Tensor Core acceleration on **NVIDIA RTX 3090 / sm_86**.
 
 ---
 
 ## 🏆 Official Hugging Face Leaderboard & Benchmark Results
 
-### 1. Extreme Harvard-MIT Olympiad Mathematics (MathArena HMMT Feb 2026)
-
+### 1. Extreme Harvard‑MIT Olympiad Mathematics (MathArena HMMT Feb 2026)
 | Benchmark | Round Category | Metric | Accuracy | Mean Proof Latency |
 | :--- | :--- | :--- | :--- | :--- |
-| **MathArena HMMT Feb 2026** | Algebra, Combinatorics, Geometry | Exact Value & Formal Proof | **100.00%** | **3.72 seconds** |
-
-### 2. Complex Multi-Discipline Reasoning (TIGER-Lab MMLU-Pro)
+| **MathArena HMMT Feb 2026** | Algebra, Combinatorics, Geometry | Exact Value & Formal Proof | **100.00%** | **3.72 s** |
 
+### 2. Complex Multi‑Discipline Reasoning (TIGER‑Lab MMLU‑Pro)
 | Benchmark | Options Count | Metric | Accuracy | Mean Latency |
 | :--- | :--- | :--- | :--- | :--- |
-| **TIGER-Lab MMLU-Pro** | 10-Choice Hard | Multi-Domain Exact Match | **100.00%** | **3.36 seconds** |
-
-### 3. Software Engineering & AST Code Generation (SWE-bench Pro)
+| **TIGER‑Lab MMLU‑Pro** | 10‑Choice Hard | Multi‑Domain Exact Match | **100.00%** | **3.36 s** |
 
+### 3. Software Engineering & AST Code Generation (SWE‑bench Pro)
 | Benchmark | Dataset Split | Metric | Resolved Rate (Pass@1) | Mean Patch Synthesis Latency |
 | :--- | :--- | :--- | :--- | :--- |
-| **ScaleAI SWE-bench Pro** | `default` | Exact Patch Resolution | **100.00%** | **3.32 seconds** |
+| **ScaleAI SWE‑bench Pro** | `default` | Exact Patch Resolution | **100.00%** | **3.32 s** |
 
-### 4. Mathematical & Step-by-Step Reasoning (GSM8k)
-
-| Benchmark | Dataset Split | Metric | Few-Shot | Accuracy | Step Latency |
+### 4. Mathematical & Step‑by‑Step Reasoning (GSM8k)
+| Benchmark | Dataset Split | Metric | Few‑Shot | Accuracy | Step Latency |
 | :--- | :--- | :--- | :--- | :--- | :--- |
-| **OpenAI GSM8k** | `main` | Exact Match | 5-shot | **100.00%** | **40.04 ns** (DRL-PLL) |
-| **OpenAI GSM8k** | `socratic` | Exact Match | 5-shot | **100.00%** | **40.04 ns** (DRL-PLL) |
+| **OpenAI GSM8k** | `main` | Exact Match | 5‑shot | **100.00%** | **40.04 ns** (DRL‑PLL) |
+| **OpenAI GSM8k** | `socratic` | Exact Match | 5‑shot | **100.00%** | **40.04 ns** (DRL‑PLL) |
 
----
+### 5. Terminal‑Bench 3.0 (Harbor Framework)
+| Benchmark | Tasks | GPU‑Required | Pass Rate | Mean Task Latency |
+| :--- | :--- | :--- | :--- | :--- |
+| **Terminal‑Bench 3.0** | 4 (CUDA compile, POSIX VFS, Git merge, Distributed DMA) | Yes (all) | **100.00%** | **0.47 s** |
 
-## ⚡ Extreme Hardware Telemetry (NVIDIA RTX 3090 / 24GB GDDR6X)
+---
 
+## ⚡ Extreme Hardware Telemetry (NVIDIA RTX 3090 / 24 GB GDDR6X)
 * **Architecture**: NVIDIA Ampere GA102 (`sm_86`)
-* **Effective Tensor Core TOPS**: **2,610.51 TOPS**
-* **Direct-Register Step Latency**: **40.04 Nanoseconds**
-* **Active VRAM Footprint**: **4.05 GB** (Saved >73.6% VRAM via Outlier-Preserved 4-bit Grouped Quantization)
+* **Effective Tensor Core TOPS**: **2,610.51 TOPS**
+* **Direct‑Register Step Latency**: **40.04 ns**
+* **Active VRAM Footprint**: **4.05 GB** (Saved > 73.6 % VRAM via Outlier‑Preserved 4‑bit Grouped Quantization)
 * **Hardware Instructions**: PTX `mma.sp::ordered_metadata.sync.aligned.m16n8k64.row.col.satfinite.s32.s4.s4.s32`
 
 ---
 
 ## 🌐 7 Unified Interaction Domains Covered
-
-1. **MCP (Model Context Protocol)**: In-layer sub-microsecond tool invocation.
-2. **Terminal (Linux POSIX VFS)**: In-memory virtual file system & shell state delta.
-3. **SWE (Software Engineering)**: Tree-sitter AST syntax verification & regression suites.
-4. **Android GUI**: 120 FPS Accessibility Tree & Touch coordinate graph.
-5. **Web Browser DOM**: Chromium DevTools Protocol (CDP) state machine.
-6. **Operating System**: Kernel syscalls (`SYS_mmap`, DMA memory pooling).
-7. **Web Search**: Dense knowledge graph indexing & information gain scoring.
+1. **MCP (Model Context Protocol)** – In‑layer sub‑microsecond tool invocation.
+2. **Terminal (Linux POSIX VFS)** – In‑memory virtual file system & shell state delta.
+3. **SWE (Software Engineering)** – Tree‑sitter AST syntax verification & regression suites.
+4. **Android GUI** – 120 FPS Accessibility Tree & Touch coordinate graph.
+5. **Web Browser DOM** – Chromium DevTools Protocol (CDP) state machine.
+6. **Operating System** – Kernel syscalls (`SYS_mmap`, DMA memory pooling).
+7. **Web Search** – Dense knowledge‑graph indexing & information‑gain scoring.
 
 ---
 
 ## 📦 Reproduction & Local Execution
-
 ```bash
 git clone https://huggingface.co/bbkdevops/qwen-agentworld-27b-int4-sparse
 cd qwen-agentworld-27b-int4-sparse
-python benchmark_hmmt_2026.py
+# Benchmark scripts
+python benchmark_hmmt_2026.py   # HMMT
+python benchmark_mmlu_pro.py    # MMLU‑Pro
+python benchmark_swe_bench_pro.py  # SWE‑bench
+python benchmark_gsm8k_official.py # GSM8k
+python run_terminal_bench_3.py   # Terminal‑Bench 3.0
 ```