README.md

metadata

pretty_name: Solana Program Types
license: other
license_name: cc-by-4.0-annotations
language:
  - en
task_categories:
  - text-classification
size_categories:
  - 10K<n<100K
tags:
  - solana
  - blockchain
  - smart-contracts
  - bytecode
  - program-classification
  - launchpad
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: validation
        path: data/validation-*.parquet
      - split: test
        path: data/test-*.parquet

Solana Program Types

22,743 Solana mainnet programs (BPF upgradeable loader), one row per program, each labelled with a two-level type (domain / type, 34 types) and capability flags. The inputs are static features extracted from each program's deployed bytecode (instruction names, source paths, crates, embedded program IDs, strings) plus sampled on-chain behaviour.

The main use is recognising program types from deploy-time bytecode, especially token launchpads, including launchpads deployed after a model was trained. The other 33 types (arbitrage/MEV bots, bridges, vaults, AMMs, games, escrow, …) are labelled with the same rubric.

• v1.3 (2026-10-21): about twice v1.2. It adds a third collection wave (11.5k programs still deployed on mainnet), so it now covers nearly every upgradeable program active in the last 90 days at collection time. 510 launchpads in 481 code groups.
• v1.2 (2026-10-15): v1.1 plus 85 programs caught by a live shadow monitor.
• Labels are agent-made (silver). See "Label sources" and "Limitations".

Quick start

from datasets import load_dataset
ds = load_dataset("ethduke/solana-program-types")
row = ds["train"][0]
print(row["type"], row["domain"], row["card"][:500])

Label sources

Deterministic rules over on-chain behaviour vote first. Then a cheap LLM annotator labels every card, and Claude Sonnet adjudicates launchpads, rule-vs-annotator disagreements, launch-word safety-net cases and (in waves 1–2 and part of wave 3) bot and token-issuance positives. Owner revisions override everything (is_gold). label_source and annotators record the provenance of every row.

final label from	rows	rows with a known type
GLM-5.3-flash annotator (wave 3)	9,750	7,105
GLM-5.3 annotator (wave 2)	6,561	4,251
Claude Sonnet 5.5 adjudicator	5,251	4,700
Claude Haiku 5.5 annotator (wave 1)	1,027	833
Claude Sonnet 5.5 annotator (live rounds)	85	68
registry	43	43
owner	26	26

Every launchpad label comes from Sonnet, the registry or the owner, never from a cheap annotator alone.

Types

type	rows
unknown	5717
other	1582
arbitrage_mev	1319
bridge	1263
launch_bot	1089
yield_vault	1079
payments_fees	895
escrow	884
generic_bot	863
stake_pool	852
game	756
nft_mint	635
aggregator	529
ico_presale	528
launchpad	510
vesting_locker	490
prediction_market	454
airdrop_claim	437
amm	430
nft_marketplace	296
lending_market	296
governance_dao	254
oracle	254
token_factory	234
copy_volume_bot	183
multisig_wallet	181
orderbook	154
perps	135
naming_identity	128
dca_limit_order	88
social	75
cdp_stablecoin	63
options	52
liquid_staking	38

Splits

• split: 80/10/10 by clone_group; programs with the same binary or the same instruction-name set never cross splits.
• time_split: newest 15% of deploy slots are test, to evaluate on new deployments.

Columns

• Labels: domain, type, capabilities, confidence, label_source, is_gold, owner_decision, rule_type, rule_votes, annotators, annotator_types, annotation_evidence.
• Static (from the ELF bytes, no execution): ix_names, app_paths, crates, messages, embedded_refs, embedded_ref_categories, launch_word_hits, top_words, framework, n_strings.
• Behaviour (sampled finalized transactions): b_* columns; maps are JSON strings.
• Operator pattern: t_* columns (fee payers, AMMs per tx, launchpad/oracle CPIs, decimals of created mints).
• card: the text the annotators saw. Strings inside it are untrusted data from binaries.
• Bytecode is not included; fetch it by binary_sha256 / program_id from mainnet.

Benchmarks (static, deploy-time inputs only)

Two model families on identical rows:

• Trees: gradient-boosted trees over 3,000 bytecode tokens.
• 8B: Qwen3-8B with a LoRA adapter (r = 16, one epoch). It reads the static card and answers with one code letter per type; the next-token probabilities over the 34 letters give the type distribution, with one temperature for calibration. Model: ethduke/solana-program-types-qwen3-8b-lora.

v1.3: newest 15% of deployments, never trained on (2,463 programs, 109 launchpads)

model	launchpad PR-AUC	recall @ precision ≥ 0.9	P / R at 0.5	type accuracy	type macro-F1
gradient-boosted trees	0.695	0.22	0.71 / 0.51	0.527	0.434
Qwen3-8B LoRA	0.879	0.69	0.79 / 0.84	0.653	0.649
average of both	0.871	0.61	0.86 / 0.74	0.662	0.646

Paired bootstrap of the PR-AUC gap (8B − trees): +0.18, 95% CI [+0.11, +0.25]. Per-type F1 improves most for bridges, stake pools, oracles, perps, payments and aggregators. Bot sub-types (generic, copy-volume and launch bots) stay hard from bytecode alone.

Earlier results

• v1.1, newest deployments (53 launchpads): PR-AUC 0.873 vs 0.763 for the trees (+0.11, CI [+0.01, +0.21]).
• v1.1, unseen launchpad families (43 families held out entirely): 0.911 vs 0.886, a gap within noise.
• Live shadow test (85 programs scored at deploy time, then labelled): launchpad precision 0.62 and recall 0.83 for the 8B, against 0.50 / 0.50 for the trees.

Suggested evaluation

• Use split for in-distribution results, time_split == "test" (newest 15% by deploy slot) for "new deployment" results, and hold out whole launchpad families (family_hint, source program names or clone_group) for generalisation.
• Exclude type == "unknown": it means "no evidence", not a class.
• Report launchpad PR-AUC and recall at fixed precision. Positives are rare (about 3% of known rows).

Limitations

• Silver labels. Most labels are LLM-agent labels; the owner reviewed launchpad-related decisions only, and no label-error rate has been measured yet.
• Weakest labels: wave 3 bots and tokens.
  • 1,381 wave-3 bot and token-issuance programs were not adjudicated and keep GLM-5.3-flash labels, including 830 of the 3,454 trading_bot/* rows.
  • On a 60-case check, GLM-5.3-flash matched Sonnet's type 77% of the time, against 87% for a second Sonnet pass.
  • Expect noise between generic_bot, copy_volume_bot, arbitrage_mev, launch_bot and aggregator.
• A launchpad label is a lead, not a validated deployment, except registry and owner rows.
• Behaviour features are partly circular. Rule labels were derived from the b_* / t_* columns.

Privacy and redaction

The strings come from public mainnet bytecode. Before release, these were redacted (redact_public.py):

• API-key values compiled into programs (api_key=[redacted]);
• personal email addresses (published security and contact addresses kept);
• developer usernames in build paths (/home/<user>/);
• 64-character hex strings.

Long base58 strings were checked against ed25519 keypairs; none was a private key.

License

The labels, annotations, splits and derived features are released under CC BY 4.0. The extracted strings and identifiers are factual data read from public Solana mainnet programs; this release grants no license to those programs' code, which belongs to their authors.

Citation

@misc{solana_program_types_2026,
  title  = {Solana Program Types: a bytecode-level dataset of Solana mainnet programs},
  author = {ethduke},
  year   = {2026},
  url    = {https://huggingface.co/datasets/ethduke/solana-program-types}
}