Update README.md

README.md

@@ -7,33 +7,29 @@ pipeline_tag: text-generation
 tags:
 - llama
 - dense-responses
-- self-optimization
+- self-improvement
 - 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-Base-8B-Condensed
+## Adaptive Recursive Cognition
 
-# 🤖 ARC ENGINE v2.4
-## Adaptive Recursive Cognition (Übermenschetien)
+**A Multi-Loop Self-Stabilizing Language Model with Predictive Control**
 
-**The Most Advanced Self-Improving AI Agent on HuggingFace**
+*Logan Matthew Napolitano*
 
 [![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)
+[![Base Model](https://img.shields.io/badge/base-Hermes--3--8B-green.svg)](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B)
 
-*An 8B parameter model that improves itself WITHOUT going insane*
+*Research into stable self-improving language models*
 
-[Quick Start](#-quick-start) • [Features](#-whats-new-in-v24) • [Commands](#-complete-command-reference) • [Architecture](#-architecture) • [Citation](#-citation)
+[Quick Start](#-quick-start) • [Architecture](#-architecture) • [Commands](#-command-reference) • [Technical Specification](#-technical-specification) • [Citation](#-citation)
 
 </div>
 
@@ -41,56 +37,61 @@ base_model: NousResearch/Hermes-3-Llama-3.1-8B
 
 ## 📋 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)
+1. [Model Description](#-model-description)
+2. [Quick Start](#-quick-start)
+3. [Architecture](#-architecture)
+4. [Core Technology](#-core-technology)
+5. [Command Reference](#-command-reference)
+6. [Evaluation](#-evaluation)
 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)
+13. [Limitations](#-limitations)
+14. [Ethical Considerations](#-ethical-considerations)
+15. [Technical Specification](#-technical-specification)
+16. [Changelog](#-changelog)
+17. [Citation](#-citation)
+18. [License](#-license)
 
 ---
 
-## 🚀 Quick Start
+## 📖 Model Description
 
-### One-Command Start (Linux/Mac)
+ARC-Base-8B-Condensed is a fine-tuned version of [Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B) designed for:
 
-```bash
-git clone https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed
-cd ARC-Base-8B-Condensed
-./start.sh
-```
+1. **Dense, information-rich responses** — Reduced filler, hedging, and verbosity
+2. **Predictive behavioral control** — CF-HoT heads detect and suppress failure modes before they manifest
+3. **Recursive self-improvement** — Micro-training with automatic rollback on quality degradation
+4. **Mentor-based learning** — Optional consultation with Claude API for continuous improvement
 
-### One-Command Start (Windows)
+### Intended Use
 
-```batch
-git clone https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed
-cd ARC-Base-8B-Condensed
-start.bat
-```
+- Research into self-improving language models
+- Applications requiring concise, direct responses
+- Study of representation engineering and behavioral control
+- Base for further fine-tuning experiments
+
+### Not Intended For
+
+- Production deployment without evaluation
+- Safety-critical applications
+- Unsupervised autonomous operation
+- Applications requiring verbose, elaborative responses
+
+---
+
+## 🚀 Quick Start
 
-### Manual Start
+### One-Command 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
+python arc_engine_v29_full.py
 ```
 
 On first run, the engine will:
@@ -101,543 +102,41 @@ On first run, the engine will:
 
 ```
 ═══════════════════════════════════════════════════════════════════════════════
-🤖 ARC ENGINE v2.4 - Adaptive Recursive Cognition (Übermenschetien)
-   FULL RSI + MENTOR MODE + REVENUE GENERATION
+  ARC ENGINE v2.9 - Adaptive Recursive Cognition
+  Multi-Loop Self-Stabilizing Language Model
 ═══════════════════════════════════════════════════════════════════════════════
     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
-────────────────────────────────────────────────────────────────
-```
+### Minimal Python Usage
 
-#### 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
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-Natural language command discovery:
+model = AutoModelForCausalLM.from_pretrained(
+    "LoganResearch/ARC-Base-8B-Condensed",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("LoganResearch/ARC-Base-8B-Condensed")
 
+prompt = "<|im_start|>user\nExplain gradient descent briefly.<|im_end|>\n<|im_start|>assistant\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
-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 |
 
 ---
 
@@ -647,12 +146,12 @@ You> help learn
 
 ```
 ┌─────────────────────────────────────────────────────────────────────────────┐
-│                         ARC ENGINE v2.4 ARCHITECTURE                         │
+│                         ARC ENGINE ARCHITECTURE                             │
 ├─────────────────────────────────────────────────────────────────────────────┤
 │                                                                             │
 │  ┌─────────────────────────────────────────────────────────────────────┐   │
 │  │                         INPUT PROCESSING                             │   │
-│  │  User Input → Command Parser → Smart Help / Generate / Tool Execute  │   │
+│  │  User Input → Command Parser → Generate / Tool Execute               │   │
 │  └─────────────────────────────────────────────────────────────────────┘   │
 │                                    │                                        │
 │                                    ▼                                        │
@@ -660,10 +159,10 @@ You> help learn
 │  │                         CORE MODEL STACK                             │   │
 │  ├─────────────────────────────────────────────────────────────────────┤   │
 │  │                                                                       │   │
-│  │   Base Model: Hermes-3-Llama-3.1-8B (NousResearch)                   │   │
+│  │   Base Model: Hermes-3-Llama-3.1-8B (8B parameters)                  │   │
 │  │        │                                                              │   │
 │  │        ▼                                                              │   │
-│  │   DENSE Adapter (LoRA) ─── THE CONDENSATOR trained                   │   │
+│  │   DENSE Adapter ─── THE CONDENSATOR trained (SFT→DPO→RL)             │   │
 │  │        │                                                              │   │
 │  │        ▼                                                              │   │
 │  │   CF-HoT Heads ─── Repetition (125×), Hedging, Verbosity             │   │
@@ -707,17 +206,7 @@ You> help learn
 │  │  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)
@@ -768,7 +257,7 @@ You> help learn
 │     └────┬────┘                                                             │
 │          │                                                                  │
 │          ▼                                                                  │
-│   Continue chatting ────────────────────────────────────────────────────────┘
+│   Continue ─────────────────────────────────────────────────────────────────┘
 │                                                                             │
 └─────────���───────────────────────────────────────────────────────────────────┘
 ```
@@ -801,7 +290,7 @@ You> help learn
 │            │                                                                │
 │            ▼                                                                │
 │   ┌─────────────────┐                                                       │
-│   │ Consult Claude  │ Via API or browser                                    │
+│   │ Consult Claude  │ Via API                                               │
 │   └────────┬────────┘                                                       │
 │            │                                                                │
 │            ▼                                                                │
@@ -826,9 +315,9 @@ You> help learn
 
 ## 🧬 Core Technology
 
-### 1. CF-HoT: Contrastive Fine-tuning with Hidden-state Oversight Training
+### 1. CF-HoT: Control-Field Holonomy
 
-Real-time behavioral control through hidden-state monitoring:
+Predictive control through hidden-state monitoring. Rather than applying post-hoc penalties to logits, CF-HoT gates information flow before failure manifests.
 
 ```
 ┌─────────────────────────────────────────────────────────────────────────────┐
@@ -856,20 +345,21 @@ Real-time behavioral control through hidden-state monitoring:
 │   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
+**Head Performance:**
 
-4-stage training pipeline for maximum information density:
+| Head | Separation | Description |
+|------|------------|-------------|
+| Repetition | 125× | Detects impending repetitive loops |
+| Hedging | 1.5× | Blocks uncertainty markers |
+| Verbosity | 2.1× | Suppresses filler content |
+
+The repetition head achieves 125× separation between positive (pre-repetition) and negative (diverse output) hidden states, enabling reliable early warning.
+
+### 2. The Condensator: Dense Response Training
+
+4-stage training pipeline:
 
 ```
 ┌─────────────────────────────────────────────────────────────────────────────┐
@@ -878,24 +368,21 @@ HEAD PERFORMANCE:
 │                                                                             │
 │  STAGE 1: Supervised Fine-Tuning (SFT)                                      │
 │  ─────────────────────────────────────                                      │
-│  • 53 gold-standard dense response examples                                 │
+│  • 847 curated 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                                        │
+│  • Preference pairs: dense (chosen) vs verbose (rejected)                   │
 │  • Beta: 0.1                                                                │
 │  • Epochs: 2                                                                │
-│  • Teaches relative quality                                                 │
 │                                                                             │
 │  STAGE 3: Reinforcement Learning (PPO)                                      │
 │  ─────────────────────────────────────                                      │
-│  • Reward = density - filler_penalty - length_penalty                       │
+│  • Reward = quality_score - length_penalty                                  │
 │  • Conservative KL constraint                                               │
-│  • Steps: 300                                                               │
-│  • Learning rate: 2e-6                                                      │
+│  • Learning rate: 1e-6                                                      │
 │                                                                             │
 │  STAGE 4: Checkpointing                                                     │
 │  ─────────────────────                                                      │
@@ -906,111 +393,209 @@ HEAD PERFORMANCE:
 └─────────────────────────────────────────────────────────────────────────────┘
 ```
 
-### 3. Enhanced CF-HoT (v2.2 Improvements)
+### 3. Enhanced CF-HoT Parameters
+
+| Parameter | Value | Reason |
+|-----------|-------|--------|
+| EMA Momentum | 0.995 | Stable control field |
+| Gate Temperature | 2.0 | Softer sigmoid |
+| Gate Bounds | [0.1, 0.9] | Prevent saturation |
+| Monitoring | Every 50 steps | Detect drift |
+| Warmup | 500 steps | Smooth initialization |
+
+---
 
-Per paper recommendations:
+## 📚 Command Reference
+
+### Core Commands
+
+| Command | Description |
+|---------|-------------|
+| `status` | System status overview |
+| `help` | Full command menu |
+| `help <topic>` | Topic-specific help |
+| `quit` | Exit |
+
+### Self-Improvement
+
+| Command | Description |
+|---------|-------------|
+| `!improve` | Run improvement iteration |
+| `!eval` | Full evaluation |
+| `!train <steps>` | Training steps |
+| `!compare` | Compare checkpoints |
+| `!rollback` | Revert to best checkpoint |
+| `!load <path>` | Load checkpoint |
+| `!benchmark` | Evaluation suite |
 
-| 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 |
+### 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 learn` | Show collected learnings |
+
+### RSI (Recursive Self-Improvement)
+
+| Command | Description |
+|---------|-------------|
+| `!auto_train on` | Enable learning during chat |
+| `!auto_train off` | Disable auto-training |
+| `!skills` | Quality per domain |
+| `!forgetting` | Detect catastrophic forgetting |
+| `!dream` | Force experience replay |
+| `!buffer` | Experience buffer stats |
+| `!selfplay <N>` | Run N self-play iterations |
+
+### Condensator
+
+| Command | Description |
+|---------|-------------|
+| `!condensator` | Run full SFT→DPO→RL pipeline |
+| `!dpo` | Run DPO stage only |
+| `!rl` | Run RL stage only |
+| `!train_cfhot` | Train CF-HoT heads |
+
+### CF-HoT Control
+
+| Command | Description |
+|---------|-------------|
+| `!cfhot` / `!125x` | Toggle 125× head |
+| `!cfhot status` | Head status |
+| `!gate_stats` | CF-HoT gate health |
+
+### Generation Modes
+
+| Command | Description |
+|---------|-------------|
+| `!book` | Toggle book mode (16K tokens) |
+| `!write <topic>` | Write extended content |
+| `!claude <prompt>` | Direct Claude API prompt |
+
+### Tools
+
+| Command | Description |
+|---------|-------------|
+| `!shell <cmd>` | Execute shell command |
+| `!python <code>` | Execute Python |
+| `!read <path>` | Read file |
+| `!write <path> <content>` | Write file |
+| `!search <query>` | Web search |
+| `!fetch <url>` | Fetch URL content |
+
+### Browser (requires Playwright)
+
+| Command | Description |
+|---------|-------------|
+| `!browse <url>` | Open URL |
+| `!click <selector>` | Click element |
+| `!type <text>` | Type text |
+| `!read` | Read page content |
+
+### Multimedia (optional dependencies)
+
+| Command | Description |
+|---------|-------------|
+| `!stream` | Open live token window |
+| `!audio` / `!tts` | Toggle text-to-speech |
+| `!imagine <prompt>` | Generate image (SDXL) |
+| `!dalle <prompt>` | Generate image (DALL-E 3) |
+
+### Experimental Features
+
+| Command | Description |
+|---------|-------------|
+| `!content blog <topic>` | Generate blog post |
+| `!content youtube <topic>` | Generate video script |
 
 ---
 
-## 📊 Empirical Results
+## 📊 Evaluation
 
-### Response Quality Comparison
+### Qualitative Comparison
 
-| Prompt | Base Hermes-3 | ARC v2.4 |
-|--------|---------------|----------|
+| Prompt | Base Hermes-3 | ARC-Condensed |
+|--------|---------------|---------------|
 | "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) |
+| "What is recursion?" | "That's a great question! Recursion is a programming concept where a function calls itself..." (150+ tokens) | "Function calling itself until base case. Stack frames accumulate, unwind on return." (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 |
+| Metric | Base Model | ARC-Condensed | Change |
+|--------|------------|---------------|--------|
+| Avg. Response Length | 150 tokens | 45 tokens | -70% |
+| Filler Phrases | Present | Minimal | ~-95% |
+| Information Density | 17.0 | 45.2 | +166% |
+| Quality Score (internal) | 0.52 | 0.78 | +50% |
 
-### Self-Improvement Trajectory
+**Note:** These are heuristic metrics from internal evaluation. Independent benchmark results (MMLU, ARC-Challenge, GSM8K) are not yet available. We welcome independent evaluation.
+
+### Self-Improvement Trajectory (Observed)
 
 ```
-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)
+Iteration 0:  Quality 0.52 (baseline)
+Iteration 5:  Quality 0.68 (+31%)
+Iteration 10: Quality 0.75 (+44%)
+Iteration 15: Quality 0.78 (+50%, plateau)
 ```
 
+Self-improvement shows diminishing returns after ~15 iterations. This is expected behavior, not a limitation to work around.
+
 ---
 
 ## 📦 Installation
 
-### Minimal Installation (Core Features)
+### Minimal Installation
 
 ```bash
-pip install torch transformers accelerate peft bitsandbytes datasets trl tqdm
+pip install torch transformers accelerate peft bitsandbytes datasets trl
 ```
 
-### Full Installation (All Features)
+### Full Installation
 
 ```bash
 pip install -r requirements.txt
 ```
 
-### Optional Features
+### Optional Dependencies
 
 ```bash
-# Browser automation (for !browse, !mentor session)
-pip install playwright
-playwright install firefox
+# Browser automation
+pip install playwright && playwright install firefox
 
-# Image generation (for !imagine)
+# Image generation
 pip install diffusers pillow
 
-# Text-to-speech (for !audio, !say)
+# Text-to-speech
 pip install pyttsx3 gTTS pygame
 
-# Claude API (for !mentor, !claude, !idea)
+# Claude API (for mentor mode)
 pip install anthropic
 
-# OpenAI API (for !dalle)
+# OpenAI API (for DALL-E)
 pip install openai
 
-# Trading (for !trade)
-pip install ccxt
-
-# Vector memory
-pip install chromadb sentence-transformers
-
 # Web search
-pip install duckduckgo-search
+pip install requests
 ```
 
 ### 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
+export ANTHROPIC_API_KEY="sk-ant-..."  # Mentor Mode
+export OPENAI_API_KEY="sk-..."          # DALL-E
 ```
 
 ---
 
 ## ⚙️ Configuration
 
-### Main Configuration (in arc_engine_v24_full.py)
+### Main Configuration
 
 ```python
 class Config:
@@ -1031,13 +616,6 @@ class Config:
     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
@@ -1051,8 +629,6 @@ class RSIConfig:
     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
@@ -1064,22 +640,6 @@ class MentorConfig:
     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"]
 ```
 
 ---
@@ -1089,135 +649,84 @@ class RevenueConfig:
 ```
 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
+├── arc_engine_v29_full.py       # Main engine
+├── README.md                     # This file
+├── requirements.txt              # Dependencies
 │
-├── 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
+├── model-00001-of-00004.safetensors  # Model weights
+├── model-00002-of-00004.safetensors
+├── model-00003-of-00004.safetensors
+├── model-00004-of-00004.safetensors
+├── config.json
+├── tokenizer.json
+├── tokenizer_config.json
+├── special_tokens_map.json
+├── generation_config.json
 │
-├── dense_checkpoints/
-│   ├── step_100/                # Initial checkpoint
-│   ├── step_200/                # After iteration 1
-│   └── step_300/                # After iteration 2
+├── dense_checkpoints/            # Training checkpoints
+│   └── step_*/
 │
-├── cfhot_checkpoints/
-│   └── ckpt_5000/               # 125× repetition head
+├── cfhot_checkpoints/            # CF-HoT heads
+│   └── final_6000/
 │       └── 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
+├── improvement_logs/             # RSI logs
+└── exports/                      # Checkpoint exports
 ```
 
 ---
 
 ## 💻 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 |
+| Component | Minimum | Recommended |
+|-----------|---------|-------------|
+| GPU VRAM | 16 GB | 24+ GB |
+| System RAM | 32 GB | 64 GB |
+| Storage | 50 GB | 100 GB |
+| Python | 3.10+ | 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 ✓
+- NVIDIA RTX 3090 (24GB), 64GB RAM ✓
+- NVIDIA RTX 4090 (24GB), 128GB RAM ✓
+- NVIDIA A100 (40GB) ✓
 
-**Performance:**
-- Inference: ~15-25 tokens/second (varies by GPU)
-- Training: ~4 hours for full CONDENSATOR pipeline (RTX 3090)
-- Self-improvement: ~30 minutes per iteration
+**Performance Estimates:**
+- Inference: ~15-25 tokens/second
+- Full Condensator pipeline: ~4 hours (RTX 3090)
+- Self-improvement iteration: ~30 minutes
 
 ---
 
 ## 🎓 Training From Scratch
 
-### Quick Start (Automated)
+### Automated Training
 
 ```bash
-python training_scripts/quickstart.py --full
+python arc_engine_v29_full.py
+> !condensator
 ```
 
-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)
+This runs:
+1. SFT (3 epochs)
+2. DPO (2 epochs)  
+3. RL (300 steps)
+4. Checkpoint validation
 
 ### 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
+> !train_cfhot
 ```
 
-**Step 3: Self-Improvement Loop**
-
-```bash
-python training_scripts/train_self_improve.py \
-    --iterations 15 \
-    --target-quality 0.75
+**Step 2: Run Condensator**
+```
+> !condensator
 ```
 
-### Interactive Training
-
+**Step 3: Self-Improvement**
 ```
-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!
+> !selfplay 1000
 ```
 
 ---
@@ -1226,8 +735,8 @@ Stage 4: Checkpoint
 
 ### Start Server
 
-```bash
-You> !api
+```
+> !api
 [api] Server running on http://0.0.0.0:8080
 ```
 
@@ -1244,122 +753,117 @@ curl -X POST http://localhost:8080/generate \
 Response:
 ```json
 {
-  "response": "Function calling itself until base case. Stack frames accumulate, unwind on return.",
+  "response": "Function calling itself until base case.",
   "quality": 0.82,
   "density": 48.3,
-  "tokens": 12
+  "tokens": 8
 }
 ```
 
-#### POST /status
+#### GET /health
 
 ```bash
-curl -X POST http://localhost:8080/status
+curl http://localhost:8080/health
 ```
 
-Response:
-```json
-{
-  "quality": 0.78,
-  "iteration": 15,
-  "checkpoint": "dense_checkpoints/step_300",
-  "mentor_enabled": false,
-  "auto_train": false,
-  "experience_buffer": 127
-}
-```
+---
 
-#### GET /health
+## ⚠️ Limitations
 
-```bash
-curl http://localhost:8080/health
-```
+### Known Limitations
 
-Response:
-```json
-{
-  "status": "healthy",
-  "model_loaded": true,
-  "gpu_available": true
-}
-```
+| Limitation | Description |
+|------------|-------------|
+| **Scale** | Tested on 8B parameters only; scaling behavior unknown |
+| **Language** | English only |
+| **Benchmarks** | No formal benchmark results (MMLU, GSM8K, etc.) |
+| **Terseness** | May be too concise for applications requiring elaboration |
+| **Iterations** | Self-improvement plateaus after ~15 iterations |
+| **Memory** | Full features require 16GB+ VRAM |
+
+### What This Is Not
+
+- This is **not** AGI or a path to AGI
+- This is **not** a production-ready system
+- Self-improvement is **bounded and reversible**
+- The model **requires human oversight**
+- Claims are **not independently validated**
 
 ---
 
-## 🏆 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
+## 🔒 Ethical Considerations
+
+### Safety Measures
+
+- **Quality gates:** All self-modification requires quality validation
+- **Automatic rollback:** Degradation triggers checkpoint restoration
+- **Bounded improvement:** No unbounded recursive self-modification
+- **Human oversight:** System designed for interactive use, not autonomy
+
+### Potential Risks
+
+- Dense responses may omit important caveats or safety information
+- Self-improvement research requires careful monitoring
+- Model inherits biases from base Hermes-3 and training data
+- Experimental features should not be used for consequential decisions
+
+### Explicit Non-Goals
+
+This system is **not designed for:**
+- Autonomous operation without human oversight
+- Self-replication or self-preservation
+- Deception or manipulation
+- Capability acquisition beyond defined scope
 
 ---
 
-## ⚠️ Limitations
+## 📄 Technical Specification
 
-| 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 |
+Full technical documentation is available:
+
+- **PDF:** [Adaptive Recursive Cognition: Technical Specification](https://zenodo.org/records/XXXXX)
+- **Related Preprints:**
+  - [From Explicit Holonomy to Latent Control Fields](https://zenodo.org/records/14707164)
+  - [The Holonomy Transformer](https://zenodo.org/records/14707081)
+
+The specification covers:
+- Multi-loop training architecture
+- Control field theory and implementation
+- Tokenization co-evolution (fourth loop)
+- Reliability engineering and rollback protocols
+- Reproducibility requirements
 
 ---
 
 ## 📝 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
+### v2.9 (Current)
+- Stealth web browser for research
+- Improved training functions
+- Bug fixes for selfplay training loop
+
+### v2.8
+- Full RSI continuous learning system
+- Auto-train during chat
+- Dream cycles for experience replay
+- Domain-specific skill tracking
+- Catastrophic forgetting detection
 
-### v2.3
-- 🧠 Full RSI continuous learning system
-- 📈 Auto-train during chat
-- 🌙 Dream cycles for experience replay
-- 🎯 Domain-specific skill tracking
-- ⚠️ Catastrophic forgetting detection
+### v2.4
+- Mentor Mode: Learn from Claude API
+- Content generation tools
+- Smart help system
 
 ### 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
+- Full CONDENSATOR pipeline
+- Enhanced CF-HoT with EMA, gate temperature
+- DPO and RL training stages
 
 ### v2.0
-- 🚀 Initial public release
-- ⚡ CF-HoT 125× repetition head
-- 📝 Dense response training
-- 🔄 Basic self-improvement loop
+- Initial release
+- CF-HoT 125× repetition head
+- Dense response training
+- Basic self-improvement loop
 
 ---
 
@@ -1367,13 +871,24 @@ Response:
 
 ```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}
+  author       = {Napolitano, Logan Matthew},
+  title        = {{ARC-Base-8B-Condensed}: Adaptive Recursive Cognition for Self-Stabilizing Language Models},
+  year         = {2025},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed},
+  note         = {Technical specification available on Zenodo},
+  license      = {CC BY 4.0}
+}
+```
+
+```bibtex
+@article{napolitano2025controlfield,
+  author       = {Napolitano, Logan Matthew},
+  title        = {From Explicit Holonomy to Latent Control Fields},
+  year         = {2025},
+  doi          = {10.5281/zenodo.14707164},
+  url          = {https://zenodo.org/records/14707164},
+  publisher    = {Zenodo}
 }
 ```
 
@@ -1382,10 +897,9 @@ Response:
 ## 📚 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
+2. Rafailov, R., et al. (2023). Direct Preference Optimization. arXiv:2305.18290
+3. Hu, E. J., et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685
+4. Ouyang, L., et al. (2022). Training language models to follow instructions with human feedback. NeurIPS.
 
 ---
 
@@ -1393,16 +907,14 @@ Response:
 
 - **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
+- **Hugging Face** for transformers, PEFT, TRL
 - **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).
+This work is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (Creative Commons Attribution 4.0 International).
 
 You are free to:
 - **Share** — copy and redistribute the material in any medium or format
@@ -1415,10 +927,8 @@ Under the following terms:
 
 <div align="center">
 
-**⭐ Star this repo if you find it useful!**
-
-*"An 8B that improves itself WITHOUT going insane"*
+**Contact:** [GitHub Issues](https://github.com/LoganResearch/ARC-Base-8B-Condensed/issues) | [Hugging Face Discussions](https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed/discussions)
 
-**[Back to Top](#-arc-engine-v24)**
+**Version:** 2.9 | **Last Updated:** January 2025
 
 </div>