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End of preview. Expand in Data Studio

Solana Clawd Realtime Research Instruct

Instruction-tuning dataset generated by scripts/realtime_dataset_ingest.py from submitted PDFs, notebooks, parquet QA rows, JSON/JSONL files, and local reference text.

Contents

  • Total examples: 29058
  • Train/eval/test: 26152 / 1452 / 1454
  • Sources: 28
  • Duplicate examples removed: 0
  • Duplicate files skipped: 2
  • Secret-like records skipped: 296

Format

Each row uses OpenAI/Hugging Face chat messages:

{"messages": [{"role": "system", "content": "..."}, {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}

Rows also include non-training metadata columns: source, source_type, source_sha256, record_id, example_sha256, tags, and metadata.

Sources

  • 2410.21169v5.pdf (pdf, 57 examples)
  • 2412.04913v3.pdf (pdf, 8 examples)
  • 2412.07591v2.pdf (pdf, 8 examples)
  • 2512.01112v3.pdf (pdf, 104 examples)
  • 2512.06505v4.pdf (pdf, 13 examples)
  • 2512.19113v2.pdf (pdf, 37 examples)
  • 2512.22476v1.pdf (pdf, 68 examples)
  • 2601.10812v1.pdf (pdf, 37 examples)
  • 2601.17008v1.pdf (pdf, 19 examples)
  • 2602.00776v1.pdf (pdf, 29 examples)
  • 2602.14860v1.pdf (pdf, 30 examples)
  • 2603.10092v1.pdf (pdf, 27 examples)
  • 2604.01431v1.pdf (pdf, 19 examples)
  • 2605.05089v1.pdf (pdf, 32 examples)
  • 2605.05878v1.pdf (pdf, 16 examples)
  • 2605.10400v1.pdf (pdf, 87 examples)
  • 2605.10428v1.pdf (pdf, 48 examples)
  • 2605.12151v2.pdf (pdf, 9 examples)
  • 2605.29174v1 (1).pdf (pdf, 24 examples)
  • 2605.29174v1.pdf (pdf, 0 examples)
  • 2606.08232v1 (1).pdf (pdf, 11 examples)
  • 2606.08232v1.pdf (pdf, 0 examples)
  • analysing-crypto-charts-like-pro.ipynb (notebook, 47 examples)
  • analysis-of-smart-contracts-in-blockchain.ipynb (notebook, 22 examples)
  • solana-prediction.ipynb (notebook, 53 examples)
  • test-00000-of-00001.parquet (parquet, 1407 examples)
  • train-00000-of-00001.parquet (parquet, 26843 examples)
  • SKILL.md (text, 3 examples)

Source Inventory

PDF Research Sources

File What it contains Pages Examples Identifier
2410.21169v5.pdf Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction 46 57 https://arxiv.org/abs/2410.21169v5
2412.04913v3.pdf Bridging Culture and Finance: A Multimodal Analysis of Memecoins in the Web3 Ecosystem 4 8 -
2412.07591v2.pdf CoinCLIP: A Multimodal Framework for Assessing Viability in Web3 Memecoins 4 8 -
2512.01112v3.pdf Autodeleveraging: Impossibilities and Optimization 103 104 https://arxiv.org/abs/2512.01112v3
2512.06505v4.pdf Amortizing Perpetual Options 12 13 https://arxiv.org/abs/2512.06505v4
2512.19113v2.pdf A Unified Framework and Comparative Study of Decentralized Finance Derivatives Protocols 36 37 https://arxiv.org/abs/2512.19113v2
2512.22476v1.pdf AutoQuant: An Auditable Expert-System Framework for Execution-Constrained Auto-Tuning in Cryptocurrency Perpetual Futures 67 68 https://arxiv.org/abs/2512.22476v1
2601.10812v1.pdf Optimal Liquidation of Perpetual Contracts 36 37 https://arxiv.org/abs/2601.10812v1
2601.17008v1.pdf Bayesian Robust Financial Trading with Adversarial Synthetic Market Data 12 19 https://arxiv.org/abs/2601.17008v1
2602.00776v1.pdf Explainable Patterns in Cryptocurrency Microstructure 28 29 https://arxiv.org/abs/2602.00776v1
2602.14860v1.pdf Predicting the success of new crypto-tokens: the Pump.fun case 29 30 https://arxiv.org/abs/2602.14860v1
2603.10092v1.pdf Execution Is the New Attack Surface: Survivability-Aware Agentic Crypto Trading with OpenClaw-Style Local Executors 26 27 https://arxiv.org/abs/2603.10092v1
2604.01431v1.pdf Do Prediction Markets Forecast Cryptocurrency Volatility? Evidence from Kalshi Macro Contracts 14 19 https://arxiv.org/abs/2604.01431v1
2605.05089v1.pdf Dynamic Collateral Control for Permissionless Spot Perpetual Basis Trading 23 32 https://arxiv.org/abs/2605.05089v1
2605.05878v1.pdf Agentic, Context-Aware Risk Intelligence in the Internet of Value 15 16 https://arxiv.org/abs/2605.05878v1
2605.10400v1.pdf Resolution-Aware Perpetual Futures on Binary Prediction Markets: An Empirical Risk-Design Framework Using Polymarket Data 86 87 https://arxiv.org/abs/2605.10400v1
2605.10428v1.pdf A Taxonomy of Event-Linked Perpetual Futures: Variant Designs Beyond the Single-Market Binary Case 47 48 https://arxiv.org/abs/2605.10428v1
2605.12151v2.pdf RED-2400: A Public Benchmark of Algorithmically-Rejected Trading Events with Outcome Labels 8 9 -
2605.29174v1 (1).pdf Paper Agents, Paper Gains: An Empirical Analysis of DeFi Investment Agents 23 24 https://arxiv.org/abs/2605.29174v1
2605.29174v1.pdf duplicate file skipped - 0 -
2606.08232v1 (1).pdf Hour-Aware Adaptive Risk Management for Autonomous Memecoin Trading: A Multi-Layer Intelligence Framework 11 11 -
2606.08232v1.pdf duplicate file skipped - 0 -

Notebook Sources

File Cells Kernel Examples
analysing-crypto-charts-like-pro.ipynb 53 Python 3 47
analysis-of-smart-contracts-in-blockchain.ipynb 27 Python 3 22
solana-prediction.ipynb 67 Python 3 53

Parquet QA Sources

File Rows Columns Examples
test-00000-of-00001.parquet 1426 question, answer, chunk 1407
train-00000-of-00001.parquet 27092 question, answer, chunk 26843

Text and Skill Sources

File Type Details Examples
SKILL.md text 10506 3

Google Document Processing

The ingestion script supports Google-backed PDF extraction:

  • pdf_extractor: auto tries Document AI when OAuth/ADC credentials are present, then Gemini when GEMINI_API_KEY or GOOGLE_API_KEY is present, then local pypdf.
  • Document AI endpoint: https://us-documentai.googleapis.com/v1/projects/1013652097839/locations/us/processors/29a612e70aee73e1:process
  • Document AI field mask: text,pages.pageNumber,pages.detectedLanguages,pages.imageQualityScores
  • Document AI billing labels: pipeline=solana-clawd, dataset=realtime-research, client=clawd
  • Gemini model: gemini-2.5-flash

Document AI's ProcessDocument endpoint normally requires Google Cloud OAuth or Application Default Credentials. API keys are used by the Gemini extractor.

Reproduce

cd /path/to/solana-clawd/ai-training
python3 scripts/realtime_dataset_ingest.py --config configs/realtime_dataset_config.yaml
python3 scripts/realtime_dataset_ingest.py --input my.pdf my.json --push
python3 scripts/realtime_dataset_ingest.py --pdf-extractor gemini --input my.pdf
python3 scripts/realtime_dataset_ingest.py --pdf-extractor documentai --input my.pdf

Use watch mode for drop-folder style updates:

python3 scripts/realtime_dataset_ingest.py --watch-dir data/incoming --watch --push

Safety Notes

The builder filters high-confidence API keys, private keys, and token patterns before writing rows. It does not publish local absolute paths in dataset rows. Review data/realtime_research_dataset_manifest.json before public release.

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