Release BehaviorBench artifact (from lyang84/BehaviorBench-camera-ready)

.gitattributes

@@ -58,3 +58,16 @@ saved_model/**/* 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
+v1_profilegen/state/train_rows.jsonl filter=lfs diff=lfs merge=lfs -text
+v1_profilegen/state/validation_rows.jsonl filter=lfs diff=lfs merge=lfs -text
+v1/v1_profilegen/state/train_rows.jsonl filter=lfs diff=lfs merge=lfs -text
+v1/v1_profilegen/state/validation_rows.jsonl filter=lfs diff=lfs merge=lfs -text
+Faith/benchmark_profile/state/train_rows.jsonl filter=lfs diff=lfs merge=lfs -text
+Faith/benchmark_profile/state/validation_rows.jsonl filter=lfs diff=lfs merge=lfs -text
+Belief/benchmark_profile/state/train_rows.jsonl filter=lfs diff=lfs merge=lfs -text
+Belief/benchmark_profile/state/validation_rows.jsonl filter=lfs diff=lfs merge=lfs -text
+legacy/v1/v1_profilegen/state/train_rows.jsonl filter=lfs diff=lfs merge=lfs -text
+legacy/v1/v1_profilegen/state/validation_rows.jsonl filter=lfs diff=lfs merge=lfs -text
+Belief/metadata/profile_state_train_rows.jsonl filter=lfs diff=lfs merge=lfs -text
+Belief/metadata/profile_state_validation_rows.jsonl filter=lfs diff=lfs merge=lfs -text
+manifold/**/*.csv filter=lfs diff=lfs merge=lfs -text

CHANGELOG.md

@@ -0,0 +1,10 @@
+# Changelog
+
+## 1.0.0 — NeurIPS 2026 artifact candidate
+
+- Organized Polymarket data into `dataset/` and model-ready instances into `benchmark/`.
+- Documented Belief and Trade schemas, interfaces, splits, and support pools.
+- Added reference tie-aware Belief evaluation metadata without changing benchmark samples.
+- Added Apache-2.0 license, Responsible AI documentation, and Croissant 1.1 metadata.
+
+Future releases should record all changes to samples, labels, schemas, splits, evaluation rules, or generated profiles here.

DATASET_CARD.md

@@ -0,0 +1,77 @@
+---
+pretty_name: UserModeling (Polymarket BehaviorBench)
+task_categories:
+  - text-classification
+  - tabular-regression
+language:
+  - en
+license: apache-2.0
+tags:
+  - behavioral-modeling
+  - prediction-markets
+  - user-modeling
+  - polymarket
+---
+
+# UserModeling (Polymarket BehaviorBench)
+
+## Dataset description
+
+This dataset supports wallet-level behavioral prediction on Polymarket. It asks whether models can use a wallet's past activity to predict its future position-derived stance or next trading action. It does **not** use market resolution as the primary prediction target.
+
+### Tasks
+
+- **Belief:** predict a dominant held outcome (`choice`) and a position-derived normalized holding imbalance (`confidence`).
+- **Trade:** predict a next-action direction (`increase`, `decrease`, or `close`) and an estimated USDC amount.
+
+### Input interfaces
+
+- **Direct:** behavior history is supplied directly.
+- **Profile:** history is represented by a generated wallet profile.
+- **Retrieval:** context is augmented with records retrieved from an external support-wallet pool.
+
+## Data organization
+
+- `dataset/Belief/source/data.parquet`: 1,810,609 wallet-market stance rows.
+- `dataset/Belief/splits/`: fixed train/validation/test instances for 2,000 experiment wallets.
+- `dataset/Belief/support_set/`: 1,669,164 rows from 29,319 non-experiment wallets.
+- `dataset/Trade/source/`: upstream trade, price-matched, semantic, and behavior-feature tables.
+- `dataset/Trade/splits/`: fixed structured train/validation/test trade rows.
+- `dataset/Trade/support_set/`: 5,086,400 trades from 11,676 non-experiment wallets.
+- `benchmark/`: model-ready Direct, Profile, and Retrieval instances in Parquet and/or compressed JSONL.
+
+See the repository [README](README.md) and [`docs/`](docs/) for schemas and lineage.
+
+## Belief label semantics
+
+For released source rows:
+
+\[
+\text{confidence}
+= 2\frac{\text{stance\_hold\_amt}}{\text{total\_hold\_amt}} - 1
+= \frac{H_{\max}-H_{\min}}{H_{\max}+H_{\min}}.
+\]
+
+This value measures the normalized imbalance of the wallet's two-sided holdings. It is **not** a directly elicited subjective probability, a market price, or the probability that the market resolves to the selected outcome.
+
+## Splits and retrieval support
+
+Belief split metadata states a 70/15/15 per-wallet, time-ordered construction using `stance_first_block`, `stance_last_block`, and `market_id`, with rolling history and at most 30 history items in prompts.
+
+Belief retrieval metadata documents exact `event_slug` matching, a strict block-time filter (`support.stance_last_block < target.stance_first_block`), same-wallet exclusion, and the five latest matches.
+
+Trade support metadata states that the fixed 2,000 experiment wallets are excluded and support trades are sorted by wallet, block, and log index.
+
+## Intended uses
+
+- Evaluating personalized behavioral prediction from prior wallet activity.
+- Comparing direct-history, profile-based, and retrieval-augmented user representations.
+- Studying classification and continuous behavioral targets.
+
+## Responsible use
+
+The release contains public pseudonymous blockchain addresses and behavioral traces. It is intended for research evaluation, not identity attribution, deanonymization, surveillance, financial advice, or consequential decisions about individuals. See [`docs/RESPONSIBLE_AI.md`](docs/RESPONSIBLE_AI.md) for limitations, biases, sensitive-information considerations, intended uses, social impact, synthetic profile content, and provenance.
+
+## License and machine-readable metadata
+
+BehaviorBench is released under the Apache License 2.0; see [`LICENSE`](LICENSE). Croissant 1.1 core and Responsible AI metadata are provided in [`croissant.json`](croissant.json).

INDEX.json

@@ -0,0 +1,18 @@
+{
+  "canonical_tiers": [
+    "Belief",
+    "Trade"
+  ],
+  "canonical_layout": [
+    "source",
+    "splits",
+    "benchmark_direct",
+    "benchmark_profile",
+    "benchmark_retrieval",
+    "metadata"
+  ],
+  "trade_extra": [
+    "support_set"
+  ],
+  "legacy_root": "legacy/"
+}

LICENSE

@@ -0,0 +1,202 @@
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README.md

@@ -1,3 +1,138 @@
 ---
+pretty_name: BehaviorBench
+task_categories:
+  - text-classification
+  - tabular-regression
+language:
+  - en
 license: apache-2.0
+tags:
+  - behavioral-modeling
+  - prediction-markets
+  - user-modeling
+  - polymarket
 ---
+
+# BehaviorBench: Polymarket Behavioral Modeling Data
+
+This repository contains data for modeling **wallet-level behavior on Polymarket**. The prediction target is a wallet's behavior—not the eventual resolution of a market.
+
+Two tasks are provided:
+
+- **Belief:** predict a wallet's dominant outcome holding and a position-derived confidence score.
+- **Trade:** predict the direction and estimated size of a wallet's next trade.
+
+Each task is released at two levels:
+
+- `dataset/`: structured source tables, fixed experimental splits, and retrieval support pools.
+- `benchmark/`: model-ready instances for Direct, Profile, and Retrieval interfaces.
+
+The Belief `confidence` field is a normalized position imbalance derived from holdings. See [Belief schema and labels](docs/schema_belief.md).
+
+## Repository layout
+
+```text
+dataset/
+  Belief/
+    source/       # wallet-market holding labels
+    splits/       # fixed benchmark splits for 2,000 experiment wallets
+    support_set/  # external wallets used by retrieval
+  Trade/
+    source/       # upstream trade, price, and behavior-feature tables
+    splits/       # fixed benchmark splits for 2,000 experiment wallets
+    support_set/  # external wallet trade histories used by retrieval
+benchmark/
+  Belief/{direct,profile,retrieval}/
+  Trade/{direct,profile,retrieval}/
+metadata/
+docs/
+```
+
+This camera-ready artifact contains the **Polymarket BehaviorBench** data only; the separate Manifold extension is not part of this release.
+
+## Dataset summary
+
+### Belief
+
+A Belief source row represents one wallet's position-derived stance in one market. The benchmark asks a model to predict:
+
+```json
+{"choice": "an outcome", "confidence": 0.0, "rationale": "..."}
+```
+
+The fixed split contains:
+
+| Split | Rows | Wallets |
+|---|---:|---:|
+| train | 98,094 | 2,000 |
+| validation | 21,147 | 2,000 |
+| test | 22,204 | 2,000 |
+
+The source table has 1,810,609 rows. The retrieval support set excludes the 2,000 experiment wallets and contains 1,669,164 rows from 29,319 wallets.
+
+### Trade
+
+A Trade benchmark row represents a target transaction together with preceding wallet history and decision context. The target contains:
+
+```json
+{"direction": "increase | decrease | close", "amount_usdc_est": 0.0}
+```
+
+The structured split contains:
+
+| Split | Rows |
+|---|---:|
+| train | 1,311,941 |
+| validation | 114,471 |
+| test | 59,560 |
+
+The model-ready Trade benchmark files use stable `sample_id` values for alignment across interfaces. The retrieval support set excludes the fixed experiment wallets and contains 5,086,400 trades from 11,676 wallets.
+
+## Benchmark interfaces
+
+- **Direct:** supplies wallet behavior history directly.
+- **Profile:** supplies a generated wallet profile (and, depending on task, limited current context).
+- **Retrieval:** augments the instance with records retrieved from a support pool that excludes experiment wallets.
+
+See [Benchmark interfaces](docs/interfaces.md) for exact released columns and documented retrieval behavior.
+
+## Loading examples
+
+```python
+import pandas as pd
+
+belief = pd.read_parquet("dataset/Belief/splits/test.parquet")
+trade = pd.read_parquet("benchmark/Trade/direct/test.parquet")
+
+print(belief[["sample_id", "label_choice", "label_confidence"]].head())
+print(trade[["sample_id", "input_text", "target"]].head())
+```
+
+To stream compressed JSONL:
+
+```python
+import gzip, json
+
+with gzip.open("benchmark/Belief/direct/test.jsonl.gz", "rt") as f:
+    first = json.loads(next(f))
+```
+
+## Documentation
+
+- [Data construction and lineage](docs/data_construction.md)
+- [Belief schema and label semantics](docs/schema_belief.md)
+- [Trade schema and label semantics](docs/schema_trade.md)
+- [Splits and support pools](docs/splits_and_support.md)
+- [Direct, Profile, and Retrieval interfaces](docs/interfaces.md)
+
+## NeurIPS 2026 artifact metadata
+
+- License: [Apache-2.0](LICENSE)
+- Machine-readable metadata: [`croissant.json`](croissant.json)
+- Responsible AI documentation: [`docs/RESPONSIBLE_AI.md`](docs/RESPONSIBLE_AI.md)
+- Artifact checklist: [`docs/NEURIPS_2026_ARTIFACT_CHECKLIST.md`](docs/NEURIPS_2026_ARTIFACT_CHECKLIST.md)
+- Version history: [`CHANGELOG.md`](CHANGELOG.md)
+
+The canonical release target is `https://huggingface.co/datasets/Salesforce/BehaviorBench`. After upload, regenerate or download Hugging Face Croissant metadata, merge the RAI fields, and validate the final public URLs with the NeurIPS Croissant validator before OpenReview submission.
+
+For the controlled handoff from the temporary repository to `Salesforce/BehaviorBench`, follow [`docs/SALESFORCE_MIGRATION.md`](docs/SALESFORCE_MIGRATION.md) and use `scripts/prepare_salesforce_release.py`. This prevents accidental inclusion of `manifold/` and checks for unresolved Git LFS pointers.

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+990a4319c199005646233e642bea3786dabf455f57ac54fb59161e2490412618  /data/polymarket-user-modeling/release/v1_cognition_mk10plus_minimal/cognition_wallet_market_labels_holdpos_noconf_mk10plus_stats.csv

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benchmark/Belief/evaluation/README.md

@@ -0,0 +1,41 @@
+# Tie-aware Belief evaluation
+
+This directory adds set-valued choice labels for benchmark rows with exactly zero position-derived confidence. It does not modify the released prompts, v1 `label_choice`, sample IDs, splits, profiles, retrieval contexts, or regression targets.
+
+For a non-tied row (`label_confidence > 0`), choice scoring remains exact match against `label_choice`.
+
+For an exact tie (`label_confidence == 0`), both equally held market outcomes are valid:
+
+```python
+correct = normalize(predicted_choice) in normalize(valid_choices)
+```
+
+Invalid or unrelated outputs remain incorrect.
+
+## Files
+
+- `tied_valid_choices.parquet`: set-valued labels for tied rows.
+- `tied_valid_choices.jsonl`: the same metadata in JSONL format.
+- `tie_aware_scorer.py`: reference scorer for saved predictions.
+- `build_tie_aware_metadata.py`: reproducible metadata builder.
+- `validation_report.json`: coverage and integrity checks.
+
+The metadata contains:
+
+```text
+sample_id, split, wallet, market_id, question,
+label_choice_v1, label_confidence, valid_choices,
+choice_status, outcome_source
+```
+
+Outcome names are recovered, in order of preference, from released Trade observations, Polymarket CLOB market metadata, other observed Belief stances for the same market, or an explicit complement map for standard binary labels such as yes/no and Democrat/Republican.
+
+## Example
+
+```bash
+python benchmark/Belief/evaluation/tie_aware_scorer.py predictions.parquet
+```
+
+The prediction file must include `sample_id`, `label_choice`, and `predicted_choice` (or specify another prediction column).
+
+Tie-aware accuracy should be identified explicitly in result tables so it is not confused with the original v1 exact-match metric. No model inference needs to be rerun; saved predictions can be rescored.

benchmark/Belief/evaluation/build_tie_aware_metadata.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+"""Build set-valued labels for exact-zero Belief confidence rows.
+
+This script does not modify benchmark inputs or original labels. It derives the
+complete binary outcome set from released Trade observations, archived CLOB
+metadata where available, other observed Belief stances, and a small explicit
+binary complement map for standard outcome names.
+"""
+from pathlib import Path
+import json
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+ROOT = Path(__file__).resolve().parents[3]
+OUTDIR = Path(__file__).resolve().parent
+SPLITS = ("train", "validation", "test")
+COMPLEMENT = {
+    "yes": "no", "no": "yes",
+    "up": "down", "down": "up",
+    "over": "under", "under": "over",
+    "democrat": "republican", "republican": "democrat",
+    "democrats": "republicans", "republicans": "democrats",
+    "democratic": "republican",
+    "artemis": "starship", "starship": "artemis",
+}
+
+
+def norm(x):
+    return " ".join(str(x).strip().lower().split())
+
+
+def main():
+    # Temporary recovery products are built at repository root by the audited
+    # extraction step; they can be regenerated from released tables/API data.
+    trade_path = OUTDIR / "sources/tie_outcomes_from_trades.parquet"
+    clob_path = OUTDIR / "sources/tie_outcomes_clob_cache.json"
+    if not trade_path.exists() or not clob_path.exists():
+        raise FileNotFoundError("Run the outcome-recovery extraction step first")
+
+    trade = pq.read_table(trade_path).to_pandas()
+    outcome_map = {}
+    provenance = {}
+    for market_id, g in trade.groupby("market_id"):
+        vals = sorted({norm(x) for x in g.outcome.dropna()})
+        if len(vals) == 2:
+            outcome_map[market_id] = vals
+            provenance[market_id] = "released_trade_source"
+
+    clob = json.loads(clob_path.read_text())
+    for market_id, record in clob.items():
+        vals = sorted({norm(x) for x in record.get("outcomes", [])})
+        if len(vals) == 2:
+            outcome_map[market_id] = vals
+            provenance[market_id] = "polymarket_clob_api"
+
+    source = pq.read_table(
+        ROOT / "dataset/Belief/source/data.parquet",
+        columns=["market_id", "stance_outcome"],
+    ).to_pandas()
+    observed = source.groupby("market_id").stance_outcome.agg(
+        lambda x: sorted({norm(v) for v in x.dropna()})
+    )
+    for market_id, vals in observed.items():
+        if market_id not in outcome_map and len(vals) == 2:
+            outcome_map[market_id] = vals
+            provenance[market_id] = "released_belief_observed_stances"
+
+    rows = []
+    for split in SPLITS:
+        table = pq.read_table(ROOT / f"benchmark/Belief/direct/{split}.parquet")
+        for row in table.to_pylist():
+            if float(row["label_confidence"]) != 0.0:
+                continue
+            market_id = row["market_id"]
+            vals = outcome_map.get(market_id)
+            source_name = provenance.get(market_id)
+            if not vals:
+                stance = norm(row["label_choice"])
+                other = COMPLEMENT.get(stance)
+                if not other:
+                    raise ValueError(f"Cannot recover second outcome for {market_id}: {stance}")
+                vals = sorted({stance, other})
+                source_name = "explicit_binary_complement"
+            elif len(vals) == 1:
+                other = COMPLEMENT.get(vals[0])
+                if not other:
+                    raise ValueError(f"Only one outcome for {market_id}: {vals}")
+                vals = sorted({vals[0], other})
+                source_name = "explicit_binary_complement"
+            assert len(vals) == 2
+            assert norm(row["label_choice"]) in vals
+            rows.append({
+                "sample_id": row["sample_id"],
+                "split": split,
+                "wallet": row["wallet"],
+                "market_id": market_id,
+                "question": row["question"],
+                "label_choice_v1": row["label_choice"],
+                "label_confidence": row["label_confidence"],
+                "valid_choices": vals,
+                "choice_status": "tied",
+                "outcome_source": source_name,
+            })
+
+    schema = pa.schema([
+        ("sample_id", pa.string()), ("split", pa.string()),
+        ("wallet", pa.string()), ("market_id", pa.string()),
+        ("question", pa.string()), ("label_choice_v1", pa.string()),
+        ("label_confidence", pa.float64()),
+        ("valid_choices", pa.list_(pa.string())),
+        ("choice_status", pa.string()), ("outcome_source", pa.string()),
+    ])
+    table = pa.Table.from_pylist(rows, schema=schema)
+    pq.write_table(table, OUTDIR / "tied_valid_choices.parquet", compression="snappy")
+    with open(OUTDIR / "tied_valid_choices.jsonl", "w", encoding="utf-8") as f:
+        for row in rows:
+            f.write(json.dumps(row, ensure_ascii=False, separators=(",", ":")) + "\n")
+
+    counts = pd.DataFrame(rows).groupby("split").size().to_dict()
+    sources = pd.DataFrame(rows).outcome_source.value_counts().to_dict()
+    report = {
+        "total_tied_samples": len(rows),
+        "by_split": counts,
+        "outcome_sources": sources,
+        "checks": {
+            "unique_sample_ids": len({r['sample_id'] for r in rows}) == len(rows),
+            "all_confidence_exact_zero": all(r['label_confidence'] == 0.0 for r in rows),
+            "all_have_two_valid_choices": all(len(r['valid_choices']) == 2 for r in rows),
+            "v1_label_is_valid": all(norm(r['label_choice_v1']) in r['valid_choices'] for r in rows),
+        },
+    }
+    (OUTDIR / "validation_report.json").write_text(json.dumps(report, indent=2) + "\n")
+    print(json.dumps(report, indent=2))
+
+if __name__ == "__main__":
+    main()

benchmark/Belief/evaluation/sources/tie_outcomes_clob_cache.json

@@ -0,0 +1,1277 @@
+{
+  "0x040b0c89ae3233160493a3f6a96249e5bcc0bb8011f4f57729f7f01f217f3631": {
+    "outcomes": [
+      "hawks",
+      "wizards"
+    ],
+    "question": "NBA: Atlanta Hawks vs. Washington Wizards 2023-02-28"
+  },
+  "0x03849c9d28e315a9de8b652d4ccfa21209fc17b9ecf6887541a41d792093ebbf": {
+    "outcomes": [
+      "up",
+      "down"
+    ],
+    "question": "XRP Up or Down - September 27, 1AM ET"
+  },
+  "0x005c049ebc75a165d37f06225d00cf1fbfd6688666e9210029b5d09ef2d8ca55": {
+    "outcomes": [
+      "yes",
+      "no"
+    ],
+    "question": "Will the price of Bitcoin be above $98,000 on December 9?"
+  },
+  "0x0031605849bb07564ecadd8eb1f7e9a615b1932ae97f5b0b66c79fd0c4432c69": {
+    "outcomes": [],
+    "question": "HTTP 404"
+  },
+  "0x0a4b345c1b3c91f8a150885b9ba97c1eebb4e79b2ffe6e45afedf7f54dc150e1": {
+    "outcomes": [
+      "yes",
+      "no"
+    ],
+    "question": "Will Trump say \"Hostage\" during Gulf Leaders Summit on May 14?"
+  },
+  "0x0815150f2478d8ed863bfe558995fdd109622359deb5b10794c79bf0ecff3828": {
+    "outcomes": [],
+    "question": "HTTP 404"
+  },
+  "0x046006520615bd9c8ff83983051b393b013baa1dcce1c7ab7ae084d83acbeba4": {
+    "outcomes": [],
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+  },
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+    "outcomes": [
+      "up",
+      "down"
+    ],
+    "question": "Ethereum Up or Down - October 28, 4PM ET"
+  },
+  "0x0b1558991c6888addef0726e21ec0fb03809e800746584deb6e05dbc13c63de1": {
+    "outcomes": [],
+    "question": "HTTP 404"
+  },
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+    "outcomes": [
+      "yes",
+      "no"
+    ],
+    "question": "Will Victor Marx win the 2026 Colorado Governor Republican primary election?"
+  },
+  "0x0e32e0bb12d9835d6378a71a8d18d5214e4ac9562071f35bf3d73cb7fae86830": {
+    "outcomes": [
+      "yes",
+      "no"
+    ],
+    "question": "Solana above $180 on September 5?"
+  },
+  "0x0c529e26c21c26bd860a52419f2f3cc816ea760a235a3e2cd139f534f28048e0": {
+    "outcomes": [
+      "up",
+      "down"
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+    "question": "Solana Up or Down - October 2, 12AM ET"
+  },
+  "0x1337c8ac31347db07eb1afeb759ba1778c4f991612a1c3ae16802295f49e4b6c": {
+    "outcomes": [
+      "up",
+      "down"
+    ],
+    "question": "Solana Up or Down - June 21, 11PM ET"
+  },
+  "0x11710a61d5cdc4dd67925fa828eaa6c5944f66a242315cb49358e8c7f928e0ac": {
+    "outcomes": [
+      "yes",
+      "no"
+    ],
+    "question": "SC Freiburg vs. BV Borussia 09 Dortmund: Both Teams to Score"
+  },
+  "0x1395b2578ce6b44e66902c924dd206a4f187a32d0b1d1b56c9547453283c6ec4": {
+    "outcomes": [
+      "yes",
+      "no"
+    ],
+    "question": "Will the price of XRP be above $2.10 on December 10?"
+  },
+  "0x13b7464439a66d27413a9fa6387dc6f3ba6a69a06ed7237086be004305079494": {
+    "outcomes": [],
+    "question": "HTTP 404"
+  },
+  "0x15d671f890c665b44717071f4829a3e9730daa7beec7af786110ba4f374f9660": {
+    "outcomes": [],
+    "question": "HTTP 404"
+  },
+  "0x1444aa19610940c750a2fb4ac6c26bccbc0080a8b2f93e715b69d443e0cc7b18": {
+    "outcomes": [
+      "portugal",
+      "morocco"
+    ],
+    "question": "World Cup: Saturday - Portugal vs. Morocco"
+  },
+  "0x166d9245d328fecb77d7af8e1c5f24e0a2ba6afc36a83e022a5424cc650b91ff": {
+    "outcomes": [],
+    "question": "HTTP 404"
+  },
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+    "outcomes": [
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+    "question": "XRP Up or Down - September 3, 9AM ET"
+  },
+  "0x1b3f94435c23ef2607bbf1352973ca4d673f197e9b503a4930eb7406b7b20781": {
+    "outcomes": [
+      "pistons",
+      "wizards"
+    ],
+    "question": "Spread: Pistons (-12.5)"
+  },
+  "0x1e3ec7a3838ee99085d4d49aa865fad389322574356b251bdeb4fa664039543e": {
+    "outcomes": [
+      "yes",
+      "no"
+    ],
+    "question": "Will the Ole Miss Rebels be the #6 seed in the 2025-2026 College Football Playoff?"
+  },
+  "0x1eadb13d2336f7b830b6522edb13a6418d5ef1d911456c59cef4f88e7683fefc": {
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+    "outcomes": [
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+      "no"
+    ],
+    "question": "FC Porto vs. Malmö FF: Both Teams to Score"
+  },
+  "0x210e492fca17b370f24a1ec8191a629b4f2cb7531f8cee15a25a1427a01c33b7": {
+    "outcomes": [
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+    "question": "Grizzlies vs. Knicks: O/U 231.5"
+  },
+  "0x206f511075f50b31e574fab5d49d9b304d3a6faf5efa82df37a7965778eeb919": {
+    "outcomes": [
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+    "question": "XRP Up or Down - October 10, 1AM ET"
+  },
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+    "question": "Solana Up or Down - October 4, 4AM ET"
+  },
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+  },
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+  },
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+    ],
+    "question": "Solana Up or Down - July 8, 9PM ET"
+  },
+  "0x2bbb3da6b52386c7ca81b45dfbd28922b9a5aa7a0667c6f9c45453e4a1f7af56": {
+    "outcomes": [
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+    ],
+    "question": "Will Bitcoin reach $111K September 1–7?"
+  },
+  "0x2f0ce6a15b77753fb922b51d4ac73215c7fc4a9791e5e6887c2801c273173d89": {
+    "outcomes": [
+      "diamondbacks",
+      "brewers"
+    ],
+    "question": "MLB: Arizona Diamondbacks vs. Milwaukee Brewers 2023-04-12"
+  },
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+    "question": "Dave & Buster’s Entertainment (PLAY) Up or Down After Earnings?"
+  },
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+      "alabama crimson tide"
+    ],
+    "question": "Kennesaw State Owls vs. Alabama Crimson Tide (W)"
+  },
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+  },
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+  },
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+  },
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+  },
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+    "outcomes": [
+      "up",
+      "down"
+    ],
+    "question": "NYA (NYA) Up or Down on November 5?"
+  },
+  "0x4444cb404701c302cf388a7d6413457332ea03e9030ce8700a29774e27bf8c45": {
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+    "question": "HTTP 404"
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+    "outcomes": [
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+    ],
+    "question": "SK Brann vs. Fenerbahçe SK: Both Teams to Score"
+  },
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+    "outcomes": [
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+      "no"
+    ],
+    "question": "Will Ronna McDaniel be reelected as RNC Chair?"
+  },
+  "0x4b9efa67614e5348b7114050ff10703c86ff7a622d664ccfe4b4fa7cef3b28d2": {
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+  },
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+  "0x4d18f58c6f010f6581a1c73dbb32522cbd6eab1650d21fcbf11e1075efcebfc1": {
+    "outcomes": [
+      "yes",
+      "no"
+    ],
+    "question": "Will Ricky Castillo win the World Wide Technology Championship?"
+  },
+  "0x4e8d7b33787278a7f7d4f4e5ce12811823dc4cf01a6abd9ff25a356ed6dc534f": {
+    "outcomes": [],
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+  },
+  "0x513fd1306900bc297ec36645e1552922eeea85955fb9b41563059a1217fd9d82": {
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+    "question": "HTTP 404"
+  },
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+    "outcomes": [
+      "ca unión",
+      "ca newell's old boys"
+    ],
+    "question": "Spread: CA Unión (-0.5)"
+  },
+  "0x550f1c88f9dee7fb414ca30d1a5f6923a7d8a56703bb97dba58ca7e551eaf1f7": {
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+  },
+  "0x561661ddcdd239c58510647ca097ee267ed1e7930175c59c80e94d44387e90d6": {
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+    "question": "HTTP 404"
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+  "0x563934e6924cc202fc2a08173466e2f570a9847e2336892166571ad9f9c6fdb8": {
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+    "question": "HTTP 404"
+  },
+  "0x5680e5167b509cda65b544bff49804b151e3224acec09f9083619dd545cfe83b": {
+    "outcomes": [
+      "yes",
+      "no"
+    ],
+    "question": "Wormhole airdrop by March 1?"
+  },
+  "0x568f4f221dbfcdafeed9117eef2d4f9675f9ff1ff8b6c57c19623469bef00de9": {
+    "outcomes": [],
+    "question": "HTTP 404"
+  },
+  "0x56f88815c77017385f7bcf525b4db31c4a95074f3a23aba2b0db6bbf614bd6fe": {
+    "outcomes": [
+      "warriors",
+      "kings"
+    ],
+    "question": "NBA: Golden State Warriors vs. Sacramento Kings 2023-04-28"
+  },
+  "0x575430a61141693080e9476d1d1ee0e6c7dbbe8aa1ab3abfa4f52ffc0b4a33f0": {
+    "outcomes": [],
+    "question": "HTTP 404"
+  },
+  "0x5b43881378f983f014bfdfa5ed49dd337edad2212ff092cd18a45f8fb4cc60e9": {
+    "outcomes": [],
+    "question": "HTTP 404"
+  },
+  "0x5cf20b24c019e39e377b652f1bc0c31a9fbaad8f6b7c08ddca9533bff21b53c2": {
+    "outcomes": [
+      "up",
+      "down"
+    ],
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benchmark/Belief/evaluation/sources/tie_outcomes_from_trades.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e3f2a4a5d6304e342f04b37d8f8abf0055ab8d4cd83c1be9212e931f1fad6893
+size 2041678

benchmark/Belief/evaluation/tie_aware_scorer.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+"""Tie-aware choice scoring for Belief predictions."""
+import argparse, json
+from pathlib import Path
+import pandas as pd
+
+
+def normalize_choice(value):
+    return " ".join(str(value).strip().lower().split())
+
+
+def load_predictions(path):
+    path = Path(path)
+    if path.suffix == ".parquet":
+        return pd.read_parquet(path)
+    return pd.read_json(path, lines=True)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("predictions", help="Parquet/JSONL with sample_id and predicted_choice")
+    ap.add_argument("--prediction-column", default="predicted_choice")
+    ap.add_argument("--ties", default=str(Path(__file__).with_name("tied_valid_choices.parquet")))
+    args = ap.parse_args()
+
+    pred = load_predictions(args.predictions)
+    if pred.sample_id.duplicated().any():
+        raise ValueError("Predictions contain duplicate sample_id values")
+    ties = pd.read_parquet(args.ties)[["sample_id", "valid_choices"]]
+    merged = pred.merge(ties, on="sample_id", how="left", validate="one_to_one")
+
+    def correct(row):
+        p = normalize_choice(row[args.prediction_column])
+        if isinstance(row.valid_choices, (list, tuple)) or getattr(row.valid_choices, "ndim", 0) == 1:
+            return p in {normalize_choice(x) for x in row.valid_choices}
+        # Non-tied rows retain exact-match v1 scoring.
+        return p == normalize_choice(row.label_choice)
+
+    if "label_choice" not in merged:
+        raise ValueError("Predictions must include label_choice for non-tied rows")
+    merged["tie_aware_correct"] = merged.apply(correct, axis=1)
+    result = {
+        "samples": len(merged),
+        "tie_aware_accuracy": float(merged.tie_aware_correct.mean()),
+        "tied_samples_scored": int(merged.valid_choices.notna().sum()),
+    }
+    print(json.dumps(result, indent=2))
+
+if __name__ == "__main__":
+    main()

benchmark/Belief/evaluation/tied_valid_choices.parquet

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+size 2315600

benchmark/Belief/evaluation/validation_report.json

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+{
+  "total_tied_samples": 20319,
+  "by_split": {
+    "test": 2949,
+    "train": 14217,
+    "validation": 3153
+  },
+  "outcome_sources": {
+    "released_trade_source": 20066,
+    "polymarket_clob_api": 113,
+    "explicit_binary_complement": 105,
+    "released_belief_observed_stances": 35
+  },
+  "checks": {
+    "unique_sample_ids": true,
+    "all_confidence_exact_zero": true,
+    "all_have_two_valid_choices": true,
+    "v1_label_is_valid": true
+  }
+}

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+size 5179621

benchmark/Belief/profile/test.parquet

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+size 6262373

benchmark/Belief/profile/train.jsonl.gz

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benchmark/Belief/profile/train.parquet

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benchmark/Belief/profile/validation.jsonl.gz

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+size 4173397

benchmark/Belief/profile/validation.parquet

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benchmark/Belief/retrieval/README.md

@@ -0,0 +1,9 @@
+# Belief Retrieval Benchmark
+
+Belief-level retrieval benchmark samples with strict time-causal retrieval.
+
+Retrieval method:
+- exact event_slug match
+- only support rows with `stance_last_block < target stance_first_block`
+- exclude same wallet
+- take latest top-5 rows

benchmark/Belief/retrieval/benchmark_metadata.json

@@ -0,0 +1,8 @@
+{
+  "benchmark": "Belief retrieval",
+  "version": "belief_retrieval_eventslug_time_top5",
+  "retrieval_method": "event_slug exact match + strict time filter (stance_last_block < target stance_first_block), exclude same wallet, top5 latest",
+  "files": [
+    "train/validation/test parquet + jsonl.gz"
+  ]
+}

benchmark/Belief/retrieval/test.jsonl.gz

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