--- license: cc-by-4.0 language: - en task_categories: - tabular-classification pretty_name: OracleEconLab Oracle Incentives and Accountability v1 size_categories: - n<1K tags: - blockchain - oracle - economics - trustworthy-ai - tabular configs: - config_name: abc_dictionary data_files: data/abc_dictionary.parquet - config_name: uma_real_episode data_files: data/uma_real_episode.parquet - config_name: uma_real_episode_abc data_files: data/uma_real_episode_abc.parquet - config_name: uma_public_rpc_episode data_files: data/uma_public_rpc_episode.parquet - config_name: uma_public_rpc_abc data_files: data/uma_public_rpc_abc.parquet - config_name: uma_decisions data_files: data/uma_decisions.parquet - config_name: uma_decision_splits data_files: data/uma_decision_splits.parquet - config_name: decision_evidence data_files: data/decision_evidence.parquet - config_name: ai_feature_dictionary data_files: data/ai_feature_dictionary.parquet - config_name: public_ai_episodes data_files: data/public_ai_episodes.parquet - config_name: public_ai_decisions data_files: data/public_ai_decisions.parquet - config_name: public_ai_splits data_files: data/public_ai_splits.parquet - config_name: public_ai_evidence data_files: data/public_ai_evidence.parquet - config_name: public_ai_predictions data_files: data/public_ai_predictions.parquet - config_name: public_ai_metrics data_files: data/public_ai_metrics.parquet --- # OracleEconLab Oracle Incentives and Accountability v1 This review-sized draft turns fixed, public UMA protocol evidence into complete or right-censored economic lifecycles. It is a release candidate for the paper *Who Verifies Decentralized Information? Economic Incentives, Accountability, and Trustworthy AI Across Oracle Protocols.* ## Dataset configurations | Configuration | Rows | Observation unit | Purpose | |---|---:|---|---| | `abc_dictionary` | 28 | economic variable | Formulas, units, sources, missing rules and A/B/C family | | `uma_real_episode` | 1 | cross-chain episode | End-to-end Polygon OOV2 to Ethereum DVM validation | | `uma_real_episode_abc` | 28 | case-measure status | Honest mapping of every A/B/C measure to the real case | | `uma_public_rpc_episode` | 1 | cross-chain episode | Clean-room episode reconstructed without curated-ledger input | | `uma_public_rpc_abc` | 28 | case-measure status | Public-source-only A/B/C mapping and explicit unavailable states | | `uma_decisions` | 810 | challenged proposal-time decision | Minimal four-action AI evaluation | | `uma_decision_splits` | 810 | decision split assignment | Frozen chronological train/validation/test partition | | `decision_evidence` | 2430 | timestamped evidence reference | Prediction-level provenance and leakage audit | | `ai_feature_dictionary` | 16 | decision-time feature | Feature meaning, source and availability | | `public_ai_episodes` | 64 | public-source cross-chain episode | Economic lifecycle reconstructed from fixed public receipts | | `public_ai_decisions` | 64 | proposal-time decision | Credential-free public-source AI demonstration inputs | | `public_ai_splits` | 64 | split assignment | Label-free chronological 44/10/10 partition | | `public_ai_evidence` | 192 | timestamped evidence reference | Decision-time traceability for every demonstration prediction | | `public_ai_predictions` | 70 | model/action output | Four-action predictions, confidence, evidence and protocol endpoint | | `public_ai_metrics` | 7 | model evaluation | Calibration, coverage, abstention and stylized economic cost | ## A/B/C economic measure families - **A — Verification concentration (Who verifies?):** HHI, top-k share, effective number of monitors and threshold monitor count. - **B — Incentives and frictions (Why verify?):** reward/bond, capital-days, fees, Gas, verification cost and scenario-based expected private utility. - **C — Accountability outcomes (What happens?):** dispute, adjudication, transfer, realized payoff, delay and right censoring. A/B/C are measure families. They are not the project's Sample A/B/C data cohorts. Identity, decision-time, cross-chain-link and provenance fields remain supporting fields rather than being mislabeled as economic outcomes. ## Construction and provenance The fixed cutoff is `2026-06-30T23:59:59Z`. The real case begins with canonical Polygon request, proposal, dispute and settlement records, links them to the Ethereum DVM with a Grade-A deterministic match, reconciles token flows, and separates returned principal from realized reward. Every processed episode or decision contains transaction/evidence references and a deterministic provenance identifier. File integrity is recorded in `checksums.csv`. The main reconstruction commands live in the GitHub `real-release-v1.5` branch. `public_evidence/` includes the five fixed receipts, canonical headers, case specification, source checksums and verified contract-semantic rules used by the clean-room entry. This draft contains review-sized processed evidence; it does not claim that every protocol ledger can be regenerated without archive infrastructure. The executed `notebooks/uma_minimal_trustworthy_ai_demo.ipynb` loads the frozen tables, reruns the leakage audit and chronological evaluation, and writes the review-sized artifacts under `demo_outputs/`. Its reported performance is not edited or filtered after execution. The independent `public_ai_evidence/` path closes the public-source chain for a fixed 64-episode demonstration: a label-free transaction registry, 280 canonical Polygon/Ethereum receipt and header bundles, decoded economic episodes, decision-time features, model outputs and QC. Live mode uses credential-free public endpoints; offline mode deterministically replays the frozen snapshot. The model result is intentionally reported even when weak. It is a pipeline demonstration rather than evidence of production challenge quality. ## Trustworthy-AI task At the OOV2 proposal timestamp, the benchmark emits one of `Accept`, `Investigate`, `Challenge`, or `Abstain`. The endpoint is whether the later UMA protocol resolution rejects the proposal. Only decision-time fields may be used as model inputs. Columns whose names end in `_outcome_only`, the protocol label, dispute result and settlement evidence are evaluation-only and must not enter the model. The 810 observations condition on proposals that were actually challenged, Grade-A linked, price-consistent and flow-exact. The task is therefore a conditional protocol-outcome evaluation, not a causal policy for all proposals and not an independent truth classifier. ## Intended uses - audit economic variable construction and source traceability; - reproduce the real UMA episode and its payoff decomposition; - evaluate calibration, abstention, leakage and robustness on the conditional decision cohort; - study A/B/C constructs within their declared observation units. ## Uses that are not validated - production deployment of automated challenges; - causal claims that rewards or penalties improve truthfulness; - treating protocol acceptance or DVM resolution as independent factual truth; - identity, geography or intent inference from blockchain addresses; - aggregation of heterogeneous token amounts without asset-preserving conversion. ## Biases and limitations The decision cohort has selective labels because it includes only actually challenged UMA requests. It over-represents mechanisms and market adapters for which exact cross-chain and token-flow evidence is recoverable. Address-level concentration is not entity-level concentration. Off-chain investigation labor is unavailable and remains a declared scenario. Independent factual truth is available only for a small external-rule subset outside the core protocol label. ## Personal and sensitive information The data contain public pseudonymous contract/account addresses, transaction hashes and block metadata. They contain no asserted mapping from an address to a natural person and must not be used to infer human identity. RPC endpoint URLs, credentials and private keys are not included. ## Licensing Original curated tables and documentation are CC BY 4.0. Upstream contracts, APIs and third-party records remain governed by their own terms. See `DATA_LICENSE.md` and `CITATION.cff`. ## Pinned research delivery added 10 October 2026 The existing root-level `data/` and public-evidence directories remain the previously released demonstration resource. Two separately named archives now support the revised agentic-audit research: - `atlas/selected-inputs/2026-10-10/`: four fixed curated UMA source tables used by research cohort, feature and label reconstruction. This is a **selected Atlas snapshot**, not the complete 67-file candidate Atlas or a claim that its known semantic problems are resolved. - `research/agentic-audit/2026-10-10/`: experiment-specific 810-case cohort and chronological splits, frozen evidence, original Qwen response records, additional experiments, archived failed/interrupted/superseded attempts, 73 reference CSVs, 18 numeric LaTeX tables, exact prompts and eight original figures. Historical GPT records are separate provenance and are excluded from Qwen comparisons. The active supplemental scope is 483 validation runs, 320 matched B2 runs (repeats 3 and 4), and 480 A1 guard runs. Optional low-temperature experiments were cancelled. Original Qwen main and corruption panels contain 1,600 and 320 case-runs. Raw model outputs are machine-generated records, not endorsements of their contents or independent factual truth. Read each manifest and the research limitations. Research code: https://github.com/Oracle4CEG/Oracle-Agentic-Audit (delivery through a personal-fork pull request). Atlas construction: https://github.com/Oracle4CEG/Oracle4SD-Atlas. The research `manifests/public-release.json` pins the exact dataset commit and SHA-256 of each archive. `python -m experiments.download` downloads anonymously and verifies all files; `python -m analysis.replay --retrain` refits CPU baselines and replays saved LLM records without a GPU or inference service. Hosted Colab acceptance remains a separate check; no hosted execution is claimed by uploading this dataset. No model weights, credentials or confidential manuscripts are included. The existing data license applies to original curated data; archived source/code and third-party content retain their original terms. Research code now has author-confirmed MIT terms. The archive does not certify a public upstream model checkpoint revision. ### Recorded research acceptance and code license The public inputs remain pinned to dataset commit `ee197fc6fd6b8d980e7c7dda4feae966efe5d28c`. The acceptance directory `research/agentic-audit/2026-10-10/acceptance/github-notebook/` contains the actually executed notebook, environment, anonymous-download receipt, replay receipt and per-table comparison. All six code cells completed in a fresh GitHub Actions environment; 73 scientific CSV comparisons and 18 numeric LaTeX tables plus exact prompts passed, and eight figures were regenerated. The receipt records elapsed time and figure hash equality separately. Separate delivery notes record the earlier local network interruptions. This is a **GitHub Actions fresh-notebook acceptance**, not a hosted Google Colab run. GitHub Actions independently passed the recorded CPU replay. No new LLM inference was performed. The commit-pinned notebook is available in research PR #2; final hosted Colab and independent scientific review remain pending. The author confirmed MIT for original research code on 10 October 2026. See the research repository's LICENSE and DATA_LICENSE.md. Dataset and third-party terms are unchanged. This acceptance addition does not change the bytes or identities of the previously published Atlas-input and research-record archives.