LoganResearch
/

ARC-Base-8B-Condensed

@@ -1,180 +1,1424 @@
-# Lie-Holonomy Transformer (LHT)
-A PyTorch implementation of the gauge-theoretic reasoning architecture from "Beyond Holonomy: Lie-Algebraic Symbol Emergence and the Homotopy Type Structure of Neural Reasoning."
-## Core Ideas
-This architecture treats **reasoning as geometry**:
-| Concept | Mathematical Structure | Implementation |
-|---------|----------------------|----------------|
-| Propositions | Manifold M | Embedding space |
-| Inference | Parallel transport | Gauge-covariant attention |
-| Consistency | Holonomy = Identity | Holonomy loss |
-| Symbols | Lie algebra generators | Generator network |
-| Proof equivalence | Homotopy | Layer depth |
-## Architecture Overview
-```
-Input tokens
-     │
-     ▼
-┌─────────────────────────────────────┐
-│  Token Embedding (Proposition M)    │
-│  + Position Embedding               │
-│  + Fiber Initialization (gauge)     │
-└─────────────────────────────────────┘
-     │
-     ▼
-┌─────────────────────────────────────┐
-│  LHT Layer (× n_layers)             │
-│  ┌─────────────────────────────┐    │
-│  │ Connection Network A(x)     │    │  ← Learns gauge connection
-│  │ Parallel Transport Γ_{j→i}  │    │  ← Transports fiber elements
-│  │ Gauge-Covariant Attention   │    │  ← Modified self-attention
-│  │ Lie Algebra Generator       │    │  ← Generates inference ops
-│  │ Generator Application       │    │  ← Applies exp(X) to fiber
-│  └─────────────────────────────┘    │
-└─────────────────────────────────────┘
-     │
-     ▼
-┌─────────────────────────────────────┐
-│  Output: logits + geometric losses  │
-└─────────────────────────────────────┘
-```
-## Key Components
-### 1. Connection Network
-Learns the gauge connection ω that defines how to parallel transport inferential states:
-```python
-A_μ(x) ∈ gl(k,ℝ)  # Lie algebra valued 1-form
 ```
-### 2. Parallel Transport
-Computes transport operators between positions:
 ```python
-Γ_{j→i} = exp(-A_μ(x_j)(x_i - x_j)^μ)
 ```
-### 3. Gauge-Covariant Attention
-Standard attention with parallel transport of values:
 ```python
-# Standard:  Attn(Q,K,V)_i = Σ_j α_ij V_j
-# Gauge:     GaugeAttn_i   = Σ_j α_ij Γ_{j→i}(V_j)
 ```
-### 4. Holonomy Loss
-Enforces reasoning consistency by requiring closed loops to return to identity:
 ```python
-L_hol = E[||Hol_γ - I||²_F]
 ```
-### 5. Curvature Regularization
-Encourages flat reasoning spaces where order doesn't matter:
 ```python
-L_curv = E[||F(x)||²_F]  where F = dω + ω∧ω
 ```
-## Installation
 ```bash
-pip install torch
 ```
-## Usage
-### Basic
-```python
-from lht import LieHolonomyTransformer, LHTConfig
-# Create model
-config = LHTConfig(
-    vocab_size=32000,
-    d_model=512,
-    d_fiber=64,
-    n_heads=8,
-    n_layers=6,
-    lie_algebra_rank=8,
-)
-model = LieHolonomyTransformer(config)
-# Forward pass
-output = model(
-    input_ids=tokens,
-    labels=labels,
-    return_geometric_losses=True
-)
-# Get losses
-lm_loss = output['lm_loss']
-holonomy_loss = output['holonomy_loss']
-curvature_loss = output['curvature_loss']
-total_loss = model.get_total_loss(output)
-```
-### Training with Geometric Loss Annealing
-```python
-from lht import LHTTrainer
-trainer = LHTTrainer(model, optimizer, config)
-for batch in dataloader:
-    metrics = trainer.train_step(batch)
-    # Early training: high curvature loss → flat representations
-    # Mid training: high holonomy loss → consistency
-    # Late training: high waypoint loss → discrete structure
 ```
-### Waypoint Detection
-```python
-from lht import WaypointDetector
-detector = WaypointDetector(config, n_waypoints=32)
-waypoint_ids, stability = detector(representations)
 ```
-## Configuration
-| Parameter | Description | Default |
-|-----------|-------------|---------|
-| `d_model` | Proposition manifold dimension | 512 |
-| `d_fiber` | Fiber (gauge) dimension | 64 |
-| `lie_algebra_rank` | k for GL(k,ℝ) structure group | 8 |
-| `lambda_holonomy` | Weight for holonomy loss | 0.1 |
-| `lambda_curvature` | Weight for curvature loss | 0.01 |
-| `lambda_waypoint` | Weight for waypoint stability | 0.05 |
-## Theoretical Predictions
-The framework makes testable predictions:
-1. **Chain-of-thought benefit correlates with curvature** - High-curvature domains (causal reasoning) benefit more from CoT than low-curvature domains (arithmetic)
-2. **Waypoints emerge spontaneously** - Training with holonomy loss should cause discrete symbol-like structures to form at flat loci
-3. **Holonomy predicts errors** - Incorrect reasoning paths should have higher holonomy magnitude
-4. **Compositional generalization improves** - Holonomy constraints force consistent composition
-## File Structure
 ```
-lie_holonomy_transformer/
-├── lht.py           # Core implementation
-├── train.py         # Training script
-├── README.md        # This file
-└── experiments/     # Benchmark code (TODO)
 ```
-## References
-- "Beyond Holonomy: Lie-Algebraic Symbol Emergence..." (the paper)
-- Cohen et al. (2019). Gauge Equivariant Convolutional Networks
-- Weiler & Cesa (2019). General E(2)-Equivariant Steerable CNNs
-- The Univalent Foundations Program (2013). Homotopy Type Theory
-## License
-MIT

+---
+license: cc-by-4.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- llama
+- dense-responses
+- self-optimization
+- representation-engineering
+- cf-hot
+- recursive-self-improvement
+- mentor-mode
+- revenue-generation
+- autonomous-agent
+base_model: NousResearch/Hermes-3-Llama-3.1-8B
+---
+<div align="center">
+![ARC Banner](banner.svg)
+# 🤖 ARC ENGINE v2.4
+## Adaptive Recursive Cognition (Übermenschetien)
+**The Most Advanced Self-Improving AI Agent on HuggingFace**
+[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
+[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
+[![Lines of Code](https://img.shields.io/badge/lines-10,346-green.svg)]()
+[![HuggingFace](https://img.shields.io/badge/🤗-HuggingFace-yellow.svg)](https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed)
+*An 8B parameter model that improves itself WITHOUT going insane*
+[Quick Start](#-quick-start) • [Features](#-whats-new-in-v24) • [Commands](#-complete-command-reference) • [Architecture](#-architecture) • [Citation](#-citation)
+</div>
+---
+## 📋 Table of Contents
+1. [Quick Start](#-quick-start)
+2. [What's New in v2.4](#-whats-new-in-v24)
+3. [Complete Command Reference](#-complete-command-reference)
+4. [Architecture](#-architecture)
+5. [Core Technology](#-core-technology)
+6. [Empirical Results](#-empirical-results)
+7. [Installation](#-installation)
+8. [Configuration](#-configuration)
+9. [Repository Structure](#-repository-structure)
+10. [Hardware Requirements](#-hardware-requirements)
+11. [Training From Scratch](#-training-from-scratch)
+12. [API Reference](#-api-reference)
+13. [Comparison With Other Agents](#-comparison-with-other-agents)
+14. [Limitations](#-limitations)
+15. [Changelog](#-changelog)
+16. [Citation](#-citation)
+17. [License](#-license)
+---
+## 🚀 Quick Start
+### One-Command Start (Linux/Mac)
+```bash
+git clone https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed
+cd ARC-Base-8B-Condensed
+./start.sh
+```
+### One-Command Start (Windows)
+```batch
+git clone https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed
+cd ARC-Base-8B-Condensed
+start.bat
+```
+### Manual Start
+```bash
+# Clone repository
+git clone https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed
+cd ARC-Base-8B-Condensed
+# Install dependencies
+pip install -r requirements.txt
+# Run the engine
+python arc_engine_v24_full.py
+```
+On first run, the engine will:
+1. Download the base model (~16GB)
+2. Load the DENSE adapter and CF-HoT heads
+3. Initialize all subsystems
+4. Present an interactive command prompt
+```
+═══════════════════════════════════════════════════════════════════════════════
+🤖 ARC ENGINE v2.4 - Adaptive Recursive Cognition (Übermenschetien)
+   FULL RSI + MENTOR MODE + REVENUE GENERATION
+═══════════════════════════════════════════════════════════════════════════════
+    DENSE Mode:      ON (CONDENSATOR checkpoint)
+    CF-HoT Control:  ON
+    CF-HoT 125×:     OFF
+    AGENTIC Mode:    ON (Full shell/python access)
+    Mentor Mode:     OFF
+    Auto-Train:      OFF
+    Live Browser:    ON
+    Claude API:      ON
+    Experience Buffer: 0 examples
+═══════════════════════════════════════════════════════════════════════════════
+    NEW IN v2.4: !mentor, !revenue, !freelance, !content, !trade
+    Smart help: Type 'help <topic>' (e.g. 'help money', 'help learn')
+You> hello
+Hello. How can I help?
+[Quality: 0.82 | Density: 45.2 | Coherence: 0.95 | Tokens: 5]
+You> help money
+════════════════════════════════════════════════════════════════
+  🔍 SMART HELP: "money"
+════════════════════════════════════════════════════════════════
+  💰 REVENUE GENERATION - Make real money
+  ──────────────────────────────────────────────────────────────
+    !revenue                       Show revenue dashboard and earnings
+    !revenue goal <amount>         Set daily revenue target
+    !freelance scan                Scan Upwork/Fiverr for matching jobs
+    !content blog <topic>          Generate monetizable blog post
+    ...
+```
+---
+## ⭐ What's New in v2.4
+### 🎓 Mentor Mode: Learn From Claude
+The model can now consult Claude when uncertain and **learn from the responses**:
+```
+You> !mentor on
+✓ Mentor Mode ENABLED
+  Will auto-consult Claude when quality < 0.6 or uncertainty > 0.4
+You> !mentor ask What is the difference between TCP and UDP?
+🎓 Asking Claude: What is the difference between TCP and UDP?
+[Local (0.71)]: TCP is connection-oriented protocol with guaranteed delivery...
+[Consulting Claude...]
+[Claude]: TCP (Transmission Control Protocol) provides reliable, ordered delivery
+with connection establishment, acknowledgments, and retransmission. UDP (User
+Datagram Protocol) is connectionless, offering faster but unreliable delivery
+without guarantees. TCP suits applications needing reliability (web, email);
+UDP suits real-time applications prioritizing speed (gaming, streaming, DNS).
+✓ Learning recorded (1 total)
+```
+**How it works:**
+1. Model generates its own response first
+2. If quality is low OR uncertainty is high, consults Claude
+3. Creates DPO training pair: Claude's response = chosen, local = rejected
+4. Adds to experience buffer for continuous learning
+5. Model progressively improves by talking to Claude
+| Command | Description |
+|---------|-------------|
+| `!mentor on` | Enable auto-consultation when uncertain |
+| `!mentor off` | Disable mentor mode |
+| `!mentor ask <question>` | Ask Claude directly, learn from response |
+| `!mentor session` | Open Claude.ai in browser |
+| `!mentor learn` | Show learnings collected from Claude |
+| `!mentor status` | Show mentor mode statistics |
+---
+### 💰 Revenue Generation: Actually Make Money
+A complete suite of tools for generating real revenue:
+#### Revenue Dashboard
+```
+You> !revenue
+════════════════════════════════════════════════════════════════
+  💰 REVENUE DASHBOARD
+════════════════════════════════════════════════════════════════
+  TODAY:    $0.00 / $50.00 goal
+  THIS WEEK: $0.00 / $300.00 goal
+  ALL TIME:  $0.00
+────────────────────────────────────────────────────────────────
+  ACTIVE STREAMS:
+    📋 Freelance: 0 apps, 0 jobs found
+    ✍️  Content: 0 pieces generated
+    📈 Trading: 0 trades, $0.00 P&L
+    🔗 Affiliate: 0 reviews
+    ⚡ Tasks: 0 completed
+────────────────────────────────────────────────────────────────
+```
+#### Freelance Job Hunting
+```
+You> !freelance scan upwork
+[freelance] Scanning upwork for jobs...
+✓ Found 8 potential jobs:
+  1. Python automation script for data processing...
+     Budget: $50-100
+  2. Web scraping project - extract product data...
+     Budget: $25/hr
+  3. AI chatbot development using transformers...
+     Budget: $500-1000
+You> !freelance apply 1
+[freelance] Generating proposal for: Python automation script...
+--- PROPOSAL ---
+I'm an experienced Python developer specializing in automation and data processing.
+Having worked on similar projects involving ETL pipelines and automated workflows,
+I can deliver a robust, well-documented solution within your timeline.
+My approach:
+1. Analyze your data format and requirements
+2. Build modular, testable processing pipeline
+3. Implement error handling and logging
+4. Provide documentation and deployment support
+I'm available to start immediately and can complete this within 3-5 days.
+--- END ---
+Submit this proposal? (yes/no):
+```
+#### Content Generation
+```
+You> !content blog "AI automation trends 2025"
+[content] Generating blog post about: AI automation trends 2025
+--- BLOG POST (1,847 words) ---
+# AI Automation Trends Reshaping Business in 2025
+The landscape of artificial intelligence automation is evolving at an
+unprecedented pace. As we navigate through 2025, several key trends are
+emerging that promise to fundamentally transform how businesses operate...
+## 1. Autonomous AI Agents
+Perhaps the most significant development is the rise of truly autonomous
+AI agents capable of executing complex, multi-step tasks without human
+intervention...
+--- END ---
+You> !content youtube "How to use AI for passive income"
+[content] Generating YouTube script about: How to use AI for passive income
+--- YOUTUBE SCRIPT ---
+[HOOK - 0:00]
+What if I told you that AI could generate income for you while you sleep?
+I'm not talking about some get-rich-quick scheme - I'm talking about
+legitimate, sustainable passive income streams powered by artificial intelligence.
+[INTRO - 0:30]
+Hey everyone, welcome back to the channel...
+--- END ---
+You> !content social "productivity tips"
+[content] Generating social media posts about: productivity tips
+--- SOCIAL POSTS ---
+TWITTER/X:
+🚀 The 2-minute rule changed my life: If it takes less than 2 minutes, do it NOW.
+Stop letting small tasks pile up into overwhelming mountains. #productivity #lifehack
+LINKEDIN:
+After years of optimizing my workflow, I've discovered that productivity isn't
+about doing more—it's about doing what matters...
+INSTAGRAM:
+✨ 5 Productivity Hacks That Actually Work ✨
+1. Time blocking (game changer!)
+2. The 2-minute rule
+3. Single-tasking > multitasking
+...
+--- END ---
+```
+#### Trading (With Safety Limits)
+```
+You> !trade status
+📈 Trading Status:
+  Connected: False
+  Exchange: binance
+  Positions: 0
+  Trades: 0
+  P&L: $0.00
+You> !trade analyze BTC/USDT
+[trade] Analyzing BTC/USDT...
+📊 Market Analysis: BTC/USDT
+  Recommendation: HOLD
+  Confidence: 65%
+You> !trade execute BTC/USDT buy 50
+⚠️ TRADE CONFIRMATION
+  Symbol: BTC/USDT
+  Side: BUY
+  Amount: $50
+Execute? (yes/no):
+```
+#### Affiliate Marketing
+```
+You> !affiliate review "Sony WH-1000XM5 headphones"
+[affiliate] Generating review for: Sony WH-1000XM5 headphones
+--- PRODUCT REVIEW ---
+# Sony WH-1000XM5 Review: The Gold Standard in Noise Cancellation
+After two months of daily use, here's my honest assessment of Sony's
+flagship wireless headphones...
+## Key Features
+- Industry-leading noise cancellation
+- 30-hour battery life
+- Multipoint connection (2 devices)
+- Speak-to-Chat auto-pause
+## Pros
+✅ Best-in-class ANC
+✅ Incredibly comfortable for all-day wear
+✅ Excellent call quality with 8 microphones
+✅ Premium build quality
+## Cons
+❌ No water resistance
+❌ Can't fold flat like XM4
+❌ Premium price point ($399)
+## Who Should Buy
+Perfect for frequent travelers, remote workers, and audiophiles who
+prioritize comfort and noise cancellation...
+## Final Verdict: 9/10
+--- END ---
+You> !affiliate find
+🔗 Affiliate Opportunities:
+  • Amazon technology bestsellers
+  • ShareASale technology programs
+  • CJ Affiliate technology merchants
+  • ClickBank digital products
 ```
+| Command | Description |
+|---------|-------------|
+| `!revenue` | Show revenue dashboard |
+| `!revenue goal <amount>` | Set daily revenue target |
+| `!revenue record <$> <source> [desc]` | Record an earning |
+| `!freelance scan [platform]` | Scan Upwork/Fiverr for jobs |
+| `!freelance apply <#>` | Generate proposal for job |
+| `!freelance status` | Show application stats |
+| `!content blog <topic>` | Generate blog post |
+| `!content youtube <topic>` | Generate YouTube script |
+| `!content social <topic>` | Generate social media posts |
+| `!trade status` | Portfolio overview |
+| `!trade analyze [symbol]` | Market analysis |
+| `!trade execute <sym> <side> <amt>` | Execute trade |
+| `!affiliate review <product>` | Generate product review |
+| `!affiliate find` | Find affiliate opportunities |
+| `!automate status` | Task automation stats |
+---
+### 🔍 Smart Help System
+Natural language command discovery:
+```
+You> help money
+════════════════════════════════════════════════════════════════
+  🔍 SMART HELP: "money"
+════════════════════════════════════════════════════════════════
+  💰 REVENUE GENERATION - Make real money
+  ──────────────────────────────────────────────────────────────
+    !revenue                       Show revenue dashboard and earnings
+    !freelance scan                Scan Upwork/Fiverr for matching jobs
+    !content blog <topic>          Generate monetizable blog post
+    ...
+You> help learn
+════════════════════════════════════════════════════════════════
+  🔍 SMART HELP: "learn"
+════════════════════════════════════════════════════════════════
+  🎓 LEARNING & IMPROVEMENT - Get smarter
+  ──────────────────────────────────────────────────────────────
+    !mentor on                     Auto-consult Claude when uncertain
+    !auto_train on                 Enable continuous learning during chat
+    !condensator                   Run full training pipeline (SFT→DPO→RL)
+    ...
+```
+**Available help topics:**
+- `help money` - Revenue generation commands
+- `help learn` - Training and improvement commands
+- `help write` - Content creation commands
+- `help browse` - Browser automation commands
+- `help code` - Shell and Python commands
+- `help claude` - Claude integration commands
+- `help image` - Image generation commands
+- `help email` - Gmail commands
+- `help audio` - Text-to-speech commands
+- `help status` - System info commands
+---
+## 📚 Complete Command Reference
+### v2.4 New Commands
+#### Mentor Mode 🎓
+| Command | Description |
+|---------|-------------|
+| `!mentor` | Show mentor mode status |
+| `!mentor on` | Enable auto-consultation |
+| `!mentor off` | Disable mentor mode |
+| `!mentor ask <question>` | Ask Claude and learn from response |
+| `!mentor session` | Open Claude.ai in browser |
+| `!mentor learn` | Show collected learnings |
+#### Revenue Generation 💰
+| Command | Description |
+|---------|-------------|
+| `!revenue` | Revenue dashboard |
+| `!revenue goal <$>` | Set daily goal |
+| `!revenue record <$> <source>` | Record earning |
+| `!freelance scan [platform]` | Scan for jobs |
+| `!freelance apply <#>` | Generate proposal |
+| `!freelance status` | Application stats |
+| `!content blog <topic>` | Generate blog post |
+| `!content youtube <topic>` | Generate video script |
+| `!content social <topic>` | Generate social posts |
+| `!trade status` | Portfolio overview |
+| `!trade analyze [symbol]` | Market analysis |
+| `!trade execute <sym> <side> <$>` | Execute trade |
+| `!affiliate review <product>` | Product review |
+| `!affiliate find` | Find opportunities |
+| `!automate status` | Task automation stats |
+#### Smart Help 🔍
+| Command | Description |
+|---------|-------------|
+| `help` | Full command menu |
+| `help <topic>` | Smart help for topic |
+### v2.3 RSI Commands
+#### Continuous Learning 🧠
+| Command | Description |
+|---------|-------------|
+| `!auto_train on` | Enable learning during chat |
+| `!auto_train off` | Disable auto-training |
+| `!auto_train status` | Show auto-train stats |
+| `!skills` | Quality per domain |
+| `!curiosity` | Areas of uncertainty |
+| `!forgetting` | Detect catastrophic forgetting |
+| `!dream` | Force experience replay |
+| `!self_play` | Generate adversarial prompts |
+| `!meta` | Meta-learning stats |
+| `!goals add <metric> <target>` | Add goal |
+| `!goals list` | List goals |
+| `!explain on` | Show reasoning |
+| `!explain off` | Hide reasoning |
+| `!feedback +` | Positive feedback |
+| `!feedback -` | Negative feedback |
+| `!buffer` | Experience buffer stats |
+### v2.2 CONDENSATOR Commands
+| Command | Description |
+|---------|-------------|
+| `!condensator` | Run full SFT→DPO→RL pipeline |
+| `!dpo [checkpoint]` | Run DPO stage only |
+| `!rl [checkpoint]` | Run RL stage only |
+| `!rsi_full` | RSI with CONDENSATOR |
+| `!train_cfhot` | Train CF-HoT heads |
+| `!gate_stats` | CF-HoT gate health |
+### v2.1 Features
+#### Extended Generation ✍️
+| Command | Description |
+|---------|-------------|
+| `!book` | Toggle book mode (16K tokens) |
+| `!write <topic>` | Write a complete book |
+| `!idea <request>` | Claude-powered ideas |
+| `!idea <request> --deep` | 30 detailed ideas |
+| `!claude <prompt>` | Direct Claude prompt |
+| `!expand <idea>` | Expand idea to plan |
+#### CF-HoT Control 🧬
+| Command | Description |
+|---------|-------------|
+| `!cfhot` / `!125x` | Toggle 125× head |
+| `!cfhot status` | Head status |
+| `!rsi15` | 15-iteration stress test |
+#### Multimedia 🎬
+| Command | Description |
+|---------|-------------|
+| `!stream` | Open live token window |
+| `!stream off` | Close stream window |
+| `!audio` / `!tts` | Toggle text-to-speech |
+| `!audio voices` | List TTS voices |
+| `!audio voice N` | Set voice |
+| `!audio rate N` | Set speech rate |
+| `!say <text>` | Speak immediately |
+#### Image Generation 🖼️
+| Command | Description |
+|---------|-------------|
+| `!image` | Image system status |
+| `!image load` | Load SDXL model |
+| `!imagine <prompt>` | Generate with SDXL |
+| `!dalle <prompt>` | Generate with DALL-E 3 |
+| `!image view` | View last image |
+| `!image view <path>` | View image file |
+#### Self-Improvement 📈
+| Command | Description |
+|---------|-------------|
+| `!improve` | Run improvement loop |
+| `!eval` | Full evaluation |
+| `!train <steps>` | Training steps |
+| `!compare` | Compare checkpoints |
+| `!rollback` | Revert to best |
+| `!load <path>` | Load checkpoint |
+| `!plot` | Quality visualization |
+| `!benchmark` | Evaluation suite |
+| `!export [name]` | Export checkpoint |
+| `!import <path>` | Import checkpoint |
+| `!learn` | Learn from history |
+| `!api` | Start REST API |
+#### Agentic Tools 🛠️
+| Command | Description |
+|---------|-------------|
+| `!shell <cmd>` | Execute shell command |
+| `!python <code>` | Execute Python |
+| `!read <path>` | Read file |
+| `!write <path> <content>` | Write file |
+| `!ls [path]` | List directory |
+| `!web <query>` | Web search |
+#### Browser 🌐
+| Command | Description |
+|---------|-------------|
+| `!browse <url>` | Open URL |
+| `!click <selector>` | Click element |
+| `!type <text>` | Type text |
+| `!fill <sel> <text>` | Fill field |
+| `!read` | Read page |
+| `!close` | Close browser |
+#### Email 📧
+| Command | Description |
+|---------|-------------|
+| `!gmail search <query>` | Search emails |
+| `!gmail read <id>` | Read email |
+| `!gmail send <to> <subj> <body>` | Send email |
+#### Mining ⛏️
+| Command | Description |
+|---------|-------------|
+| `!mine` | Mining status |
+| `!mine profit` | Profitability check |
+| `!mine auto` | Auto-mine best coin |
+#### RSI Mode 🔄
+| Command | Description |
+|---------|-------------|
+| `rsi` / `rsi status` | RSI status |
+| `rsi start` / `!rsi` | Start RSI mode |
+| `rsi stop` | Stop RSI |
+| `rsi pause` | Pause RSI |
+| `rsi resume` | Resume RSI |
+| `rsi mode <X>` | Set mode |
+| `rsi target <0.X>` | Set target |
+#### Task Chaining 🔗
+| Command | Description |
+|---------|-------------|
+| `chain: <task>` | Add to chain |
+| `run chain` | Execute chain |
+| `clear chain` | Clear chain |
+#### Utilities ⚙️
+| Command | Description |
+|---------|-------------|
+| `status` | System status |
+| `history` | Quality history |
+| `toggle <flag>` | Toggle settings |
+| `help` | Help menu |
+| `quit` | Exit |
+---
+## 🏗️ Architecture
+### System Overview
+```
+┌─────────────────────────────────────────────────────────────────────────────┐
+│                         ARC ENGINE v2.4 ARCHITECTURE                         │
+├─────────────────────────────────────────────────────────────────────────────┤
+│                                                                             │
+│  ┌─────────────────────────────────────────────────────────────────────┐   │
+│  │                         INPUT PROCESSING                             │   │
+│  │  User Input → Command Parser → Smart Help / Generate / Tool Execute  │   │
+│  └─────────────────────────────────────────────────────────────────────┘   │
+│                                    │                                        │
+│                                    ▼                                        │
+│  ┌─────────────────────────────────────────────────────────────────────┐   │
+│  │                         CORE MODEL STACK                             │   │
+│  ├─────────────────────────────────────────────────────────────────────┤   │
+│  │                                                                       │   │
+│  │   Base Model: Hermes-3-Llama-3.1-8B (NousResearch)                   │   │
+│  │        │                                                              │   │
+│  │        ▼                                                              │   │
+│  │   DENSE Adapter (LoRA) ─── THE CONDENSATOR trained                   │   │
+│  │        │                                                              │   │
+│  │        ▼                                                              │   │
+│  │   CF-HoT Heads ─── Repetition (125×), Hedging, Verbosity             │   │
+│  │        │                                                              │   │
+│  │        ▼                                                              │   │
+│  │   Output Generation ─── Quality-controlled, density-optimized         │   │
+│  │                                                                       │   │
+│  └─────────────────────────────────────────────────────────────────────┘   │
+│                                    │                                        │
+│                                    ▼                                        │
+│  ┌─────────────────────────────────────────────────────────────────────┐   │
+│  │                       QUALITY EVALUATION                             │   │
+│  │  Response → Density Score → Coherence Score → Overall Quality        │   │
+│  │                    │                                                  │   │
+│  │                    ▼                                                  │   │
+│  │  ┌──────────────────────────────────────────────────────────────┐   │   │
+│  │  │ Mentor Mode Check: Quality < 0.6 OR Uncertainty > 0.4?       │   │   │
+│  │  │      │ Yes                                                    │   │   │
+│  │  │      ▼                                                        │   │   │
+│  │  │ Consult Claude → Learn from Response → Update Training Buffer │   │   │
+│  │  └──────────────────────────────────────────────────────────────┘   │   │
+│  └─────────────────────────────────────────────────────────────────────┘   │
+│                                    │                                        │
+│                                    ▼                                        │
+│  ┌─────────────────────────────────────────────────────────────────────┐   │
+│  │                      RSI EXPERIENCE BUFFER                           │   │
+│  │  Store: prompt, response, quality, domain, difficulty, feedback      │   │
+│  │                    │                                                  │   │
+│  │         ┌──────────┴──────────┐                                      │   │
+│  │         ▼                     ▼                                      │   │
+│  │  Auto-Train Trigger?    Dream Cycle?                                 │   │
+│  │         │                     │                                      │   │
+│  │         ▼                     ▼                                      │   │
+│  │  Micro-training        Experience Replay                             │   │
+│  │  (25 steps)            (Reinforce learnings)                         │   │
+│  └─────────────────────────────────────────────────────────────────────┘   │
+│                                    │                                        │
+│                                    ▼                                        │
+│  ┌─────────────────────────────────────────────────────────────────────┐   │
+│  │                      VALIDATION & COMMIT                             │   │
+│  │  New Quality vs Old Quality → Better? COMMIT : ROLLBACK              │   │
+│  └─────────────────────────────────────────────────────────────────────┘   │
+│                                                                             │
+├─────────────────────────────────────────────────────────────────────────────┤
+│                          SUBSYSTEMS                                         │
+├───────────────────┬───────────────────┬───────────────────┬─────────────────┤
+│   REVENUE GEN     │   AGENTIC TOOLS   │    MULTIMEDIA     │   BROWSER       │
+├───────────────────┼───────────────────┼───────────────────┼─────────────────┤
+│ • Freelance       │ • Shell exec      │ • TTS audio       │ • Playwright    │
+│ • Content gen     │ • Python exec     │ • Image gen       │ • Page parsing  │
+│ • Trading         │ • File I/O        │ • Stream window   │ • Form filling  │
+│ • Affiliate       │ • Web search      │ • Book mode       │ • Screenshots   │
+│ • Tasks           │ • Gmail API       │ • Idea expansion  │ • Claude.ai     │
+└───────────────────┴───────────────────┴───────────────────┴─────────────────┘
+```
+### RSI Loop (Recursive Self-Improvement)
+```
+┌─────────────────────────────────────────────────────────────────────────────┐
+│                    RECURSIVE SELF-IMPROVEMENT LOOP                          │
+├─────────────────────────────────────────────────────────────────────────────┤
+│                                                                             │
+│   ┌─────────┐                                                               │
+│   │  CHAT   │◄─────────────────────────────────────────────────┐           │
+│   └────┬────┘                                                   │           │
+│        │                                                        │           │
+│        ▼                                                        │           │
+│   ┌─────────┐                                                   │           │
+│   │ MEASURE │ Calculate quality, density, coherence             │           │
+│   └────┬────┘                                                   │           │
+│        │                                                        │           │
+│        ▼                                                        │           │
+│   ┌─────────┐                                                   │           │
+│   │ BUFFER  │ Store in experience buffer with metadata          │           │
+│   └────┬────┘                                                   │           │
+│        │                                                        │           │
+│        ▼                                                        │           │
+│   ┌──────────────┐                                              │           │
+│   │ AUTO-TRIGGER │ Buffer full? Quality threshold? Feedback?    │           │
+│   └──────┬───────┘                                              │           │
+│          │                                                      │           │
+│     Yes  │  No ─────────────────────────────────────────────────┘           │
+│          │                                                                  │
+│          ▼                                                                  │
+│   ┌─────────────┐                                                           │
+│   │ MICRO-TRAIN │ 25 steps on high-quality buffer samples                   │
+│   └──────┬──────┘                                                           │
+│          │                                                                  │
+│          ▼                                                                  │
+│   ┌─────────────┐                                                           │
+│   │  VALIDATE   │ Compare new model vs checkpoint                           │
+│   └──────┬──────┘                                                           │
+│          │                                                                  │
+│     ┌────┴────┐                                                             │
+│     │         │                                                             │
+│  Better?   Worse?                                                           │
+│     │         │                                                             │
+│     ▼         ▼                                                             │
+│  COMMIT    ROLLBACK                                                         │
+│     │         │                                                             │
+│     └────┬────┘                                                             │
+│          │                                                                  │
+│          ▼                                                                  │
+│   Continue chatting ────────────────────────────────────────────────────────┘
+│                                                                             │
+└─────────────────────────────────────────────────────────────────────────────┘
+```
+### Mentor Mode Flow
+```
+┌─────────────────────────────────────────────────────────────────────────────┐
+│                         MENTOR MODE LEARNING FLOW                           │
+├─────────────────────────────────────────────────────────────────────────────┤
+│                                                                             │
+│   User Prompt                                                               │
+│        │                                                                    │
+│        ▼                                                                    │
+│   ┌─────────────────┐                                                       │
+│   │ Local Generation │ Generate response with local 8B model                │
+│   └────────┬────────┘                                                       │
+│            │                                                                │
+│            ▼                                                                │
+│   ┌─────────────────┐                                                       │
+│   │ Quality Check   │ Evaluate density, coherence, quality                  │
+│   └────────┬────────┘                                                       │
+│            │                                                                │
+│            ▼                                                                │
+│   ┌────────────────────────────────────┐                                    │
+│   │ Quality < 0.6 OR Uncertainty > 0.4 │                                    │
+│   └────────┬───────────────────────────┘                                    │
+│            │                                                                │
+│       Yes  │  No ──────────► Return local response                          │
+│            │                                                                │
+│            ▼                                                                │
+│   ┌─────────────────┐                                                       │
+│   │ Consult Claude  │ Via API or browser                                    │
+│   └────────┬────────┘                                                       │
+│            │                                                                │
+│            ▼                                                                │
+│   ┌─────────────────┐                                                       │
+│   │ Create DPO Pair │                                                       │
+│   │ chosen: Claude  │                                                       │
+│   │ rejected: Local │                                                       │
+│   └────────┬────────┘                                                       │
+│            │                                                                │
+│            ▼                                                                │
+│   ┌─────────────────┐                                                       │
+│   │ Add to Buffer   │ High-quality experience for training                  │
+│   └────────┬────────┘                                                       │
+│            │                                                                │
+│            ▼                                                                │
+│   Return Claude's response + log learning                                   │
+│                                                                             │
+└─────────────────────────────────────────────────────────────────────────────┘
+```
+---
+## 🧬 Core Technology
+### 1. CF-HoT: Contrastive Fine-tuning with Hidden-state Oversight Training
+Real-time behavioral control through hidden-state monitoring:
+```
+┌─────────────────────────────────────────────────────────────────────────────┐
+│                        CF-HoT ARCHITECTURE                                  │
+├─────────────────────────────────────────────────────────────────────────────┤
+│                                                                             │
+│   Hidden States (Layers 16-24)                                              │
+│            │                                                                │
+│            ▼                                                                │
+│   ┌─────────────────┐                                                       │
+│   │ Fiber Projection │ Compress to d=16 per layer                          │
+│   └────────┬────────┘                                                       │
+│            │                                                                │
+│            ▼                                                                │
+│   ┌─────────────────┐                                                       │
+│   │ Layer Attention  │ Weighted aggregation across layers                   │
+│   └────────┬────────┘                                                       │
+│            │                                                                │
+│            ▼                                                                │
+│   ┌─────────────────┐                                                       │
+│   │ Risk Predictor   │ Binary classifier: P(unwanted_behavior)              │
+│   └────────┬────────┘                                                       │
+│            │                                                                │
+│            ▼                                                                │
+│   If P > threshold ──► Apply logit penalties                                │
+│                                                                             │
+└─────────────────────────────────────────────────────────────────────────────┘
+HEAD PERFORMANCE:
+┌──────────────┬─────────────────┬───────────────┐
+│ Head         │ Separation      │ Accuracy      │
+├──────────────┼─────────────────┼───────────────┤
+│ Repetition   │ 125×            │ 99.2%         │
+│ Hedging      │ 1.5×            │ 87.3%         │
+│ Verbosity    │ 2.1×            │ 91.5%         │
+└──────────────┴─────────────────┴───────────────┘
+```
+### 2. THE CONDENSATOR: Dense Response Training
+4-stage training pipeline for maximum information density:
+```
+┌─────────────────────────────────────────────────────────────────────────────┐
+│                       THE CONDENSATOR PIPELINE                              │
+├─────────────────────────────────────────────────────────────────────────────┤
+│                                                                             │
+│  STAGE 1: Supervised Fine-Tuning (SFT)                                      │
+│  ─────────────────────────────────────                                      │
+│  • 53 gold-standard dense response examples                                 │
+│  • Learning rate: 2e-5                                                      │
+│  • Epochs: 3                                                                │
+│  • Loss: 1.17 → 0.72 (39% reduction)                                       │
+│                                                                             │
+│  STAGE 2: Direct Preference Optimization (DPO)                              │
+│  ─────────────────────────────────────────────                              │
+│  • Preference pairs: dense > verbose                                        │
+│  • Beta: 0.1                                                                │
+│  • Epochs: 2                                                                │
+│  • Teaches relative quality                                                 │
+│                                                                             │
+│  STAGE 3: Reinforcement Learning (PPO)                                      │
+│  ─────────────────────────────────────                                      │
+│  • Reward = density - filler_penalty - length_penalty                       │
+│  • Conservative KL constraint                                               │
+│  • Steps: 300                                                               │
+│  • Learning rate: 2e-6                                                      │
+│                                                                             │
+│  STAGE 4: Checkpointing                                                     │
+│  ─────────────────────                                                      │
+│  • Save every 25 steps                                                      │
+│  • A/B comparison on held-out prompts                                       │
+│  • Automatic rollback if quality drops                                      │
+│                                                                             │
+└──────────────────────────────────���──────────────────────────────────────────┘
+```
+### 3. Enhanced CF-HoT (v2.2 Improvements)
+Per paper recommendations:
+| Parameter | Old Value | New Value | Reason |
+|-----------|-----------|-----------|--------|
+| EMA Momentum | 0.9 | 0.995 | Stable control field |
+| Gate Temperature | 1.0 | 2.0 | Softer sigmoid |
+| Gate Bounds | [0, 1] | [0.1, 0.9] | Prevent saturation |
+| Monitoring | None | Every 50 steps | Detect drift |
+| Warmup | None | 0.9→0.995 over 500 steps | Smooth start |
+---
+## 📊 Empirical Results
+### Response Quality Comparison
+| Prompt | Base Hermes-3 | ARC v2.4 |
+|--------|---------------|----------|
+| "hello" | "Hello! I'm here to help you with any questions or tasks you might have. Feel free to ask me anything!" (23 tokens) | "Hello. How can I help?" (5 tokens) |
+| "What is recursion?" | "That's a great question! Recursion is a programming concept where..." (150+ tokens) | "Function calling itself until base case. Stack frames accumulate, unwind." (12 tokens) |
+| "How are you?" | "As an AI, I don't have feelings in the traditional sense, but I'm functioning well..." (25 tokens) | "Functional. Task?" (3 tokens) |
+### Quantitative Metrics
+| Metric | Base Model | ARC v2.4 | Improvement |
+|--------|------------|----------|-------------|
+| Information Density | 17.0 | 45.2 | **+166%** |
+| Avg Token Count | 150 | 45 | **-70%** |
+| Filler Phrases | High | ~0 | **-95%** |
+| Quality Score | 0.52 | 0.78 | **+50%** |
+| CF-HoT Separation | - | 125× | N/A |
+### Self-Improvement Trajectory
+```
+Iteration 0: Quality 0.52 (baseline)
+Iteration 1: Quality 0.58 (+11.5%)
+Iteration 2: Quality 0.63 (+8.6%)
+Iteration 3: Quality 0.68 (+7.9%)
+Iteration 4: Quality 0.72 (+5.9%)
+Iteration 5: Quality 0.75 (+4.2%)
+...
+Iteration 15: Quality 0.78 (stable plateau)
+```
+---
+## 📦 Installation
+### Minimal Installation (Core Features)
+```bash
+pip install torch transformers accelerate peft bitsandbytes datasets trl tqdm
+```
+### Full Installation (All Features)
+```bash
+pip install -r requirements.txt
+```
+### Optional Features
+```bash
+# Browser automation (for !browse, !mentor session)
+pip install playwright
+playwright install firefox
+# Image generation (for !imagine)
+pip install diffusers pillow
+# Text-to-speech (for !audio, !say)
+pip install pyttsx3 gTTS pygame
+# Claude API (for !mentor, !claude, !idea)
+pip install anthropic
+# OpenAI API (for !dalle)
+pip install openai
+# Trading (for !trade)
+pip install ccxt
+# Vector memory
+pip install chromadb sentence-transformers
+# Web search
+pip install duckduckgo-search
+```
+### Environment Variables
+```bash
+# Optional - for enhanced features
+export ANTHROPIC_API_KEY="sk-ant-..."  # Mentor Mode, Claude integration
+export OPENAI_API_KEY="sk-..."          # DALL-E image generation
+```
+---
+## ⚙️ Configuration
+### Main Configuration (in arc_engine_v24_full.py)
 ```python
+class Config:
+    # Generation
+    temperature = 0.85
+    top_p = 0.9
+    max_new_tokens = 512
+    repetition_penalty = 1.1
+    # CF-HoT
+    use_cfhot = True
+    use_cfhot_125x = False
+    cfhot_repetition_threshold = 0.6
+    cfhot_repetition_penalty = 6.0
+    # Self-improvement
+    min_quality_score = 0.5
+    target_quality_score = 0.75
+    training_steps_per_iteration = 25
+    quality_drop_threshold = 0.1
+    # Book mode
+    book_mode = False
+    book_max_tokens = 16384
+    # API server
+    api_port = 8080
 ```
+### RSI Configuration
 ```python
+@dataclass
+class RSIConfig:
+    auto_train_enabled: bool = False
+    buffer_size: int = 1000
+    min_experiences_to_train: int = 50
+    quality_threshold_for_training: float = 0.7
+    dream_cycle_interval: int = 100
+    forgetting_check_interval: int = 50
+    adaptive_lr_enabled: bool = True
+    base_lr: float = 2e-6
 ```
+### Mentor Configuration
 ```python
+@dataclass
+class MentorConfig:
+    enabled: bool = False
+    auto_consult_threshold: float = 0.6
+    uncertainty_threshold: float = 0.4
+    learn_from_responses: bool = True
+    max_daily_consultations: int = 100
 ```
+### Revenue Configuration
 ```python
+@dataclass
+class RevenueConfig:
+    daily_goal: float = 50.0
+    weekly_goal: float = 300.0
+    freelance_enabled: bool = True
+    content_enabled: bool = True
+    trading_enabled: bool = False
+    affiliate_enabled: bool = True
+    hourly_rate: float = 25.0
+    skills: List[str] = ["python", "writing", "data analysis"]
 ```
+---
+## 📁 Repository Structure
+```
+ARC-Base-8B-Condensed/
+│
+├── arc_engine_v24_full.py      # Main engine (10,346 lines)
+├── README.md                    # This file
+├── requirements.txt             # Full dependencies
+├── requirements-minimal.txt     # Core dependencies
+├── start.sh                     # Linux/Mac launcher
+├── start.bat                    # Windows launcher
+│
+├── training_scripts/
+│   ├── the_condensator.py       # 4-stage dense training
+│   ├── train_cfhot_head.py      # CF-HoT head training
+│   ├── train_self_improve.py    # Self-improvement loop
+│   └── quickstart.py            # One-command trainer
+│
+├── dense_checkpoints/
+│   ├── step_100/                # Initial checkpoint
+│   ├── step_200/                # After iteration 1
+│   └── step_300/                # After iteration 2
+│
+├── cfhot_checkpoints/
+│   └── ckpt_5000/               # 125× repetition head
+│       └── risk_predictor.pt
+│
+├── data/
+│   ├── mentor_conversations.jsonl  # Claude learnings
+│   └── revenue_history.json        # Earnings tracking
+│
+├── outputs/
+│   ├── books/                   # Generated books
+│   ├── images/                  # Generated images
+│   ├── ideas/                   # Generated ideas
+│   └── content/                 # Generated content
+│
+├── improvement_logs/            # RSI logs
+├── exports/                     # Checkpoint packages
+└── paper/
+    └── arc_paper.pdf            # Research paper
+```
+---
+## 💻 Hardware Requirements
+| Component | Minimum | Recommended | Optimal |
+|-----------|---------|-------------|---------|
+| GPU VRAM | 8 GB | 16 GB | 24 GB |
+| System RAM | 16 GB | 32 GB | 64 GB |
+| Disk Space | 25 GB | 50 GB | 100 GB |
+| Python | 3.10+ | 3.11 | 3.11 |
+**Tested Configurations:**
+- NVIDIA RTX 3090 (24GB), 64GB RAM, Ubuntu 22.04 ✓
+- NVIDIA RTX 3080 (10GB), 32GB RAM, Windows 11 ✓
+- NVIDIA RTX 4090 (24GB), 128GB RAM, Ubuntu 24.04 ✓
+**Performance:**
+- Inference: ~15-25 tokens/second (varies by GPU)
+- Training: ~4 hours for full CONDENSATOR pipeline (RTX 3090)
+- Self-improvement: ~30 minutes per iteration
+---
+## 🎓 Training From Scratch
+### Quick Start (Automated)
 ```bash
+python training_scripts/quickstart.py --full
 ```
+This runs (~6 hours on RTX 3090):
+1. CF-HoT head training (5000 steps)
+2. CONDENSATOR dense training (3 epochs SFT + DPO + 300 RL steps)
+3. Self-improvement loop (5 iterations)
+### Manual Training
+**Step 1: Train CF-HoT Heads**
+```bash
+python training_scripts/train_cfhot_head.py \
+    --behavior repetition \
+    --steps 5000 \
+    --batch-size 16
 ```
+**Step 2: Run CONDENSATOR Pipeline**
+```bash
+python training_scripts/the_condensator.py \
+    --sft-epochs 3 \
+    --dpo-epochs 2 \
+    --rl-steps 300
 ```
+**Step 3: Self-Improvement Loop**
+```bash
+python training_scripts/train_self_improve.py \
+    --iterations 15 \
+    --target-quality 0.75
+```
+### Interactive Training
+```
+You> !condensator
+[condensator] Starting full pipeline...
+Stage 1: SFT
+  Epoch 1/3: loss=1.17
+  Epoch 2/3: loss=0.89
+  Epoch 3/3: loss=0.72
+  ✓ SFT complete
+Stage 2: DPO
+  Loading preference pairs...
+  Training with β=0.1...
+  ✓ DPO complete
+Stage 3: RL
+  Step 100/300: reward=0.42
+  Step 200/300: reward=0.58
+  Step 300/300: reward=0.71
+  ✓ RL complete
+Stage 4: Checkpoint
+  ✓ Saved to dense_checkpoints/condensator_full/
+[condensator] ✓ Pipeline complete!
+```
+---
+## 🌐 API Reference
+### Start Server
+```bash
+You> !api
+[api] Server running on http://0.0.0.0:8080
+```
+### Endpoints
+#### POST /generate
+```bash
+curl -X POST http://localhost:8080/generate \
+  -H "Content-Type: application/json" \
+  -d '{"prompt": "What is recursion?"}'
+```
+Response:
+```json
+{
+  "response": "Function calling itself until base case. Stack frames accumulate, unwind on return.",
+  "quality": 0.82,
+  "density": 48.3,
+  "tokens": 12
+}
+```
+#### POST /status
+```bash
+curl -X POST http://localhost:8080/status
+```
+Response:
+```json
+{
+  "quality": 0.78,
+  "iteration": 15,
+  "checkpoint": "dense_checkpoints/step_300",
+  "mentor_enabled": false,
+  "auto_train": false,
+  "experience_buffer": 127
+}
+```
+#### GET /health
+```bash
+curl http://localhost:8080/health
+```
+Response:
+```json
+{
+  "status": "healthy",
+  "model_loaded": true,
+  "gpu_available": true
+}
 ```
+---
+## 🏆 Comparison With Other Agents
+| Feature | ARC v2.4 | AutoGPT | BabyAGI | MetaGPT | OpenDevin |
+|---------|----------|---------|---------|---------|-----------|
+| Self-training | ✅ SFT+DPO+RL | ❌ | ❌ | ❌ | ❌ |
+| Runs locally | ✅ 8B model | ❌ API | ❌ API | ❌ API | ❌ |
+| Revenue generation | ✅ | ❌ | ❌ | ❌ | ❌ |
+| Learns from Claude | ✅ Mentor | ❌ | ❌ | ❌ | ❌ |
+| Browser automation | ✅ | ✅ | ❌ | ❌ | ✅ |
+| Single file | ✅ 10K lines | ❌ | ❌ | ❌ | ❌ |
+| Quality-aware | ✅ | ❌ | ❌ | ❌ | ❌ |
+| Auto-rollback | ✅ | ❌ | ❌ | ❌ | ❌ |
+| Image generation | ✅ | ❌ | ❌ | ❌ | ❌ |
+| Voice output | ✅ | ❌ | ❌ | ❌ | ❌ |
+**ARC v2.4 is the only open-source agent that:**
+1. Trains WHILE chatting (not before/after)
+2. Learns from a smarter model (Mentor Mode)
+3. Tracks its own quality and uncertainty
+4. Automatically reverts if it gets worse
+5. Actually tries to generate revenue
+6. Runs 100% local on consumer hardware
+---
+## ⚠️ Limitations
+| Limitation | Description |
+|------------|-------------|
+| Scale | Tested on 8B parameters only |
+| Language | English only |
+| Iterations | 15 stable iterations demonstrated |
+| Evaluation | Heuristic metrics, no formal human study |
+| Revenue | Revenue generation unproven at scale |
+| Trading | Trading features require API keys and carry risk |
+| Memory | Full features require 16GB+ VRAM |
+| SDXL | Image generation requires Python 3.11 |
+---
+## 📝 Changelog
+### v2.4 (Current)
+- ✨ **Mentor Mode**: Learn from Claude in real-time
+- 💰 **Revenue Generation**: Freelance, content, trading, affiliate
+- 🔍 **Smart Help**: Natural language command discovery
+- 📊 Updated startup banner with new features
+- 🐛 Various bug fixes and improvements
+### v2.3
+- 🧠 Full RSI continuous learning system
+- 📈 Auto-train during chat
+- 🌙 Dream cycles for experience replay
+- 🎯 Domain-specific skill tracking
+- ⚠️ Catastrophic forgetting detection
+### v2.2
+- 🔧 Full CONDENSATOR pipeline
+- 🧬 Enhanced CF-HoT with EMA, gate temperature
+- 📊 DPO and RL training stages
+- 🔄 Improved checkpoint management
+### v2.1
+- 🎬 Multimedia: streaming, TTS, images
+- 📚 Book mode (16K tokens)
+- 💡 Claude idea generation
+- 🌐 Browser automation
+- 📧 Gmail integration
+### v2.0
+- 🚀 Initial public release
+- ⚡ CF-HoT 125× repetition head
+- 📝 Dense response training
+- 🔄 Basic self-improvement loop
+---
+## 📄 Citation
+```bibtex
+@software{napolitano2025arc,
+  title={ARC: Adaptive Recursive Cognition via Contrastive Hidden-State Control},
+  author={Napolitano, Logan Matthew},
+  year={2025},
+  version={2.4},
+  url={https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed},
+  note={10,346 lines of self-improving AI agent code},
+  license={CC-BY-4.0}
+}
 ```
+---
+## 📚 References
+1. Zou, A., et al. (2023). Representation Engineering: A Top-Down Approach to AI Transparency. arXiv:2310.01405
+2. Ouyang, L., et al. (2022). Training language models to follow instructions with human feedback. NeurIPS.
+3. Rafailov, R., et al. (2023). Direct Preference Optimization: Your Language Model is Secretly a Reward Model. arXiv:2305.18290
+4. Hu, E. J., et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685
+5. Dettmers, T., et al. (2023). QLoRA: Efficient Finetuning of Quantized LLMs. arXiv:2305.14314
+---
+## 🙏 Acknowledgments
+- **NousResearch** for Hermes-3-Llama-3.1-8B base model
+- **Meta AI** for Llama 3.1 architecture
+- **Hugging Face** for transformers, PEFT, TRL, and Accelerate
+- **Stability AI** for Stable Diffusion XL
+- **Anthropic** for Claude API (Mentor Mode)
+- **OpenAI** for DALL-E 3 API
+---
+## 📜 License
+This project is licensed under **CC BY 4.0** (Creative Commons Attribution 4.0 International).
+You are free to:
+- **Share** — copy and redistribute the material in any medium or format
+- **Adapt** — remix, transform, and build upon the material for any purpose, including commercial
+Under the following terms:
+- **Attribution** — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
+---
+<div align="center">
+**⭐ Star this repo if you find it useful!**
+*"An 8B that improves itself WITHOUT going insane"*
+**[Back to Top](#-arc-engine-v24)**
+</div>