Fix Croissant FileObject checksums for validation

croissant.json

@@ -7,11 +7,13 @@
     "conformsTo": "dct:conformsTo",
     "cr": "http://mlcommons.org/croissant/",
     "rai": "http://mlcommons.org/croissant/RAI/",
+    "data": "cr:data",
     "dataType": {
       "@id": "cr:dataType",
       "@type": "@vocab"
     },
     "dct": "http://purl.org/dc/terms/",
+    "equivalentProperty": "cr:equivalentProperty",
     "examples": {
       "@id": "cr:examples",
       "@type": "@json"
@@ -29,19 +31,22 @@
     "md5": "cr:md5",
     "parentField": "cr:parentField",
     "path": "cr:path",
+    "prov": "http://www.w3.org/ns/prov#",
     "recordSet": "cr:recordSet",
     "references": "cr:references",
     "regex": "cr:regex",
     "repeated": "cr:repeated",
     "replace": "cr:replace",
     "sc": "https://schema.org/",
+    "samplingRate": "cr:samplingRate",
     "separator": "cr:separator",
+    "sha256": "sc:sha256",
     "source": "cr:source",
     "subField": "cr:subField",
     "transform": "cr:transform"
   },
   "@type": "sc:Dataset",
-  "conformsTo": "http://mlcommons.org/croissant/1.0",
+  "conformsTo": "http://mlcommons.org/croissant/1.1",
   "name": "QuantArena Artifact Bundle",
   "description": "Reproducibility artifacts for QuantArena, a controlled live-market evaluation protocol that fixes the LLM backend, market data stream, analyst workflow, and execution harness while varying only the investment doctrine (policy module) across five operational paradigms: Fundamental Value, Macro Tactical, Behavioral Momentum, Low-Volatility (Smart Beta), and a rule-based Equal-Weight baseline. The bundle contains 28 individual backtest runs across four paper-cited experiments spanning US and CN A-share equities, plus comparison aggregates, universe definitions, and provenance documentation.",
   "license": "https://creativecommons.org/licenses/by-nc/4.0/",
@@ -76,7 +81,9 @@
       "name": "manifest.json",
       "description": "Top-level run inventory (machine-readable)",
       "encodingFormat": "application/json",
-      "contentUrl": "manifest.json"
+      "contentUrl": "manifest.json",
+      "md5": "bb42335a24b28e7d795775435cf0478a",
+      "sha256": "5e2f3e8a12ba31e4352ae03c0a73ab5f940f09fdb1df87aa5aeb7bb213d748e1"
     },
     {
       "@type": "cr:FileObject",
@@ -84,7 +91,9 @@
       "name": "all_metrics.csv",
       "description": "Long-format flat table of summary metrics across all 28 runs",
       "encodingFormat": "text/csv",
-      "contentUrl": "derived/all_metrics.csv"
+      "contentUrl": "derived/all_metrics.csv",
+      "md5": "415bf46376615cffec260d32fb00189e",
+      "sha256": "45a65ac0d1a973614c7d66d4fcb31617c9e2c40dcc55f55a1d334177ab6cbd5e"
     },
     {
       "@type": "cr:FileObject",
@@ -92,7 +101,9 @@
       "name": "all_trades.csv",
       "description": "Concatenated per-trade log across all 28 runs",
       "encodingFormat": "text/csv",
-      "contentUrl": "derived/all_trades.csv"
+      "contentUrl": "derived/all_trades.csv",
+      "md5": "265efdd77a81a00460e533415cf78ee6",
+      "sha256": "ca0df264b0453e4f96d51e420d569f3c253df4961fe421d5facee24e3be2129c"
     },
     {
       "@type": "cr:FileObject",
@@ -100,7 +111,9 @@
       "name": "sector_style_universe.csv",
       "description": "5x4 sector/style universe (20 US + 20 CN tickers)",
       "encodingFormat": "text/csv",
-      "contentUrl": "universe/sector_style_universe.csv"
+      "contentUrl": "universe/sector_style_universe.csv",
+      "md5": "674bb2aed2a6fcba83255453fcb349ce",
+      "sha256": "8d834d3365c56f5967cf916fb7b810f8da799b09ee8d7f1b1c262eee4f63039a"
     },
     {
       "@type": "cr:FileSet",
@@ -380,8 +393,29 @@
     "Methodological research on controlled-intervention evaluation in finance."
   ],
   "rai:dataBiases": "Universe is restricted to 20 liquid US tickers and 20 liquid CN A-share tickers (5 sectors x 4 size-style cells per market); micro-cap, illiquid, or non-equity assets are excluded by design. Performance patterns observed here may not transfer to broader universes. Single-seed case-study runs (with one paired re-run for the US window) imply LLM-sampling stochasticity is bounded but not fully characterized; see the Reproducibility table in the paper.",
-  "rai:dataLimitations": "Trading frictions (transaction costs, market impact, short-selling, borrowing constraints) are not modeled. The 6M live-market window is much shorter than typical institutional evaluation horizons. The GPT-5.4 backend identifier reflects the API gateway label exposed at run time and not a vendor build hash; exact backend reproducibility depends on the gateway's routing decisions at run time. The bundle redistributes only the derived decision-level data, not the raw third-party feeds.",
+  "rai:dataLimitations": "Trading frictions (transaction costs, market impact, short-selling, borrowing constraints) are not modeled in the primary runs; the paper reports a deterministic post-hoc transaction-cost sensitivity sweep over the released trade logs. The 6M live-market window is much shorter than typical institutional evaluation horizons. The GPT-5.4 backend identifier reflects the API gateway label exposed at run time and not a vendor build hash; exact backend reproducibility depends on the gateway's routing decisions at run time. The bundle redistributes only the derived decision-level data, not the raw third-party feeds.",
   "rai:dataReleaseMaintenancePlan": "v1.0 corresponds to the NeurIPS 2026 E&D Track submission. Future versions will add longer windows, additional asset classes, and trading-friction modeling. Bundle versioning is tracked in CHANGELOG.md.",
   "rai:personalSensitiveInformation": "Not applicable — bundle contains no personally identifiable or sensitive information.",
-  "rai:dataSocialImpact": "The bundle supports academic study of LLM-driven trading agents under controlled conditions. Deploying such agents to real markets without additional safeguards (frictions, risk controls, auditing) carries substantial risk, including financial loss and potential amplification of herd behavior. The bundle is explicitly scoped for research and evaluation; it is not financial advice and should not be interpreted as evidence of safe or profitable real-world deployment."
-}
+  "rai:dataSocialImpact": "The bundle supports academic study of LLM-driven trading agents under controlled conditions. Deploying such agents to real markets without additional safeguards (frictions, risk controls, auditing) carries substantial risk, including financial loss and potential amplification of herd behavior. The bundle is explicitly scoped for research and evaluation; it is not financial advice and should not be interpreted as evidence of safe or profitable real-world deployment.",
+  "rai:hasSyntheticData": false,
+  "prov:wasDerivedFrom": [
+    "Yahoo Finance / yfinance US equity price and corporate-action data",
+    "Tushare China A-share price, fundamental, and news data",
+    "Financial Modeling Prep US fundamentals and news data",
+    "AKShare China macro and policy indicators",
+    "Tavily search-API news retrieval metadata",
+    "LLM backend decision outputs from the controlled QuantArena execution harness"
+  ],
+  "prov:wasGeneratedBy": [
+    {
+      "@type": "prov:Activity",
+      "name": "Controlled backtest execution",
+      "description": "The QuantArena harness executed daily decisions from 2025-09-01 to 2026-02-28 for matched US and CN 20-ticker universes, fixing backend, analyst workflow, initial capital, portfolio accounting, and execution rules while varying only the mandate module."
+    },
+    {
+      "@type": "prov:Activity",
+      "name": "Artifact derivation and anonymized release packaging",
+      "description": "Raw run directories were filtered into decision-level artifacts: per-run metrics.json, trades.csv, equity_curve.csv, comparison aggregates, universe definitions, audit manifests, and flattened all_metrics/all_trades tables. Raw third-party OHLCV/news bodies, private credentials, author-identifying repository metadata, and live LLM response logs are excluded."
+    }
+  ]
+}