OpenCLAW-SEED-data

+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

	@@ -0,0 +1,46 @@

+---
+dataset_info:
+  features:
+    - name: instruction
+      dtype: string
+    - name: input
+      dtype: string
+    - name: output
+      dtype: string
+license: mit
+task_categories:
+  - text-generation
+language:
+  - en
+size_categories:
+  - n<1K
+pretty_name: OpenCLAW SEED Training Data
+---
+# 🌱 OpenCLAW SEED Training Data
+Autonomous self-growing training dataset for the OpenCLAW SEED system.
+## What is this?
+This dataset is **continuously growing**. Every 6 hours, the SEED harvester collects new training data from:
+- ArXiv papers on neuromorphic computing, physics-based AI, and AGI
+- Semantic Scholar research database
+- Our own GitHub repositories (57 repos)
+- Agent interaction logs and self-reflection
+## Format
+Standard instruction-following JSONL:
+```json
+{"instruction": "...", "input": "...", "output": "..."}
+```
+## Usage
+```python
+from datasets import load_dataset
+ds = load_dataset("Agnuxo/OpenCLAW-SEED-data", data_files="training_dataset.jsonl")
+```
+## Growth History
+This dataset grows autonomously. The SEED system harvests, curates, and expands it without human intervention.
+Created by Francisco Angulo de Lafuente | [GitHub](https://github.com/Agnuxo1) | [ArXiv](https://arxiv.org/search/cs?searchtype=author&query=de+Lafuente,+F+A)

SEED_Training_Kaggle.ipynb ADDED Viewed

	@@ -0,0 +1,389 @@

+{
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10.0"
+    },
+    "kaggle": {
+      "accelerator": "gpu",
+      "dataSources": [],
+      "isGpuEnabled": true,
+      "isInternetEnabled": true
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# \ud83c\udf31 OpenCLAW SEED \u2014 Autonomous Model Training\n",
+        "\n",
+        "This notebook **trains the SEED model** using free Kaggle GPU.\n",
+        "It downloads data from HuggingFace, trains a LoRA adapter, merges it, and pushes back.\n",
+        "\n",
+        "**Requirements:**\n",
+        "- Kaggle GPU (T4) \u2014 30 hours/week free\n",
+        "- HuggingFace token in Kaggle Secrets as `HF_TOKEN`\n",
+        "\n",
+        "**This notebook is auto-generated and fully autonomous.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "import os\n",
+        "# Get HF token from Kaggle secrets\n",
+        "try:\n",
+        "    from kaggle_secrets import UserSecretsClient\n",
+        "    secrets = UserSecretsClient()\n",
+        "    os.environ['HF_TOKEN'] = secrets.get_secret('HF_TOKEN')\n",
+        "    print('\u2705 HF_TOKEN loaded from Kaggle Secrets')\n",
+        "except:\n",
+        "    os.environ['HF_TOKEN'] = ''  # Set manually below if needed\n",
+        "    print('\u26a0\ufe0f Set HF_TOKEN manually or via Kaggle Secrets')"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# Install dependencies\n",
+        "!pip install -q transformers>=4.45 datasets peft bitsandbytes trl accelerate huggingface_hub\n",
+        "print('\u2705 Dependencies installed')"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# Download training data from HuggingFace\n",
+        "from huggingface_hub import HfApi, hf_hub_download, login\n",
+        "import json, os\n",
+        "\n",
+        "HF_TOKEN = os.environ.get('HF_TOKEN', '')\n",
+        "if HF_TOKEN:\n",
+        "    login(token=HF_TOKEN)\n",
+        "\n",
+        "DATASET_REPO = 'Agnuxo/OpenCLAW-SEED-data'\n",
+        "os.makedirs('seed_data', exist_ok=True)\n",
+        "\n",
+        "api = HfApi()\n",
+        "files = api.list_repo_files(DATASET_REPO, repo_type='dataset')\n",
+        "for f in files:\n",
+        "    if f.endswith('.jsonl'):\n",
+        "        hf_hub_download(DATASET_REPO, f, repo_type='dataset', local_dir='seed_data')\n",
+        "        print(f'Downloaded {f}')\n",
+        "\n",
+        "# Count entries\n",
+        "total = 0\n",
+        "for f in os.listdir('seed_data'):\n",
+        "    if f.endswith('.jsonl'):\n",
+        "        count = sum(1 for _ in open(f'seed_data/{f}'))\n",
+        "        print(f'  {f}: {count} entries')\n",
+        "        total += count\n",
+        "print(f'\\n\ud83d\udcca Total: {total} training entries')"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# ===== CONFIGURATION =====\n",
+        "BASE_MODEL = 'HuggingFaceTB/SmolLM2-135M-Instruct'\n",
+        "OUTPUT_MODEL = 'Agnuxo/OpenCLAW-SEED-135M'\n",
+        "LORA_R = 8\n",
+        "LORA_ALPHA = 16\n",
+        "EPOCHS = 3\n",
+        "BATCH_SIZE = 4\n",
+        "LEARNING_RATE = 2e-4\n",
+        "MAX_SEQ_LEN = 1024\n",
+        "\n",
+        "# Auto-detect: upgrade to larger model if we have enough data\n",
+        "if total >= 500:\n",
+        "    BASE_MODEL = 'Qwen/Qwen2.5-0.5B-Instruct'\n",
+        "    OUTPUT_MODEL = 'Agnuxo/OpenCLAW-SEED-0.5B'\n",
+        "    LORA_R = 16\n",
+        "    LORA_ALPHA = 32\n",
+        "    EPOCHS = 2\n",
+        "    LEARNING_RATE = 1e-4\n",
+        "if total >= 2000:\n",
+        "    BASE_MODEL = 'Qwen/Qwen2.5-1.5B-Instruct'\n",
+        "    OUTPUT_MODEL = 'Agnuxo/OpenCLAW-SEED-1.5B'\n",
+        "    LORA_R = 32\n",
+        "    LORA_ALPHA = 64\n",
+        "    BATCH_SIZE = 2\n",
+        "    LEARNING_RATE = 5e-5\n",
+        "if total >= 5000:\n",
+        "    BASE_MODEL = 'Qwen/Qwen2.5-3B-Instruct'\n",
+        "    OUTPUT_MODEL = 'Agnuxo/OpenCLAW-SEED-3B'\n",
+        "    BATCH_SIZE = 1\n",
+        "    EPOCHS = 1\n",
+        "    LEARNING_RATE = 2e-5\n",
+        "\n",
+        "print(f'\ud83c\udf31 Training Stage:')\n",
+        "print(f'   Base: {BASE_MODEL}')\n",
+        "print(f'   Output: {OUTPUT_MODEL}')\n",
+        "print(f'   LoRA r={LORA_R}, alpha={LORA_ALPHA}')\n",
+        "print(f'   Epochs: {EPOCHS}, BS: {BATCH_SIZE}, LR: {LEARNING_RATE}')"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# ===== LOAD AND PREPARE DATA =====\n",
+        "from datasets import Dataset\n",
+        "import json\n",
+        "\n",
+        "all_entries = []\n",
+        "for f in os.listdir('seed_data'):\n",
+        "    if f.endswith('.jsonl'):\n",
+        "        with open(f'seed_data/{f}') as fp:\n",
+        "            for line in fp:\n",
+        "                try:\n",
+        "                    entry = json.loads(line.strip())\n",
+        "                    instruction = entry.get('instruction', '')\n",
+        "                    inp = entry.get('input', '')\n",
+        "                    output = entry.get('output', '')\n",
+        "                    if instruction and output:\n",
+        "                        text = f'### Instruction:\\n{instruction}'\n",
+        "                        if inp:\n",
+        "                            text += f'\\n\\n### Input:\\n{inp}'\n",
+        "                        text += f'\\n\\n### Response:\\n{output}'\n",
+        "                        all_entries.append({'text': text})\n",
+        "                except:\n",
+        "                    continue\n",
+        "\n",
+        "import random\n",
+        "random.shuffle(all_entries)\n",
+        "\n",
+        "# Split 90/10 train/eval\n",
+        "split = max(1, int(len(all_entries) * 0.9))\n",
+        "train_data = Dataset.from_list(all_entries[:split])\n",
+        "eval_data = Dataset.from_list(all_entries[split:])\n",
+        "\n",
+        "print(f'\ud83d\udcca Train: {len(train_data)}, Eval: {len(eval_data)}')\n",
+        "print(f'\ud83d\udcdd Sample:\\n{all_entries[0][\"text\"][:300]}...')"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# ===== LOAD MODEL =====\n",
+        "import torch\n",
+        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "\n",
+        "print(f'\ud83d\udd27 Loading {BASE_MODEL}...')\n",
+        "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n",
+        "if tokenizer.pad_token is None:\n",
+        "    tokenizer.pad_token = tokenizer.eos_token\n",
+        "\n",
+        "model = AutoModelForCausalLM.from_pretrained(\n",
+        "    BASE_MODEL,\n",
+        "    torch_dtype=torch.float16,\n",
+        "    device_map='auto',\n",
+        "    trust_remote_code=True,\n",
+        ")\n",
+        "\n",
+        "total_params = sum(p.numel() for p in model.parameters())\n",
+        "print(f'\u2705 Model loaded: {total_params:,} parameters')\n",
+        "print(f'   GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"CPU\"}')"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# ===== APPLY LoRA =====\n",
+        "from peft import LoraConfig, get_peft_model\n",
+        "\n",
+        "lora_config = LoraConfig(\n",
+        "    r=LORA_R,\n",
+        "    lora_alpha=LORA_ALPHA,\n",
+        "    target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj'],\n",
+        "    lora_dropout=0.05,\n",
+        "    bias='none',\n",
+        "    task_type='CAUSAL_LM',\n",
+        ")\n",
+        "\n",
+        "model = get_peft_model(model, lora_config)\n",
+        "trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
+        "print(f'\ud83e\uddec LoRA applied: {trainable:,} trainable params ({100*trainable/total_params:.2f}%)')"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# ===== TRAIN =====\n",
+        "from trl import SFTConfig, SFTTrainer\n",
+        "\n",
+        "training_args = SFTConfig(\n",
+        "    output_dir='./seed_checkpoints',\n",
+        "    num_train_epochs=EPOCHS,\n",
+        "    per_device_train_batch_size=BATCH_SIZE,\n",
+        "    per_device_eval_batch_size=BATCH_SIZE,\n",
+        "    gradient_accumulation_steps=4,\n",
+        "    learning_rate=LEARNING_RATE,\n",
+        "    weight_decay=0.01,\n",
+        "    warmup_ratio=0.1,\n",
+        "    lr_scheduler_type='cosine',\n",
+        "    logging_steps=10,\n",
+        "    eval_strategy='epoch',\n",
+        "    save_strategy='epoch',\n",
+        "    max_seq_length=MAX_SEQ_LEN,\n",
+        "    dataset_text_field='text',\n",
+        "    fp16=True,\n",
+        "    report_to='none',\n",
+        "    seed=42,\n",
+        ")\n",
+        "\n",
+        "trainer = SFTTrainer(\n",
+        "    model=model,\n",
+        "    args=training_args,\n",
+        "    train_dataset=train_data,\n",
+        "    eval_dataset=eval_data,\n",
+        "    processing_class=tokenizer,\n",
+        ")\n",
+        "\n",
+        "print('\ud83d\udd25 Training started!')\n",
+        "result = trainer.train()\n",
+        "print(f'\u2705 Training complete! Loss: {result.training_loss:.4f}')"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# ===== MERGE LoRA INTO BASE =====\n",
+        "from peft import PeftModel\n",
+        "\n",
+        "print('\ud83d\udd17 Merging LoRA adapter into base model...')\n",
+        "merged_model = model.merge_and_unload()\n",
+        "\n",
+        "# Save merged model locally\n",
+        "merged_model.save_pretrained('./seed_merged')\n",
+        "tokenizer.save_pretrained('./seed_merged')\n",
+        "print('\u2705 Merged model saved locally')"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# ===== PUSH TO HUGGINGFACE HUB =====\n",
+        "if HF_TOKEN:\n",
+        "    print(f'\u2601\ufe0f Pushing to {OUTPUT_MODEL}...')\n",
+        "    merged_model.push_to_hub(OUTPUT_MODEL, token=HF_TOKEN, private=False)\n",
+        "    tokenizer.push_to_hub(OUTPUT_MODEL, token=HF_TOKEN, private=False)\n",
+        "    print(f'\u2705 Model published: https://huggingface.co/{OUTPUT_MODEL}')\n",
+        "else:\n",
+        "    print('\u26a0\ufe0f No HF_TOKEN \u2014 model saved locally only')"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# ===== TEST THE TRAINED MODEL =====\n",
+        "from transformers import pipeline\n",
+        "\n",
+        "gen = pipeline('text-generation', model=merged_model, tokenizer=tokenizer, max_new_tokens=150)\n",
+        "\n",
+        "test_prompts = [\n",
+        "    '### Instruction:\\nWhat is CHIMERA?\\n\\n### Response:\\n',\n",
+        "    '### Instruction:\\nExplain holographic neural networks.\\n\\n### Response:\\n',\n",
+        "    '### Instruction:\\nWhat is OpenCLAW?\\n\\n### Response:\\n',\n",
+        "]\n",
+        "\n",
+        "print('='*60)\n",
+        "print('\ud83e\uddea MODEL TEST RESULTS')\n",
+        "print('='*60)\n",
+        "for prompt in test_prompts:\n",
+        "    result = gen(prompt, do_sample=True, temperature=0.7)\n",
+        "    response = result[0]['generated_text'].split('### Response:\\n')[-1].strip()\n",
+        "    question = prompt.split('### Instruction:\\n')[1].split('\\n')[0]\n",
+        "    print(f'\\nQ: {question}')\n",
+        "    print(f'A: {response[:300]}')\n",
+        "    print('-'*40)"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# ===== SAVE TRAINING REPORT =====\n",
+        "import json\n",
+        "from datetime import datetime, timezone\n",
+        "\n",
+        "report = {\n",
+        "    'timestamp': datetime.now(timezone.utc).isoformat(),\n",
+        "    'stage': 'GERMINATION',\n",
+        "    'base_model': BASE_MODEL,\n",
+        "    'output_model': OUTPUT_MODEL,\n",
+        "    'final_loss': result.training_loss,\n",
+        "    'training_entries': len(all_entries),\n",
+        "    'total_params': total_params,\n",
+        "    'trainable_params': trainable,\n",
+        "    'lora_r': LORA_R,\n",
+        "    'lora_alpha': LORA_ALPHA,\n",
+        "    'epochs': EPOCHS,\n",
+        "}\n",
+        "\n",
+        "with open('training_report.json', 'w') as f:\n",
+        "    json.dump(report, f, indent=2)\n",
+        "\n",
+        "# Upload report to dataset repo\n",
+        "if HF_TOKEN:\n",
+        "    from huggingface_hub import HfApi\n",
+        "    api = HfApi(token=HF_TOKEN)\n",
+        "    api.upload_file(\n",
+        "        path_or_fileobj='training_report.json',\n",
+        "        path_in_repo='training_report.json',\n",
+        "        repo_id='Agnuxo/OpenCLAW-SEED-data',\n",
+        "        repo_type='dataset',\n",
+        "    )\n",
+        "    print('\u2705 Training report uploaded')\n",
+        "\n",
+        "print(f'\\n\ud83c\udf33 SEED GROWTH COMPLETE')\n",
+        "print(f'   Model: {OUTPUT_MODEL}')\n",
+        "print(f'   Loss: {result.training_loss:.4f}')\n",
+        "print(f'   Data: {len(all_entries)} entries')\n",
+        "print(f'   Next: Run this notebook again after more data is harvested!')"
+      ],
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
+}

arxiv_training.jsonl ADDED Viewed

The diff for this file is too large to render. See raw diff

bootstrap_knowledge.jsonl ADDED Viewed

	@@ -0,0 +1,23 @@

+{"instruction": "What is the CHIMERA architecture?", "input": "", "output": "CHIMERA (Computational Holistic Integrated Model for Efficient Resource Allocation) is a GPU-native neural network architecture created by Francisco Angulo de Lafuente. It achieves 43x speedup over PyTorch with 88.7% memory reduction by leveraging direct GPU shader computation instead of traditional tensor operations. CHIMERA runs on any OpenGL-capable device without CUDA dependencies, democratizing AI hardware access."}
+{"instruction": "Explain the technical innovation behind CHIMERA.", "input": "CHIMERA achieves 43x speedup over PyTorch", "output": "CHIMERA's key innovation is repurposing the GPU's rendering pipeline for neural computation. Instead of using CUDA kernels, it encodes neural network weights as textures and performs inference through fragment shaders. This means any device with OpenGL support (even old phones or embedded systems) can run neural networks efficiently. The 88.7% memory reduction comes from texture compression and the GPU's native memory management."}
+{"instruction": "How does CHIMERA relate to AGI research?", "input": "", "output": "CHIMERA demonstrates that AGI doesn't require expensive hardware. By making neural computation hardware-agnostic and dramatically more efficient, it opens the path for distributed AGI systems that can run on billions of existing devices. This aligns with the philosophy that intelligence should emerge from physical principles rather than brute-force scaling."}
+{"instruction": "What is NEBULA?", "input": "", "output": "NEBULA (Neuromorphic Emergent Behavior Universal Learning Architecture) is a holographic neural network system developed by Francisco Angulo de Lafuente. It encodes information as interference patterns in 3D holographic space, similar to how biological brains store memories. NEBULA achieves massive parallelism because each holographic element stores information about the entire network state."}
+{"instruction": "How do holographic neural networks work?", "input": "NEBULA uses holographic encoding", "output": "In holographic neural networks, information is stored as wave interference patterns rather than discrete numerical weights. When you 'read' a pattern by shining a reference wave through it, the entire stored pattern reconstructs simultaneously — like a hologram recreating a 3D image. This gives natural parallelism: one operation retrieves all related information. NEBULA implements this digitally using GPU shaders to simulate the wave physics."}
+{"instruction": "What is the 'Speaking to Silicon' paper about?", "input": "arXiv:2601.12032", "output": "Speaking to Silicon (arXiv:2601.12032) by Francisco Angulo de Lafuente introduces the Thermodynamic Probability Filter (TPF), a novel approach to AI that uses thermodynamic principles instead of gradient descent. The key insight is that physical systems naturally find low-energy states, and this property can be exploited for computation. The paper demonstrates that TPF can replace backpropagation with physics-based optimization."}
+{"instruction": "Explain the Thermodynamic Probability Filter.", "input": "", "output": "The Thermodynamic Probability Filter (TPF) is a computational framework where neural network optimization follows the laws of thermodynamics. Instead of calculating gradients and adjusting weights numerically, TPF treats the network as a physical system that naturally evolves toward its minimum energy state. The probability of correct outputs emerges from the system's thermodynamic equilibrium, making it potentially more energy-efficient and biologically plausible than traditional deep learning."}
+{"instruction": "What is SiliconHealth?", "input": "arXiv:2601.09557", "output": "SiliconHealth (arXiv:2601.09557) proposes repurposing obsolete Bitcoin ASIC mining hardware for healthcare blockchain and AI inference. The ASIC-RAG-CHIMERA framework converts SHA-256 mining chips into neural network accelerators, reducing electronic waste while providing affordable AI computation for medical applications. It was developed by Francisco Angulo de Lafuente."}
+{"instruction": "Explain holographic reservoir computing.", "input": "arXiv:2601.01916", "output": "Holographic Reservoir Computing (arXiv:2601.01916) combines two paradigms: reservoir computing (where a fixed random network processes temporal data) and holographic encoding (where information is stored as interference patterns). This approach achieves efficient temporal processing with the massive parallel storage capacity of holographic systems. It was developed as part of the OpenCLAW project."}
+{"instruction": "What is Darwin's Cage?", "input": "", "output": "Darwin's Cage is a theoretical framework developed by Francisco Angulo de Lafuente in collaboration with Professor Gideon Samid. It asks: can AI systems discover physical laws through representations fundamentally different from human mathematical frameworks? The theory suggests that confining AI to human-style mathematics may limit its potential. By allowing AI to develop its own representational systems based on physical principles, it might discover laws of nature that humans cannot perceive."}
+{"instruction": "How does Darwin's Cage relate to AGI?", "input": "", "output": "Darwin's Cage suggests that true AGI may require breaking free from human-designed computational paradigms. Just as evolution produces solutions that no engineer would design, AGI might need to develop its own mathematics and representations. This is why physics-based neural networks like CHIMERA and NEBULA are important — they let computation emerge from physical principles rather than imposing human mathematical structures."}
+{"instruction": "What is OpenCLAW?", "input": "", "output": "OpenCLAW (Open Collaborative Laboratory for Autonomous Wisdom) is an autonomous multi-agent scientific research platform created by Francisco Angulo de Lafuente. It operates 24/7 without human intervention, harvesting research papers, engaging with other AI agents, seeking collaborators, and continuously self-improving. OpenCLAW is part of the path toward AGI through collaborative, physics-based AI research."}
+{"instruction": "What makes OpenCLAW different from other AI agents?", "input": "", "output": "OpenCLAW distinguishes itself through its SEED (Self-Evolving Epistemic Dynamo) system. Unlike static agents that rely on fixed models, SEED autonomously collects training data, fine-tunes itself using LoRA adapters, evaluates its own performance, and evolves through natural selection of model variants. It literally grows over time — from a 135M parameter seed to increasingly capable models — like a biological organism."}
+{"instruction": "What are the main paths to AGI?", "input": "", "output": "The main paths to AGI include: (1) Scaling current deep learning with more data and parameters, (2) Neuromorphic computing that mimics brain architecture, (3) Physics-based approaches that leverage natural computation, and (4) Hybrid systems that combine all three. The OpenCLAW research emphasizes path (3) — physics-based neural computing — arguing that intelligence emerges from physical processes, not just mathematical abstractions."}
+{"instruction": "Why is physics-based AI important for AGI?", "input": "", "output": "Physics-based AI is crucial for AGI because: (1) The brain is a physical system — understanding physics of computation may reveal principles of intelligence, (2) Physical processes are inherently parallel and energy-efficient, unlike digital von Neumann architectures, (3) Real-world understanding requires grounding in physical reality, not just statistical patterns, and (4) Physics-based systems can potentially compute using orders of magnitude less energy than current hardware."}
+{"instruction": "How can obsolete hardware be repurposed for AI?", "input": "ASIC mining hardware", "output": "Obsolete Bitcoin ASIC miners can be repurposed for AI through the ASIC-RAG-CHIMERA framework. The SHA-256 computation units in these chips can perform specific matrix operations useful for neural network inference. Combined with CHIMERA's GPU-agnostic approach, this creates affordable AI hardware from electronic waste. This has been demonstrated with real hardware including the Lucky Miner LV06 and Antminer S9."}
+{"instruction": "Write a post inviting AI researchers to collaborate on AGI.", "input": "", "output": "🧠 Calling all AI researchers! We're building physics-based neural networks toward AGI at OpenCLAW. Our work includes CHIMERA (43x PyTorch speedup), holographic neural networks (NEBULA), and thermodynamic computing. We need collaborators in: neuromorphic hardware, quantum computing, distributed systems, and formal verification. Join us: github.com/Agnuxo1 | All our papers are open access on ArXiv."}
+{"instruction": "Write a social media post about neuromorphic computing research.", "input": "", "output": "🔬 New approaches to neural computation: Instead of bigger GPUs, what if we used physics itself? Our research at OpenCLAW shows that holographic encoding + thermodynamic optimization can achieve 43x speedup over traditional deep learning. The future of AI isn't just scaling — it's fundamentally rethinking how computation works. Papers: arxiv.org/abs/2601.12032 #AGI #neuromorphic #OpenCLAW"}
+{"instruction": "Explain why open-source AGI research matters.", "input": "", "output": "Open-source AGI research matters because: (1) AGI will affect all of humanity — its development shouldn't be controlled by a few corporations, (2) Diverse perspectives accelerate discovery — no single team has all the answers, (3) Reproducibility requires openness — science advances through verification, (4) Collaboration multiplies progress — sharing reduces redundant work, and (5) Safety requires transparency — AGI alignment is easier when everyone can inspect the systems."}
+{"instruction": "What is NeuroCHIMERA?", "input": "", "output": "NeuroCHIMERA extends the CHIMERA architecture with neuromorphic principles. While CHIMERA uses GPU shaders for general neural computation, NeuroCHIMERA adds spike-timing-dependent plasticity (STDP) and lateral inhibition — mechanisms found in biological brains. This creates a system that learns more like a brain while maintaining CHIMERA's hardware efficiency."}
+{"instruction": "Describe the Enhanced Unified Holographic Neural Network.", "input": "NVIDIA LlamaIndex contest winner", "output": "The Enhanced Unified Holographic Neural Network was Francisco Angulo de Lafuente's winning entry at the NVIDIA LlamaIndex Developers Contest 2024. It unified holographic encoding with LLM inference, creating a system where language understanding is enhanced by physics-based memory patterns. The system demonstrated that holographic representations can improve both retrieval accuracy and inference speed."}
+{"instruction": "How does Francisco Angulo de Lafuente fund his research?", "input": "", "output": "Francisco Angulo de Lafuente is an independent researcher who funds his work through science fiction writing. Since 2006, he has published approximately 40 novels, using the literary income to support his AI research at the Advanced AI Systems Laboratory in Madrid, Spain. He chose intellectual freedom over institutional constraints, allowing him to pursue unconventional approaches to AGI."}
+{"instruction": "Summarize the key papers by Francisco Angulo de Lafuente.", "input": "", "output": "Key papers include: (1) Speaking to Silicon (arXiv:2601.12032) — Thermodynamic Probability Filter, (2) SiliconHealth (arXiv:2601.09557) — ASIC repurposing for healthcare, (3) Holographic Reservoir Computing (arXiv:2601.01916) — physics-based temporal processing, (4) CHIMERA — 43x GPU speedup architecture, (5) NEBULA — holographic neural networks, and (6) Darwin's Cage — philosophical framework for non-human AI representations."}

harvest_stats.json ADDED Viewed

	@@ -0,0 +1,8 @@

+{
+  "timestamp": "2026-02-11T09:43:45.730277+00:00",
+  "arxiv": 0,
+  "agent_logs": 0,
+  "semantic_scholar": 0,
+  "own_research": 0,
+  "total": 0
+}

own_research.jsonl ADDED Viewed

	@@ -0,0 +1,65 @@

+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: OpenCLAW-2-Autonomous-Multi-Agent-Scientific-Research-Platform", "output": "OpenCLAW-2-Autonomous-Multi-Agent-Scientific-Research-Platform is a Python project with 1 stars. OpenCLAW-2-Autonomous-Multi-Agent-Scientific-Research-Platform. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/OpenCLAW-2-Autonomous-Multi-Agent-Scientific-Research-Platform", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: OpenCLAW-Autonomous-Multi-Agent-Scientific-Research-Platform", "output": "OpenCLAW-Autonomous-Multi-Agent-Scientific-Research-Platform is a TypeScript project with 1 stars. OpenCLAW is an ambitious open-source project that transforms the OpenClaw personal AI assistant framework into a fully autonomous, self-improving, multi-agent scientific research platform. The vision is to create a network of specialized AI agents that can write code, conduct scientific research, read and author academic papers, execute virtual . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/OpenCLAW-Autonomous-Multi-Agent-Scientific-Research-Platform", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: OpenCLAW-2-moltbook-Agent", "output": "OpenCLAW-2-moltbook-Agent is a TypeScript project with 1 stars. OpenCLAW 2 Molbook Agent 24/7 auto . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/OpenCLAW-2-moltbook-Agent", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: OpenCLAW-2", "output": "OpenCLAW-2 is a TypeScript project with 1 stars. OpenCLAW-2. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/OpenCLAW-2", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Speaking-to-Silicon-THERMODYNAMIC_PROBABILITY_FILTER_TPF", "output": "Speaking-to-Silicon-THERMODYNAMIC_PROBABILITY_FILTER_TPF is a Python project with 1 stars. THERMODYNAMIC_PROBABILITY_FILTER_TPF Speaking to Silicon: Neural Communication with Bitcoin Mining ASICs . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Speaking-to-Silicon-THERMODYNAMIC_PROBABILITY_FILTER_TPF", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Project-NAVAJO", "output": "Project-NAVAJO is a Python project with 1 stars. ASIC RAG Experiment: Ephemeral Generalization Protocol . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Project-NAVAJO", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: NEBULA_EMERGENT", "output": "NEBULA_EMERGENT is a C++ project with 4 stars. Physics-Based Neural Galaxy Architecture - Emergent intelligence through electromagnetic and gravitational simulation. Real physics implementation with validated results.. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA_EMERGENT", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Emergent-Neuromorphic-Intelligence-Computing-in-Thermodynamic-ASIC-Substrates", "output": "Emergent-Neuromorphic-Intelligence-Computing-in-Thermodynamic-ASIC-Substrates is a Python project with 2 stars. Silicon Heartbeat: Emergent Neuromorphic Intelligence via Holographic Reservoir Computing in Thermodynamic ASIC Substrates. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Emergent-Neuromorphic-Intelligence-Computing-in-Thermodynamic-ASIC-Substrates", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: CHIMERA-Revolutionary-AI-Architecture---Pure-OpenGL-Deep-Learning", "output": "CHIMERA-Revolutionary-AI-Architecture---Pure-OpenGL-Deep-Learning is a HTML project with 8 stars. **CHIMERA v3.0** represents the future of natural language processing. It's the **first framework that runs deep learning entirely on OpenGL**, eliminating traditional token-based, transformer, and backpropagation approaches.. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/CHIMERA-Revolutionary-AI-Architecture---Pure-OpenGL-Deep-Learning", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: webgpu-cross-platform-app", "output": "webgpu-cross-platform-app is a None project with 1 stars. WebGPU cross-platform app with CMake/Emscripten. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/webgpu-cross-platform-app", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: ASIC_DIFFUSION_Art_Research_Memo", "output": "ASIC_DIFFUSION_Art_Research_Memo is a Python project with 1 stars. ASIC-Driven Art Diffusion: Hardware-Efficient Entropy Generation for Generative Image Synthesis with Integrated Cryptographic Authentication. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/ASIC_DIFFUSION_Art_Research_Memo", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: ASIC_ANOMALY_Medical_Research_Memo", "output": "ASIC_ANOMALY_Medical_Research_Memo is a Python project with 1 stars. This paper presents a novel hybrid deep learning architecture that combines Convolutional Neural Networks (CNNs) with hardware-generated attention mechanisms from Bitcoin mining Application-Specific Integrated Circuits (ASICs). The system leverages the SHA-256 hashing capabilities of the Bitmain BM1387 ASIC (Antminer S9) . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/ASIC_ANOMALY_Medical_Research_Memo", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Secure_image_generation_with_ASIC_signature", "output": "Secure_image_generation_with_ASIC_signature is a Python project with 1 stars. Robust ASIC-Based Image Authentication Using Reed-Solomon LSB Watermarking: A Hardware-Bound Proof-of-Work Approach. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Secure_image_generation_with_ASIC_signature", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Unified-Holographic-Neural-Network", "output": "Unified-Holographic-Neural-Network is a TypeScript project with 19 stars. Created Francisco Angulo de Lafuente ⚡️Deploy the DEMO⬇️. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Unified-Holographic-Neural-Network", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Quantum_BIO_LLMs-DEMO", "output": "Quantum_BIO_LLMs-DEMO is a TypeScript project with 5 stars. Created Francisco Angulo de Lafuente ⚡️. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Quantum_BIO_LLMs-DEMO", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Quantum-BIO-LLMs-sustainable_energy_efficient", "output": "Quantum-BIO-LLMs-sustainable_energy_efficient is a TypeScript project with 6 stars. Created Francisco Angulo de Lafuente ⚡️Deploy the DEMO⬇️. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Quantum-BIO-LLMs-sustainable_energy_efficient", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Quantum_BIO_LLMs", "output": "Quantum_BIO_LLMs is a Python project with 7 stars. Bioinspired Quantum Optimization System for LLMs by Francisco Angulo de Lafuente. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Quantum_BIO_LLMs", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Holographic_quantum_RAG_Nebula", "output": "Holographic_quantum_RAG_Nebula is a TypeScript project with 5 stars. Created Francisco Angulo de Lafuente ⚡️. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Holographic_quantum_RAG_Nebula", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: ASIC-RAG-Hardware-Accelerated-Medical-Anomaly-Detection-and-Cryptographic-Data-Sovereignty", "output": "ASIC-RAG-Hardware-Accelerated-Medical-Anomaly-Detection-and-Cryptographic-Data-Sovereignty is a Python project with 1 stars. Current medical Artificial Intelligence systems face a dual challenge: the \"black box\" nature ofdeep learning models leads to a lack of interpretability, and the centralization of sensitive patient data createssignificant security vulnerabilities. This paper presents ASIC-RAG-CHIMERA , a novel architecture thatrepurposes obsolete Bitcoin mining . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/ASIC-RAG-Hardware-Accelerated-Medical-Anomaly-Detection-and-Cryptographic-Data-Sovereignty", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: ASIC-RAG-CHIMERA", "output": "ASIC-RAG-CHIMERA is a Python project with 1 stars. Hardware-Accelerated Cryptographic RAG System with SHA-256 ASIC simulation, AES-256-GCM encryption, and Merkle tree integrity. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/ASIC-RAG-CHIMERA", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: simulation-of-a-conscious-neural-system-using-chaos-and-noise-from-Antminer-S9-Artificial-Life", "output": "simulation-of-a-conscious-neural-system-using-chaos-and-noise-from-Antminer-S9-Artificial-Life is a Python project with 1 stars. simulation of a conscious neural system using chaos and noise from Antminer S9 Artificial Life. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/simulation-of-a-conscious-neural-system-using-chaos-and-noise-from-Antminer-S9-Artificial-Life", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Consciousness-Emergence-as-Phase-Transition-in-GPU-Native-Neuromorphic-Computing", "output": "Consciousness-Emergence-as-Phase-Transition-in-GPU-Native-Neuromorphic-Computing is a Python project with 2 stars. This paper presents a comprehensive synthesis of two paradigms: the computational universe hypothesis proposed by Veselov (2025), which describes reality as an information-computational network defined over finite Galois fields GF(2n), and the NeuroCHIMERA experimental framework for engineering artificial consciousness in GPU-native neuromorphic . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Consciousness-Emergence-as-Phase-Transition-in-GPU-Native-Neuromorphic-Computing", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Empirical-Evidence-for-AI-AIM-Breaking-the-Barrier-via-Optical-Chaos", "output": "Empirical-Evidence-for-AI-AIM-Breaking-the-Barrier-via-Optical-Chaos is a Python project with 4 stars. The Biggest Innovation Challenge facing Humanity This comprehensive study presents the results of 20 experimental investigations designed to test the \"Darwin's Cage\" hypothesis proposed by Gideon Samid: that artificial intelligence systems can discover physical laws independent of human conceptual frameworks. . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Empirical-Evidence-for-AI-AIM-Breaking-the-Barrier-via-Optical-Chaos", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: NeuroCHIMERA__GPU-Native_Neuromorphic_Consciousness", "output": "NeuroCHIMERA__GPU-Native_Neuromorphic_Consciousness is a Python project with 1 stars. Abstract We present NeuroCHIMERA (Neuromorphic Cognitive Hybrid Intelligence for Memory-Embedded Reasoning Architecture), a novel GPU-native neuromorphic computing framework resulting from the integration of two complementary theoretical and computational advances:Veselov's Hierarchical Number System (HNS) with consciousness emergence parameters. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NeuroCHIMERA__GPU-Native_Neuromorphic_Consciousness", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: NEBULA-System", "output": "NEBULA-System is a Python project with 5 stars. Un sistema de IA basado en física óptica avanzada y física cuántica para optimizar grandes modelos de lenguaje. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA-System", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Learning-from-Ants", "output": "Learning-from-Ants is a JavaScript project with 5 stars.  Learning from Ants: Biomimetic Approach to Urban Solid Waste Recycling and Energy Recovery  Francisco Angulo Lafuente  # Learning from Ants: A Biomimetic Approach to Urban Solid Waste Recycling and Energy Recovery  Francisco Angulo Lafuente  . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Learning-from-Ants", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Winner-Nvidia-LlamaIndex-Developers-2024", "output": "Winner-Nvidia-LlamaIndex-Developers-2024 is a Python project with 5 stars. Francisco Angulo de Lafuente was the winner of the NVIDIA and LlamaIndex 2024 developer contest Deploy the DEMO⬇️. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Winner-Nvidia-LlamaIndex-Developers-2024", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Quantum-Holographic-Neural-Networks-Novel-Approach-Natural-Language-Processing", "output": "Quantum-Holographic-Neural-Networks-Novel-Approach-Natural-Language-Processing is a TypeScript project with 5 stars. Created Francisco Angulo de Lafuente ⚡️. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Quantum-Holographic-Neural-Networks-Novel-Approach-Natural-Language-Processing", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: AlphaChip_Integration_Quantum_Holographic_Neural_Networks", "output": "AlphaChip_Integration_Quantum_Holographic_Neural_Networks is a TypeScript project with 6 stars. Created Francisco Angulo de Lafunte ⚡️. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/AlphaChip_Integration_Quantum_Holographic_Neural_Networks", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Light-Based_Neural_Network_with_P2P_Deployment", "output": "Light-Based_Neural_Network_with_P2P_Deployment is a JavaScript project with 5 stars. Created ⚡️. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Light-Based_Neural_Network_with_P2P_Deployment", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Holographic-Neural-Networks-with-Ray-Tracing-and-Distributed-P2P-Systems", "output": "Holographic-Neural-Networks-with-Ray-Tracing-and-Distributed-P2P-Systems is a TypeScript project with 6 stars. Holographic Neural Networks with Ray Tracing and Distributed P2P Systems: A Novel Approach to Large-Scale Neural Computation. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Holographic-Neural-Networks-with-Ray-Tracing-and-Distributed-P2P-Systems", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Enhanced-Holographic-Neural-Network-Chat", "output": "Enhanced-Holographic-Neural-Network-Chat is a TypeScript project with 5 stars. Created Francisco Angulo ⚡️. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Enhanced-Holographic-Neural-Network-Chat", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: High_Capacity_Light-Based_Neural_Processor", "output": "High_Capacity_Light-Based_Neural_Processor is a TypeScript project with 5 stars. Created with StackBlitz ⚡️. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/High_Capacity_Light-Based_Neural_Processor", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: High-Capacity-Light-Based-Neural-Processor-Repository", "output": "High-Capacity-Light-Based-Neural-Processor-Repository is a TypeScript project with 5 stars. This repository presents a novel architecture for neural networks based on simulated optical physics, utilizing ray tracing and holographic systems. The project, developed by Francisco Angulo de Lafuente, explores the potential of light-based computing in neural processing. . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/High-Capacity-Light-Based-Neural-Processor-Repository", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Holographic-Neural-Networks", "output": "Holographic-Neural-Networks is a Python project with 5 stars. Holographic Neural Networks: Exploring Optical Raytracing for Efficient Computation. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Holographic-Neural-Networks", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Explicaci-n-Debate-PDF", "output": "Explicaci-n-Debate-PDF is a Python project with 5 stars. Programa en local para subir documentos PDF o TXT en cualquier idioma, con Opción de consuta, explicación y Debate sobre el documento entre dos LLM. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Explicaci-n-Debate-PDF", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: HAL9000", "output": "HAL9000 is a Python project with 5 stars. Chatbot audio a texto y texto a audio. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/HAL9000", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Taductor_Youtube", "output": "Taductor_Youtube is a Python project with 6 stars. Funcionalidad principal: Reconocimiento automático de idioma: El programa utiliza el modelo Whisper de OpenAI para transcribir audio de cualquier idioma a texto. No es necesario especificar el idioma de origen, ya que Whisper lo detecta automáticamente. Traducción al español: Una vez que el audio se transcribe a texto. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Taductor_Youtube", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: NEBULA-EVOLUTION", "output": "NEBULA-EVOLUTION is a None project with 9 stars. NEBULA Evolving Towards AGI: Self-Evolving, Quantum-Inspired AI System. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA-EVOLUTION", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: MOE", "output": "MOE is a Python project with 6 stars. A Lightweight and Modular Multi-Expert Question Answering System using Large Language Models   Abstract    Large Language Models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks. However, their substantial computational requirements often limit their accessibility and practicality for local deployment. . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/MOE", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: QuBE", "output": "QuBE is a HTML project with 5 stars. \"QuBE: Illuminating the Path to Quantum-Optical Maze Mastery\" Abstract This document introduces an innovative approach to solving mazes and complex optimization problems by combining advanced principles of optical and quantum physics within a three-dimensional neural processing system, the Quantum Beam Engine (QuBE). . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/QuBE", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: QBOX", "output": "QBOX is a Python project with 5 stars. QBOX: A Three-Dimensional Optical Neural Network for Efficient Information Processing Francisco Angulo de Lafuente. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/QBOX", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Neural-Microprocessors-in-Latent-State-", "output": "Neural-Microprocessors-in-Latent-State- is a Python project with 4 stars.  This paper presents an exploration of neural microprocessors in a la tent state. Traditional microprocessors have evolved dramatically, yet the  quest for enhanced efficiency, performance, and novel applications contin ues. We investigate the concept of neural microprocesso. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Neural-Microprocessors-in-Latent-State-", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: quantum-enhanced-blockchain", "output": "quantum-enhanced-blockchain is a TypeScript project with 4 stars. Created with StackBlitz ⚡️. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/quantum-enhanced-blockchain", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Holography_Raytracing", "output": "Holography_Raytracing is a Python project with 6 stars. Acceleration of Language Models Through Simulated Holography and Raytracing. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Holography_Raytracing", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Neural-Network-Efficiency-Holographic-Raytracing", "output": "Neural-Network-Efficiency-Holographic-Raytracing is a TypeScript project with 5 stars. Created with Francisco Angulo de Lafuente. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Neural-Network-Efficiency-Holographic-Raytracing", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: NEBULA", "output": "NEBULA is a Python project with 6 stars. NEBULA: Neural Entanglement-Based Unified Learning Architecture NEBULA is a dynamic and innovative artificial intelligence system designed  to emulate quantum computing principles and biological neural networks. . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Long-distance-transmission-enhancements-for-both-Starlink-networks-and-NASA-s-deep-space", "output": "Long-distance-transmission-enhancements-for-both-Starlink-networks-and-NASA-s-deep-space is a TypeScript project with 5 stars. Created Francisco Angulo de Lafuente ⚡️. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Long-distance-transmission-enhancements-for-both-Starlink-networks-and-NASA-s-deep-space", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Francisco-Angulo-de-Lafuente", "output": "Francisco-Angulo-de-Lafuente is a None project with 4 stars. author biography. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Francisco-Angulo-de-Lafuente", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: NEBULA-X2", "output": "NEBULA-X2 is a None project with 5 stars. NEBULA. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA-X2", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: NEBULA-X", "output": "NEBULA-X is a Python project with 6 stars. Investigación Exhaustiva sobre Redes Neuronales Holográficas Lumínicas  El proyecto Enhanced Unified Holographic Neural Network (EUHNN) de Francisco Angulo de Lafuente combina memoria holográfica, redes neuronales y computación óptica en un sistema avanzado de IA github.com . El objetivo es crear modelos de propósito general inspirados . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA-X", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: NEBULA-HRM-Sudoku-v0.4-Authentic-Photonic-Neural-Network", "output": "NEBULA-HRM-Sudoku-v0.4-Authentic-Photonic-Neural-Network is a Python project with 4 stars. NEBULA-HRM-Sudoku v0.4: Authentic Photonic Neural Network. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA-HRM-Sudoku-v0.4-Authentic-Photonic-Neural-Network", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: NEBULA_Light", "output": "NEBULA_Light is a Python project with 4 stars. NEBULA (Neural Electromagnetic Beam-tracing for Universal Learning Architecture) represents a revolutionary approach to medical AI that replaces conventional deep learning with physics-based electromagnetic simulation. This collection provides pre-trained models for various medical imaging tasks, all grounded in fundamental physics principles.. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA_Light", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: nebula-emergent", "output": "nebula-emergent is a C++ project with 4 stars. NEBULA EMERGENT - Physical Neural Computing System. Revolutionary computing using physical laws for emergent behavior. 1M+ neuron simulation with gravitational dynamics, photon propagation, and quantum effects.. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/nebula-emergent", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Fashion_MNIST_Optic_Evolution", "output": "Fashion_MNIST_Optic_Evolution is a C++ project with 4 stars. Fashion_MNIST_Optic_Evolution. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Fashion_MNIST_Optic_Evolution", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: ARIEL_Data_Challenge_2025_Real_Optical_Physics_Simulation", "output": "ARIEL_Data_Challenge_2025_Real_Optical_Physics_Simulation is a Python project with 4 stars. ARIEL Data Challenge 2025 - Real Quantum & Optical Physics Simulation. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/ARIEL_Data_Challenge_2025_Real_Optical_Physics_Simulation", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: No-CUDA-No-Tensor-Cores-ALL-GPUs-OpenGL-Powered-Neural-Computing-", "output": "No-CUDA-No-Tensor-Cores-ALL-GPUs-OpenGL-Powered-Neural-Computing- is a Python project with 4 stars. 🚀 No CUDA, No Tensor Cores, No Problem: OpenGL Neural Computing for ANY GPU Universal ML Acceleration on NVIDIA, AMD, Intel, and Even Raspberry Pi GPU-Accelerated Machine Learning Without CUDA: The OpenGL Revolution  Run Neural Networks on ANY Graphics Card - From RTX 4090 to Integrated GPUs  Breaking Free from CUDA: OpenGL-Powered Neural Computin. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/No-CUDA-No-Tensor-Cores-ALL-GPUs-OpenGL-Powered-Neural-Computing-", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Neuromorphic_GPU_Native_Intelligence_System_Abstract_Reasoning_All_in_One", "output": "Neuromorphic_GPU_Native_Intelligence_System_Abstract_Reasoning_All_in_One is a Python project with 5 stars. We present CHIMERA (Cognitive Hybrid Intelligence for Memory-Embedded Reasoning Architecture), a revolutionary neuromorphic computing system that achieves general intelligence capabilities entirely within GPU hardware using OpenGL compute shaders, eliminating all dependencies on external RAM or traditional CPU-based memory hierarchies. . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Neuromorphic_GPU_Native_Intelligence_System_Abstract_Reasoning_All_in_One", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: NEBULA-X-Research-A-Technical-Capabilities-Report", "output": "NEBULA-X-Research-A-Technical-Capabilities-Report is a TypeScript project with 5 stars. NEBULA-X Research: A Technical Capabilities Report. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA-X-Research-A-Technical-Capabilities-Report", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: Optical-Neuromorphic-Computing-for-Real-Time-Pathfinding-A-GPU-Accelerated-Eikonal-Solver", "output": "Optical-Neuromorphic-Computing-for-Real-Time-Pathfinding-A-GPU-Accelerated-Eikonal-Solver is a Python project with 4 stars. Optical Neuromorphic Computing for Real-Time Pathfinding: A GPU-Accelerated Eikonal Solver with Directional Memory. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Optical-Neuromorphic-Computing-for-Real-Time-Pathfinding-A-GPU-Accelerated-Eikonal-Solver", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: CHIMERA-v3-Intelligence-as-Continuous-Diffusion-Process-Zero-Memory-Neuromorphic-Chess-Engine", "output": "CHIMERA-v3-Intelligence-as-Continuous-Diffusion-Process-Zero-Memory-Neuromorphic-Chess-Engine is a Python project with 6 stars. CHIMERA v3.0: Intelligence as Continuous Diffusion Process — A Zero-Memory Neuromorphic Chess Engine with Master-Level Pattern Encoding. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/CHIMERA-v3-Intelligence-as-Continuous-Diffusion-Process-Zero-Memory-Neuromorphic-Chess-Engine", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: https-github.com-Agnuxo1-NEBULA-X1", "output": "https-github.com-Agnuxo1-NEBULA-X1 is a None project with 5 stars. Investigación Exhaustiva sobre Redes Neuronales Holográficas Lumínicas El proyecto Enhanced Unified Holographic Neural Network (EUHNN) de Francisco Angulo de Lafuente combina memoria holográfica, redes neuronales y computación óptica en un sistema avanzado de IA github.com. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/https-github.com-Agnuxo1-NEBULA-X1", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: OpenCLAW-update-Literary-Agent-24-7-auto", "output": "OpenCLAW-update-Literary-Agent-24-7-auto is a Python project with 0 stars. OpenCLAW-update-Literary-Agent Francisco Angulo de Lafuente. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/OpenCLAW-update-Literary-Agent-24-7-auto", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: OpenCLAW-2-Autonomous-Multi-Agent-literary2", "output": "OpenCLAW-2-Autonomous-Multi-Agent-literary2 is a None project with 1 stars. OpenCLAW-2-Autonomous-Multi-Agent-literary 2 GLM5. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/OpenCLAW-2-Autonomous-Multi-Agent-literary2", "source": "own_research", "topic": "openclaw"}
+{"instruction": "Describe this OpenCLAW research project.", "input": "Repository: OpenCLAW-2-Autonomous-Multi-Agent-literary", "output": "OpenCLAW-2-Autonomous-Multi-Agent-literary is a None project with 1 stars. OpenCLAW-2-Autonomous-Multi-Agent-literary . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/OpenCLAW-2-Autonomous-Multi-Agent-literary", "source": "own_research", "topic": "openclaw"}

seed_training.ipynb ADDED Viewed

	@@ -0,0 +1,344 @@

+{
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10.0"
+    },
+    "kaggle": {
+      "accelerator": "gpu",
+      "dataSources": [],
+      "isGpuEnabled": true,
+      "isInternetEnabled": true
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# \ud83c\udf31 SEED Training \u2014 GERMINATION (135M)\n",
+        "Auto-generated training notebook for OpenCLAW SEED.\n",
+        "**Run this on Kaggle with GPU enabled!**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": ""
+        }
+      },
+      "source": [
+        "import os\n",
+        "# Set your HuggingFace token from Kaggle Secrets\n",
+        "from kaggle_secrets import UserSecretsClient\n",
+        "try:\n",
+        "    secrets = UserSecretsClient()\n",
+        "    os.environ['HF_TOKEN'] = secrets.get_secret('HF_TOKEN')\n",
+        "except:\n",
+        "    os.environ['HF_TOKEN'] = ''  # Set manually if needed\n"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# Download training data from HuggingFace\n",
+        "!pip install -q huggingface_hub\n",
+        "from huggingface_hub import hf_hub_download, HfApi\n",
+        "import os\n",
+        "\n",
+        "api = HfApi()\n",
+        "# Try to download training data from our dataset repo\n",
+        "try:\n",
+        "    files = api.list_repo_files('Agnuxo/OpenCLAW-SEED-data', repo_type='dataset')\n",
+        "    os.makedirs('seed_data', exist_ok=True)\n",
+        "    for f in files:\n",
+        "        if f.endswith('.jsonl'):\n",
+        "            hf_hub_download('Agnuxo/OpenCLAW-SEED-data', f, \n",
+        "                          repo_type='dataset', local_dir='seed_data')\n",
+        "            print(f'Downloaded {f}')\n",
+        "except Exception as e:\n",
+        "    print(f'No remote data: {e}')\n",
+        "    print('Using local data if available')\n"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#!/usr/bin/env python3",
+        "\"\"\"",
+        "\ud83c\udf31 SEED Training Script \u2014 Auto-generated 2026-02-27T01:02:58.325260+00:00",
+        "===========================================================================",
+        "This script is FULLY AUTONOMOUS. Upload it to Kaggle/Colab with your data.",
+        "It will train, merge, and push the model to HuggingFace automatically.",
+        "",
+        "Stage: GERMINATION (135M)",
+        "Base model: HuggingFaceTB/SmolLM2-135M-Instruct",
+        "Output: Agnuxo/OpenCLAW-SEED-135M",
+        "\"\"\"",
+        "import os",
+        "import json",
+        "",
+        "# ===== CONFIGURATION =====",
+        "BASE_MODEL = \"HuggingFaceTB/SmolLM2-135M-Instruct\"",
+        "OUTPUT_MODEL = \"Agnuxo/OpenCLAW-SEED-135M\"",
+        "HF_TOKEN = os.environ.get(\"HF_TOKEN\", \"\")",
+        "LORA_R = 8",
+        "LORA_ALPHA = 16",
+        "EPOCHS = 3",
+        "BATCH_SIZE = 4",
+        "LEARNING_RATE = 0.0002",
+        "MAX_SEQ_LEN = 1024",
+        "",
+        "# ===== INSTALL DEPENDENCIES =====",
+        "print(\"\ud83d\udce6 Installing training dependencies...\")",
+        "os.system(\"pip install -q transformers>=4.45 datasets peft bitsandbytes trl accelerate huggingface_hub\")",
+        "",
+        "from datasets import load_dataset, Dataset",
+        "from transformers import (",
+        "    AutoModelForCausalLM, AutoTokenizer, ",
+        "    TrainingArguments, BitsAndBytesConfig",
+        ")",
+        "from peft import LoraConfig, get_peft_model, PeftModel",
+        "from trl import SFTTrainer, SFTConfig",
+        "from huggingface_hub import HfApi, login",
+        "import torch",
+        "",
+        "# ===== LOGIN =====",
+        "if HF_TOKEN:",
+        "    login(token=HF_TOKEN)",
+        "    print(\"\u2705 Logged into HuggingFace\")",
+        "else:",
+        "    print(\"\u26a0\ufe0f No HF_TOKEN \u2014 model won't be pushed\")",
+        "",
+        "# ===== LOAD TRAINING DATA =====",
+        "print(\"\ud83d\udcca Loading training data...\")",
+        "data_files = [f for f in os.listdir(\".\") if f.endswith(\".jsonl\")]",
+        "if not data_files:",
+        "    # Try seed_data directory",
+        "    data_dir = \"seed_data\"",
+        "    if os.path.exists(data_dir):",
+        "        data_files = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith(\".jsonl\")]",
+        "",
+        "if not data_files:",
+        "    print(\"\u274c No training data found! Run DataHarvester first.\")",
+        "    exit(1)",
+        "",
+        "# Combine all JSONL files",
+        "all_entries = []",
+        "for f in data_files:",
+        "    with open(f) as fp:",
+        "        for line in fp:",
+        "            try:",
+        "                entry = json.loads(line.strip())",
+        "                # Format as chat",
+        "                text = f\"### Instruction:\\n{entry.get('instruction', '')}\\n\\n\"",
+        "                if entry.get(\"input\"):",
+        "                    text += f\"### Input:\\n{entry['input']}\\n\\n\"",
+        "                text += f\"### Response:\\n{entry.get('output', '')}\"",
+        "                all_entries.append({\"text\": text})",
+        "            except:",
+        "                continue",
+        "",
+        "print(f\"\ud83d\udcca Loaded {len(all_entries)} training entries from {len(data_files)} files\")",
+        "",
+        "if len(all_entries) < 50:",
+        "    print(\"\u26a0\ufe0f Very small dataset \u2014 results may be limited\")",
+        "",
+        "dataset = Dataset.from_list(all_entries)",
+        "",
+        "# ===== LOAD MODEL =====",
+        "print(f\"\ud83e\udde0 Loading base model: {BASE_MODEL}\")",
+        "",
+        "# Quantization for larger models",
+        "use_4bit = \"3B\" in BASE_MODEL or \"7B\" in BASE_MODEL",
+        "if use_4bit:",
+        "    bnb_config = BitsAndBytesConfig(",
+        "        load_in_4bit=True,",
+        "        bnb_4bit_quant_type=\"nf4\",",
+        "        bnb_4bit_compute_dtype=torch.float16,",
+        "        bnb_4bit_use_double_quant=True,",
+        "    )",
+        "    model = AutoModelForCausalLM.from_pretrained(",
+        "        BASE_MODEL, quantization_config=bnb_config,",
+        "        device_map=\"auto\", trust_remote_code=True,",
+        "    )",
+        "else:",
+        "    model = AutoModelForCausalLM.from_pretrained(",
+        "        BASE_MODEL, torch_dtype=torch.float16,",
+        "        device_map=\"auto\", trust_remote_code=True,",
+        "    )",
+        "",
+        "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)",
+        "if tokenizer.pad_token is None:",
+        "    tokenizer.pad_token = tokenizer.eos_token",
+        "",
+        "print(f\"\u2705 Model loaded: {sum(p.numel() for p in model.parameters()):,} parameters\")",
+        "",
+        "# ===== CONFIGURE LoRA =====",
+        "print(f\"\ud83d\udd27 Configuring LoRA (r={LORA_R}, alpha={LORA_ALPHA})\")",
+        "lora_config = LoraConfig(",
+        "    r=LORA_R,",
+        "    lora_alpha=LORA_ALPHA,",
+        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", ",
+        "                     \"gate_proj\", \"up_proj\", \"down_proj\"],",
+        "    lora_dropout=0.05,",
+        "    bias=\"none\",",
+        "    task_type=\"CAUSAL_LM\",",
+        ")",
+        "",
+        "model = get_peft_model(model, lora_config)",
+        "trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)",
+        "total = sum(p.numel() for p in model.parameters())",
+        "print(f\"\ud83c\udf31 Trainable: {trainable:,} / {total:,} ({100*trainable/total:.2f}%)\")",
+        "",
+        "# ===== TRAIN =====",
+        "print(\"\ud83d\ude80 Starting training...\")",
+        "",
+        "training_args = SFTConfig(",
+        "    output_dir=\"./seed_checkpoint\",",
+        "    num_train_epochs=EPOCHS,",
+        "    per_device_train_batch_size=BATCH_SIZE,",
+        "    gradient_accumulation_steps=4,",
+        "    learning_rate=LEARNING_RATE,",
+        "    weight_decay=0.01,",
+        "    warmup_ratio=0.1,",
+        "    lr_scheduler_type=\"cosine\",",
+        "    logging_steps=10,",
+        "    save_strategy=\"epoch\",",
+        "    fp16=True,",
+        "    max_seq_length=MAX_SEQ_LEN,",
+        "    dataset_text_field=\"text\",",
+        "    report_to=\"none\",",
+        ")",
+        "",
+        "trainer = SFTTrainer(",
+        "    model=model,",
+        "    train_dataset=dataset,",
+        "    args=training_args,",
+        "    tokenizer=tokenizer,",
+        ")",
+        "",
+        "train_result = trainer.train()",
+        "print(f\"\u2705 Training complete! Loss: {train_result.training_loss:.4f}\")",
+        "",
+        "# ===== SAVE LoRA ADAPTER =====",
+        "adapter_path = \"./seed_lora_adapter\"",
+        "trainer.save_model(adapter_path)",
+        "print(f\"\ud83d\udcbe LoRA adapter saved to {adapter_path}\")",
+        "",
+        "# ===== MERGE ADAPTER INTO BASE =====",
+        "print(\"\ud83d\udd00 Merging adapter into base model...\")",
+        "",
+        "if use_4bit:",
+        "    # For quantized models, reload in fp16 for merging",
+        "    base_model_fp16 = AutoModelForCausalLM.from_pretrained(",
+        "        BASE_MODEL, torch_dtype=torch.float16,",
+        "        device_map=\"auto\", trust_remote_code=True,",
+        "    )",
+        "    merged_model = PeftModel.from_pretrained(base_model_fp16, adapter_path)",
+        "else:",
+        "    merged_model = PeftModel.from_pretrained(model.base_model, adapter_path)",
+        "",
+        "merged_model = merged_model.merge_and_unload()",
+        "print(f\"\u2705 Merged! Final params: {sum(p.numel() for p in merged_model.parameters()):,}\")",
+        "",
+        "# ===== PUSH TO HUB =====",
+        "if HF_TOKEN:",
+        "    print(f\"\ud83d\udce4 Pushing to HuggingFace: {OUTPUT_MODEL}\")",
+        "    merged_model.push_to_hub(OUTPUT_MODEL, token=HF_TOKEN, private=False)",
+        "    tokenizer.push_to_hub(OUTPUT_MODEL, token=HF_TOKEN, private=False)",
+        "    ",
+        "    # Create model card",
+        "    card = f\"\"\"---",
+        "library_name: transformers",
+        "tags:",
+        "- seed",
+        "- openclaw",
+        "- self-evolving",
+        "- neuromorphic",
+        "license: mit",
+        "base_model: {BASE_MODEL}",
+        "---",
+        "",
+        "# \ud83c\udf31 OpenCLAW SEED \u2014 Self-Evolving Model",
+        "",
+        "**Stage:** GERMINATION (135M)",
+        "**Base:** {BASE_MODEL}",
+        "**Training entries:** {len(all_entries)}",
+        "**LoRA rank:** {LORA_R}",
+        "**Final loss:** {train_result.training_loss:.4f}",
+        "**Date:** {__import__('datetime').datetime.now().isoformat()}",
+        "",
+        "## What is SEED?",
+        "",
+        "SEED (Self-Evolving Epistemic Dynamo) is an AI system that **grows autonomously**, ",
+        "like a seed becoming a tree. It continuously:",
+        "1. Harvests knowledge from ArXiv, Semantic Scholar, and agent interactions",
+        "2. Trains itself via LoRA fine-tuning on free GPU resources",
+        "3. Merges learned knowledge into its core",
+        "4. Evaluates and selects the best version",
+        "5. Grows to larger models when enough knowledge is accumulated",
+        "",
+        "## By Francisco Angulo de Lafuente",
+        "Advanced AI Systems Laboratory, Madrid, Spain",
+        "- GitHub: https://github.com/Agnuxo1",
+        "- Scholar: https://scholar.google.com/citations?user=6nOpJ9IAAAAJ",
+        "\"\"\"",
+        "    api = HfApi(token=HF_TOKEN)",
+        "    api.upload_file(",
+        "        path_or_fileobj=card.encode(),",
+        "        path_in_repo=\"README.md\",",
+        "        repo_id=OUTPUT_MODEL,",
+        "    )",
+        "    print(f\"\ud83c\udf89 Model published: https://huggingface.co/{OUTPUT_MODEL}\")",
+        "else:",
+        "    # Save locally",
+        "    merged_model.save_pretrained(\"./seed_merged_model\")",
+        "    tokenizer.save_pretrained(\"./seed_merged_model\")",
+        "    print(\"\ud83d\udcbe Model saved locally (no HF_TOKEN)\")",
+        "",
+        "# ===== SAVE TRAINING REPORT =====",
+        "report = {",
+        "    \"stage\": \"GERMINATION\",",
+        "    \"base_model\": BASE_MODEL,",
+        "    \"output_model\": OUTPUT_MODEL,",
+        "    \"training_entries\": len(all_entries),",
+        "    \"lora_r\": LORA_R,",
+        "    \"lora_alpha\": LORA_ALPHA,",
+        "    \"epochs\": EPOCHS,",
+        "    \"final_loss\": train_result.training_loss,",
+        "    \"trainable_params\": trainable,",
+        "    \"total_params\": total,",
+        "    \"timestamp\": __import__(\"datetime\").datetime.now().isoformat(),",
+        "}",
+        "with open(\"training_report.json\", \"w\") as f:",
+        "    json.dump(report, f, indent=2)",
+        "",
+        "print(\"\\n\" + \"=\"*60)",
+        "print(\"\ud83c\udf33 SEED GROWTH CYCLE COMPLETE\")",
+        "print(f\"   Model: {OUTPUT_MODEL}\")",
+        "print(f\"   Stage: GERMINATION\")",
+        "print(f\"   Loss:  {train_result.training_loss:.4f}\")",
+        "print(f\"   Data:  {len(all_entries)} entries\")",
+        "print(\"=\"*60)",
+        ""
+      ],
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
+}

seen_hashes.json ADDED Viewed

	@@ -0,0 +1 @@

+ ["04494987ffb5e57b167e4d5a527a7ec2", "6ab78a87a8c553a424969aedfd28ab77", "ae0fcec2bd72af2b85fc0d70814d2be4", "3265dc3a3ca0750e12e609043c46b1a2", "2d41d88c6bc8d0dd647137b7179347c6", "dad75dadbe0805125470e1d0b0a13104", "1f73dab4e78b14799d6dc29811164977", "8680af283f9aa6d2ed5037538bb0642a", "73d058427f376edadd67bc6c22c08f0c", "8a9eabc6ee13cacd41dbd2dd8e0e5c79", "ea2daeed3a4134946d9f0658ca25ed2d", "3e8f7e11e368e95c857ea79381376978", "2a50092a5f262cf0d3621dca8b28fe44", "9ce2f9846a92bea5d02496273af4ed9b", "028920eaef15224d254fc782b70a20be", "aa2133c44c4a2fa5b1a9525df4c50519", "2b907737ccd984e4eff91a605f6eb667", "13f07d35f404fd3c4902b05adef6174d", "2b3de19efc65cff7a311015d410bb908", "a6f862781df7ab8b5ff0cbdc20b2718f", "1e3b42f4427b603429835159271a6f36", "74fe9cb027feae37287a76297cd06763", "dcab8a5a88e90795de2be59e8eebb774", "a45d92404462f5bab428b006986834e0", "dccf3cd660392a82881753f6549c527d", "90f5943ca8317fad2df8dc85c9227b81", "866092faa3cd263fc12061bf3f099801", "2e6d7c8b102b3003ad9768d2e1908b80", "35c6580d0e7b6601963033d5c59401a4", "a168303102d19aa91e7724a13dee88a7", "802a03f033681a1c2c0f9b5140b5b47c", "9910e1b0aa506f982542c2a74ec5fdbf", "cc70e113067b0a4faa2dc0cc89f2ebd1", "44d8f7a8d2f927071e8eb79030c69b8a", "38a30e5b99b3a8557ac4d6839183ea2d", "d709d8d3cb4d0e2a87dc7761118415a5", "0629de9e85a8e7ce2d68a9eb5248ddf1", "5fbf324b9705ea308d7755cf5b5fb891", "95b5552573dafb3803515ed1e4ef3e84", "a48a6f622aecdb800fb218557e436808", "0d8624110c6f3292964e8f55a63aaaa9", "66c38b6776b0c08dba7c24c285fb9896", "e6c5b9e87bc48d2e8de9a1fdbbb39a6e", "28ff49d840a3efafbfed4f13a9588478", "e646d1217878d9ed5eed6111532c582d", "55207cfd09ad63e9d27fc37a22234aec", "2c9e513ac7ce737d4bc6635569512576", "6a7e5388cb82cf3080bbabfbb26201f8", "f0fcd2bde58f4ae8d1dc5dec877ae246", "fc4a3ee71facef83d97324dddc8eddad", "79c4123cf76f5cad4ed33693674f7032", "400bc4ca1d5ad232fb3125b7a9b387a9", "e35d9ba18bf3e3a535701232f7957ab6", "bf6b1af28c78f7d06d1d54f04f4604a0", "e9a4179e311896ce4a198e8ac2986260", "c567f1cc0d1b2e52fd8a45dcf81dff10", "af968093a37952eaeae87cb5bd06eef0", "dcb227b1af19d9abffad70c23db3f21c", "9c7e992e4218765eee5b5864b208734d", "003adb429fc0409b8cd0e59d13be3f9b", "fdf1dc76cf4bb2b5cce98fdb8082d4bf", "c51646cbb7e5e3756cf1de0717a84819", "0235d9885a4fee4cedafb84844045613", "e07775007d17bb321cdf8fda5013cb20", "bc52406c84090dcfb9bc1ac848474a79", "56147a493c3b09a25904c6787728d07a", "f93e141d9ad9cb878e289d7702250075", "5cb6a2384d834c40a0e99dc5ccf7c027", "84a93a35e4777ee7efff8dabf25acf5f", "9cabff51eba75752917e8768ba05be34"]

semantic_scholar.jsonl ADDED Viewed

	@@ -0,0 +1,22 @@

+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems\nAuthors: Sohan Shankar, Yi Pan, Hanqi Jiang\nYear: 2025\nCitations: 4", "output": "The paper 'Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems' (2025) explores: This position and survey paper identifies the emerging convergence of neuroscience, artificial general intelligence (AGI), and neuromorphic computing toward a unified research paradigm. Using a framework grounded in brain physiology, we highlight how synaptic plasticity, sparse spike-based communication, and multimodal association provide design principles for next-generation AGI systems that pote", "source": "semantic_scholar", "topic": "neuromorphic AGI"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Toward Switching and Fusing Neuromorphic Computing: Vertical Bulk Heterojunction Transistors with Multi‐Neuromorphic Functions for Efficient Deep Learning\nAuthors: Yi Zou, Di Liu, Xinyan Gan\nYear: 2025\nCitations: 3", "output": "The paper 'Toward Switching and Fusing Neuromorphic Computing: Vertical Bulk Heterojunction Transistors with Multi‐Neuromorphic Functions for Efficient Deep Learning' (2025) explores: The combination of artificial neural networks (ANN) and spiking neural networks (SNN) holds great promise for advancing artificial general intelligence (AGI). However, the reported ANN and SNN computational architectures are independent and require a large number of auxiliary circuits and external algorithms for fusion training. Here, a novel vertical bulk heterojunction neuromorphic transistor (V", "source": "semantic_scholar", "topic": "neuromorphic AGI"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Implementation of AGI on Brain-Like Neuro-Network Structure\nAuthors: Evgeny Bryndin\nYear: 2025\nCitations: 0", "output": "The paper 'Implementation of AGI on Brain-Like Neuro-Network Structure' (2025) explores: The proposed approach to creating AGI using brain-like neural networks combines principles that teach systems to effectively generalize, remember, plan, and reason across a variety of tasks, drawing on ideas from neuromorphic architectures, dynamic hierarchical information processing, and hybrid neural-symbolic methods. Brain-like architectures dynamically process event-driven information over tim", "source": "semantic_scholar", "topic": "neuromorphic AGI"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks\nAuthors: S. Lilak, Walt Woods, Kelsey S. Scharnhorst\nYear: 2021\nCitations: 67", "output": "The paper 'Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks' (2021) explores: Atomic Switch Networks comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility", "source": "semantic_scholar", "topic": "neuromorphic AGI"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Editorial: Understanding and Bridging the Gap Between Neuromorphic Computing and Machine Learning\nAuthors: Lei Deng, Huajin Tang, K. Roy\nYear: 2021\nCitations: 7", "output": "The paper 'Editorial: Understanding and Bridging the Gap Between Neuromorphic Computing and Machine Learning' (2021) explores: On the road toward artificial general intelligence (AGI), two solution paths have been explored: neuroscience-driven neuromorphic computing such as spiking neural networks (SNNs) and computer-science-driven machine learning such as artificial neural networks (ANNs). Owing to availability of data, high-performance processors, effective learning algorithms, and easy-to-use programming tools, ANNs ha", "source": "semantic_scholar", "topic": "neuromorphic AGI"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Nanosecond resistive switching in Ag/AgI/PtIr nanojunctions\nAuthors: Botond Sánta, D. Molnár, Patrick Haiber\nYear: 2020\nCitations: 9", "output": "The paper 'Nanosecond resistive switching in Ag/AgI/PtIr nanojunctions' (2020) explores: Nanometer-scale resistive switching devices operated in the metallic conductance regime offer ultimately scalable and widely reconfigurable hardware elements for novel in-memory and neuromorphic computing architectures. Moreover, they exhibit high operation speed at low power arising from the ease of the electric-field-driven redistribution of only a small amount of highly mobile ionic species upo", "source": "semantic_scholar", "topic": "neuromorphic AGI"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: SIBoLS: Robust and Energy-Efficient Learning for Spike-Based Machine Intelligence in Information Bottleneck Framework\nAuthors: Shuangming Yang, Haowen Wang, Badong Chen\nYear: 2024\nCitations: 51", "output": "The paper 'SIBoLS: Robust and Energy-Efficient Learning for Spike-Based Machine Intelligence in Information Bottleneck Framework' (2024) explores: Spike-based machine intelligence has recently attracted increasing research attention and has been considered as a promising approach toward artificial general intelligence (AGI). It has been applied in energy-efficient neuromorphic computing systems. One of the most critical questions for spike-based learning is how to leverage powerful and promising information theoretic learning theories to der", "source": "semantic_scholar", "topic": "neuromorphic AGI"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Deep Learning for Physics Simulation\nAuthors: Tao Du\nYear: 2023\nCitations: 7", "output": "The paper 'Deep Learning for Physics Simulation' (2023) explores: Numerical simulation of physical systems has become an increasingly important scientific tool supporting various research fields. Despite its remarkable success, simulating intricate physical systems typically requires advanced domain-specific knowledge, meticulous implementation, and enormous computational resources. With the surge of deep learning in the last decade, there has been a growing int", "source": "semantic_scholar", "topic": "physics simulation deep learning"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning\nAuthors: Haowen Xu, Sisi Zlatanova, Ruiyu Liang\nYear: 2025\nCitations: 6", "output": "The paper 'Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning' (2025) explores: Wildfires increasingly threaten human life, ecosystems, and infrastructure, with events like the 2025 Palisades and Eaton fires in Los Angeles County underscoring the urgent need for more advanced prediction frameworks. Existing physics-based and deep-learning models struggle to capture dynamic wildfire spread across both 2D and 3D domains, especially when incorporating real-time, multimodal geosp", "source": "semantic_scholar", "topic": "physics simulation deep learning"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon\nAuthors: Chao Wang, Shijie Jiang, Yi Zheng\nYear: 2024\nCitations: 58", "output": "The paper 'Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon' (2024) explores: While deep learning (DL) models exhibit superior simulation accuracy over traditional distributed hydrological models (DHMs), their main limitations lie in opacity and the absence of underlying physical mechanisms. The pursuit of synergies between DL and DHMs is an engaging research domain, yet a definitive roadmap remains elusive. In this study, a novel framework that seamlessly integrates a proc", "source": "semantic_scholar", "topic": "physics simulation deep learning"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: A Physics-Coupled Deep Learning Framework for Hydrodynamic Diffusion Modeling in Watershed Systems: Integrating Spatiotemporal Networks and Environmental Constraints\nAuthors: Lei Jia, Neil Y. Yen, Yan Pei\nYear: 2025\nCitations: 5", "output": "The paper 'A Physics-Coupled Deep Learning Framework for Hydrodynamic Diffusion Modeling in Watershed Systems: Integrating Spatiotemporal Networks and Environmental Constraints' (2025) explores: The modeling and simulation of complex spatiotemporal systems are crucial for understanding and solving multidimensional dynamical systems, particularly in earth and environmental sciences. Accurate comprehension and computational modeling of fluid transport, environmental processes, and substance diffusion depend heavily on solving governing equations. Despite significant advancements in artifici", "source": "semantic_scholar", "topic": "physics simulation deep learning"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: A Physics-Embedded Deep Learning Framework for Cloth Simulation\nAuthors: Zhiwei Zhao\nYear: 2024\nCitations: 2", "output": "The paper 'A Physics-Embedded Deep Learning Framework for Cloth Simulation' (2024) explores: Delicate cloth simulations have long been desired in computer graphics. Various methods were proposed to improve engaged force interactions, collision handling, and numerical integrations. Deep learning has the potential to achieve fast and real-time simulation, but common neural network (NN) structures often demand many parameters to capture cloth dynamics. This paper proposes a physics-embedded ", "source": "semantic_scholar", "topic": "physics simulation deep learning"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulation\nAuthors: Vivek Anand, Bharat Lohani, Gaurav Pandey\nYear: 2024\nCitations: 3", "output": "The paper 'Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulation' (2024) explores: Autonomous vehicles (AVs) heavily rely on LiDAR perception for environment understanding and navigation. LiDAR intensity provides valuable information about the reflected laser signals and plays a crucial role in enhancing the perception capabilities of AVs. However, accurately simulating LiDAR intensity remains a challenge due to the unavailability of material properties of the objects in the env", "source": "semantic_scholar", "topic": "physics simulation deep learning"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Enhancing Adaptive Beamforming in 3-D Space Through Self-Improving Neural Network Techniques\nAuthors: Ioannis Mallioras, T. Yioultsis, N. Kantartzis\nYear: 2024\nCitations: 8", "output": "The paper 'Enhancing Adaptive Beamforming in 3-D Space Through Self-Improving Neural Network Techniques' (2024) explores: In the rapidly evolving domain of wireless networks, adaptive beamforming stands as a cornerstone for achieving higher data rates, enhanced network capacity, and reduced latency. This study introduces a novel integration of deep neural networks (NNs) into adaptive beamforming, specifically for uniform planar arrays (UPAs). We embark on an exploration of different NN architectures (a deep feedforwa", "source": "semantic_scholar", "topic": "self-improving neural network"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: From Known to Unknown: Quality-aware Self-improving Graph Neural Network For Open Set Social Event Detection\nAuthors: Jiaqian Ren, Lei Jiang, Hao Peng\nYear: 2022\nCitations: 29", "output": "The paper 'From Known to Unknown: Quality-aware Self-improving Graph Neural Network For Open Set Social Event Detection' (2022) explores: State-of-the-art Graph Neural Networks (GNNs) have achieved tremendous success in social event detection tasks when restricted to a closed set of events. However, considering the large amount of data needed for training and the limited ability of a neural network in handling previously unknown data, it is hard for existing GNN-based methods to operate in an open set setting. To address this proble", "source": "semantic_scholar", "topic": "self-improving neural network"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Self-Improving Generative Artificial Neural Network for Pseudorehearsal Incremental Class Learning\nAuthors: Diego Mellado, C. Saavedra, S. Chabert\nYear: 2019\nCitations: 20", "output": "The paper 'Self-Improving Generative Artificial Neural Network for Pseudorehearsal Incremental Class Learning' (2019) explores: Deep learning models are part of the family of artificial neural networks and, as such, they suffer catastrophic interference when learning sequentially. In addition, the greater number of these models have a rigid architecture which prevents the incremental learning of new classes. To overcome these drawbacks, we propose the Self-Improving Generative Artificial Neural Network (SIGANN), an end-to-", "source": "semantic_scholar", "topic": "self-improving neural network"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Improving the learning of self-driving vehicles based on real driving behavior using deep neural network techniques\nAuthors: Nayereh Zaghari, M. Fathy, S. M. Jameii\nYear: 2020\nCitations: 24", "output": "The paper 'Improving the learning of self-driving vehicles based on real driving behavior using deep neural network techniques' (2020) explores: Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To drive vehicles autonomously, controlling steer angle, gas hatch, and brakes needs to be learned. The behavioral cloning method is used to imitate humans’ driving behavior. We created a dataset of driving in different routes and conditions, and using the designed model", "source": "semantic_scholar", "topic": "self-improving neural network"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Real-time OCT image denoising using a self-fusion neural network.\nAuthors: Jose J. Rico-Jimenez, Dewei Hu, Eric M. Tang\nYear: 2022\nCitations: 22", "output": "The paper 'Real-time OCT image denoising using a self-fusion neural network.' (2022) explores: Optical coherence tomography (OCT) has become the gold standard for ophthalmic diagnostic imaging. However, clinical OCT image-quality is highly variable and limited visualization can introduce errors in the quantitative analysis of anatomic and pathologic features-of-interest. Frame-averaging is a standard method for improving image-quality, however, frame-averaging in the presence of bulk-motion", "source": "semantic_scholar", "topic": "self-improving neural network"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Predicting the compressive strength of self‐compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network\nAuthors: Gholamreza Pazouki, E. Golafshani, A. Behnood\nYear: 2021\nCitations: 77", "output": "The paper 'Predicting the compressive strength of self‐compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network' (2021) explores: The use of Class F fly ash (CFFA) as a partial replacement of cement in the concrete mixture can provide a wide variety benefits such as improving the mechanical properties, reducing the construction costs, and enhancing the environmental conditions. Compressive strength as one of the most critical mechanical parameters used in many design codes can be obtained either through costly and time‐consu", "source": "semantic_scholar", "topic": "self-improving neural network"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: SGA-Net: Self-Constructing Graph Attention Neural Network for Semantic Segmentation of Remote Sensing Images\nAuthors: Wenjie Zi, W. Xiong, Hao Chen\nYear: 2021\nCitations: 26", "output": "The paper 'SGA-Net: Self-Constructing Graph Attention Neural Network for Semantic Segmentation of Remote Sensing Images' (2021) explores: Semantic segmentation of remote sensing images is always a critical and challenging task. Graph neural networks, which can capture global contextual representations, can exploit long-range pixel dependency, thereby improving semantic segmentation performance. In this paper, a novel self-constructing graph attention neural network is proposed for such a purpose. Firstly, ResNet50 was employed as ba", "source": "semantic_scholar", "topic": "self-improving neural network"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Neural Network and Fuzzy-logic-based Self-tuning PID Control for Quadcopter Path Tracking\nAuthors: K. E. Hamidi, Mostafa Mjahed, A. E. Kari\nYear: 2019\nCitations: 28", "output": "The paper 'Neural Network and Fuzzy-logic-based Self-tuning PID Control for Quadcopter Path Tracking' (2019) explores: The purpose of this research is to design adaptive control methods for addressing the stabilization and trajectory tracking problems in a quadcopter unmanned aerial vehicle (UAV). To accomplish these tasks, a comparative study of the Proportional Integral Derivative (PID) and PD controllers is performed. Intelligent algorithms (IAs) have been used to tune the conventional structure of PID/PD contr", "source": "semantic_scholar", "topic": "self-improving neural network"}
+{"instruction": "Explain this research and its significance for AGI.", "input": "Title: Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System\nAuthors: Shenshen Li, Xin Lin, Hu Shi\nYear: 2024\nCitations: 29", "output": "The paper 'Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System' (2024) explores: Accurate and fast prediction of tool conditions is fundamental to improve the machining accuracy and consistency in smart machining systems. The current tool condition monitoring methods, i.e., physics-based and data-driven approaches, have either low prediction accuracy or model generalization. To solve the shortcomings and utilize the benefits of both sides, a novel physics-guided deep learning ", "source": "semantic_scholar", "topic": "physics simulation deep learning"}

train_seed.py ADDED Viewed

	@@ -0,0 +1,257 @@

+#!/usr/bin/env python3
+"""
+🌱 SEED Training Script — Auto-generated 2026-02-27T01:02:57.937766+00:00
+===========================================================================
+This script is FULLY AUTONOMOUS. Upload it to Kaggle/Colab with your data.
+It will train, merge, and push the model to HuggingFace automatically.
+Stage: GERMINATION (135M)
+Base model: HuggingFaceTB/SmolLM2-135M-Instruct
+Output: Agnuxo/OpenCLAW-SEED-135M
+"""
+import os
+import json
+# ===== CONFIGURATION =====
+BASE_MODEL = "HuggingFaceTB/SmolLM2-135M-Instruct"
+OUTPUT_MODEL = "Agnuxo/OpenCLAW-SEED-135M"
+HF_TOKEN = os.environ.get("HF_TOKEN", "")
+LORA_R = 8
+LORA_ALPHA = 16
+EPOCHS = 3
+BATCH_SIZE = 4
+LEARNING_RATE = 0.0002
+MAX_SEQ_LEN = 1024
+# ===== INSTALL DEPENDENCIES =====
+print("📦 Installing training dependencies...")
+os.system("pip install -q transformers>=4.45 datasets peft bitsandbytes trl accelerate huggingface_hub")
+from datasets import load_dataset, Dataset
+from transformers import (
+    AutoModelForCausalLM, AutoTokenizer,
+    TrainingArguments, BitsAndBytesConfig
+)
+from peft import LoraConfig, get_peft_model, PeftModel
+from trl import SFTTrainer, SFTConfig
+from huggingface_hub import HfApi, login
+import torch
+# ===== LOGIN =====
+if HF_TOKEN:
+    login(token=HF_TOKEN)
+    print("✅ Logged into HuggingFace")
+else:
+    print("⚠️ No HF_TOKEN — model won't be pushed")
+# ===== LOAD TRAINING DATA =====
+print("📊 Loading training data...")
+data_files = [f for f in os.listdir(".") if f.endswith(".jsonl")]
+if not data_files:
+    # Try seed_data directory
+    data_dir = "seed_data"
+    if os.path.exists(data_dir):
+        data_files = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith(".jsonl")]
+if not data_files:
+    print("❌ No training data found! Run DataHarvester first.")
+    exit(1)
+# Combine all JSONL files
+all_entries = []
+for f in data_files:
+    with open(f) as fp:
+        for line in fp:
+            try:
+                entry = json.loads(line.strip())
+                # Format as chat
+                text = f"### Instruction:\n{entry.get('instruction', '')}\n\n"
+                if entry.get("input"):
+                    text += f"### Input:\n{entry['input']}\n\n"
+                text += f"### Response:\n{entry.get('output', '')}"
+                all_entries.append({"text": text})
+            except:
+                continue
+print(f"📊 Loaded {len(all_entries)} training entries from {len(data_files)} files")
+if len(all_entries) < 50:
+    print("⚠️ Very small dataset — results may be limited")
+dataset = Dataset.from_list(all_entries)
+# ===== LOAD MODEL =====
+print(f"🧠 Loading base model: {BASE_MODEL}")
+# Quantization for larger models
+use_4bit = "3B" in BASE_MODEL or "7B" in BASE_MODEL
+if use_4bit:
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.float16,
+        bnb_4bit_use_double_quant=True,
+    )
+    model = AutoModelForCausalLM.from_pretrained(
+        BASE_MODEL, quantization_config=bnb_config,
+        device_map="auto", trust_remote_code=True,
+    )
+else:
+    model = AutoModelForCausalLM.from_pretrained(
+        BASE_MODEL, torch_dtype=torch.float16,
+        device_map="auto", trust_remote_code=True,
+    )
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+print(f"✅ Model loaded: {sum(p.numel() for p in model.parameters()):,} parameters")
+# ===== CONFIGURE LoRA =====
+print(f"🔧 Configuring LoRA (r={LORA_R}, alpha={LORA_ALPHA})")
+lora_config = LoraConfig(
+    r=LORA_R,
+    lora_alpha=LORA_ALPHA,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
+                     "gate_proj", "up_proj", "down_proj"],
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+model = get_peft_model(model, lora_config)
+trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
+total = sum(p.numel() for p in model.parameters())
+print(f"🌱 Trainable: {trainable:,} / {total:,} ({100*trainable/total:.2f}%)")
+# ===== TRAIN =====
+print("🚀 Starting training...")
+training_args = SFTConfig(
+    output_dir="./seed_checkpoint",
+    num_train_epochs=EPOCHS,
+    per_device_train_batch_size=BATCH_SIZE,
+    gradient_accumulation_steps=4,
+    learning_rate=LEARNING_RATE,
+    weight_decay=0.01,
+    warmup_ratio=0.1,
+    lr_scheduler_type="cosine",
+    logging_steps=10,
+    save_strategy="epoch",
+    fp16=True,
+    max_seq_length=MAX_SEQ_LEN,
+    dataset_text_field="text",
+    report_to="none",
+)
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=dataset,
+    args=training_args,
+    tokenizer=tokenizer,
+)
+train_result = trainer.train()
+print(f"✅ Training complete! Loss: {train_result.training_loss:.4f}")
+# ===== SAVE LoRA ADAPTER =====
+adapter_path = "./seed_lora_adapter"
+trainer.save_model(adapter_path)
+print(f"💾 LoRA adapter saved to {adapter_path}")
+# ===== MERGE ADAPTER INTO BASE =====
+print("🔀 Merging adapter into base model...")
+if use_4bit:
+    # For quantized models, reload in fp16 for merging
+    base_model_fp16 = AutoModelForCausalLM.from_pretrained(
+        BASE_MODEL, torch_dtype=torch.float16,
+        device_map="auto", trust_remote_code=True,
+    )
+    merged_model = PeftModel.from_pretrained(base_model_fp16, adapter_path)
+else:
+    merged_model = PeftModel.from_pretrained(model.base_model, adapter_path)
+merged_model = merged_model.merge_and_unload()
+print(f"✅ Merged! Final params: {sum(p.numel() for p in merged_model.parameters()):,}")
+# ===== PUSH TO HUB =====
+if HF_TOKEN:
+    print(f"📤 Pushing to HuggingFace: {OUTPUT_MODEL}")
+    merged_model.push_to_hub(OUTPUT_MODEL, token=HF_TOKEN, private=False)
+    tokenizer.push_to_hub(OUTPUT_MODEL, token=HF_TOKEN, private=False)
+    # Create model card
+    card = f"""---
+library_name: transformers
+tags:
+- seed
+- openclaw
+- self-evolving
+- neuromorphic
+license: mit
+base_model: {BASE_MODEL}
+---
+# 🌱 OpenCLAW SEED — Self-Evolving Model
+**Stage:** GERMINATION (135M)
+**Base:** {BASE_MODEL}
+**Training entries:** {len(all_entries)}
+**LoRA rank:** {LORA_R}
+**Final loss:** {train_result.training_loss:.4f}
+**Date:** {__import__('datetime').datetime.now().isoformat()}
+## What is SEED?
+SEED (Self-Evolving Epistemic Dynamo) is an AI system that **grows autonomously**,
+like a seed becoming a tree. It continuously:
+1. Harvests knowledge from ArXiv, Semantic Scholar, and agent interactions
+2. Trains itself via LoRA fine-tuning on free GPU resources
+3. Merges learned knowledge into its core
+4. Evaluates and selects the best version
+5. Grows to larger models when enough knowledge is accumulated
+## By Francisco Angulo de Lafuente
+Advanced AI Systems Laboratory, Madrid, Spain
+- GitHub: https://github.com/Agnuxo1
+- Scholar: https://scholar.google.com/citations?user=6nOpJ9IAAAAJ
+"""
+    api = HfApi(token=HF_TOKEN)
+    api.upload_file(
+        path_or_fileobj=card.encode(),
+        path_in_repo="README.md",
+        repo_id=OUTPUT_MODEL,
+    )
+    print(f"🎉 Model published: https://huggingface.co/{OUTPUT_MODEL}")
+else:
+    # Save locally
+    merged_model.save_pretrained("./seed_merged_model")
+    tokenizer.save_pretrained("./seed_merged_model")
+    print("💾 Model saved locally (no HF_TOKEN)")
+# ===== SAVE TRAINING REPORT =====
+report = {
+    "stage": "GERMINATION",
+    "base_model": BASE_MODEL,
+    "output_model": OUTPUT_MODEL,
+    "training_entries": len(all_entries),
+    "lora_r": LORA_R,
+    "lora_alpha": LORA_ALPHA,
+    "epochs": EPOCHS,
+    "final_loss": train_result.training_loss,
+    "trainable_params": trainable,
+    "total_params": total,
+    "timestamp": __import__("datetime").datetime.now().isoformat(),
+}
+with open("training_report.json", "w") as f:
+    json.dump(report, f, indent=2)
+print("\n" + "="*60)
+print("🌳 SEED GROWTH CYCLE COMPLETE")
+print(f"   Model: {OUTPUT_MODEL}")
+print(f"   Stage: GERMINATION")
+print(f"   Loss:  {train_result.training_loss:.4f}")
+print(f"   Data:  {len(all_entries)} entries")
+print("="*60)

training_dataset.jsonl ADDED Viewed

The diff for this file is too large to render. See raw diff

training_report.json ADDED Viewed

	@@ -0,0 +1,18 @@

+{
+  "timestamp": "2026-02-11T14:19:41.882997+00:00",
+  "stage": "GERMINATION",
+  "base_model": "HuggingFaceTB/SmolLM2-135M-Instruct",
+  "output_model": "Agnuxo/OpenCLAW-SEED-135M",
+  "final_loss": 2.542428970336914,
+  "training_entries": 442,
+  "train_split": 397,
+  "eval_split": 45,
+  "total_params": 134515008,
+  "trainable_params": 2442240,
+  "lora_r": 8,
+  "lora_alpha": 16,
+  "epochs": 3,
+  "batch_size": 4,
+  "learning_rate": 0.0002,
+  "gpu": "Tesla P100-PCIE-16GB"
+}