--- pretty_name: "Inductive Forecasting Study — Anonymous Data Release" language: - en license: other task_categories: - text-generation - reinforcement-learning tags: - forecasting - reasoning - prediction-markets - synthetic-data - behavioral-evaluation configs: - config_name: exp1_updates data_files: - split: test path: data/exp1/updates.parquet - config_name: exp1_review_materials data_files: - split: test path: data/exp1/review_materials.parquet - config_name: exp2_coin_city_tasks data_files: - split: test path: data/exp2/tasks.parquet - config_name: exp2_coin_city_responses data_files: - split: test path: data/exp2/responses.parquet - config_name: exp3_coin_city_transfer data_files: - split: train_causal path: data/exp3_coin_city/train_causal.parquet - split: train_population_prior path: data/exp3_coin_city/train_population_prior.parquet - split: train_structureless path: data/exp3_coin_city/train_structureless.parquet - split: test path: data/exp3_coin_city/test.parquet - config_name: exp3_coin_city_scores data_files: - split: test path: data/exp3_coin_city/scores.parquet - config_name: exp4_historical_markets data_files: - split: train path: data/exp4/train.parquet - split: validation path: data/exp4/validation.parquet - split: test path: data/exp4/test.parquet - config_name: exp4_locked_test_outputs data_files: - split: test path: data/exp4/locked_test_outputs.parquet - config_name: exp4_evidence_updates data_files: - split: test path: data/exp4/evidence_updates.parquet - config_name: appendix_domain_transfer data_files: - split: train_d1 path: data/appendix_domain/train_d1.parquet - split: train_d2 path: data/appendix_domain/train_d2.parquet - split: train_d3 path: data/appendix_domain/train_d3.parquet - split: train_structureless path: data/appendix_domain/train_structureless.parquet - split: test path: data/appendix_domain/test.parquet - config_name: appendix_dag_transfer data_files: - split: train_family path: data/appendix_dag/train_family.parquet - split: train_population_prior path: data/appendix_dag/train_population_prior.parquet - split: train_structureless path: data/appendix_dag/train_structureless.parquet - split: test_family path: data/appendix_dag/test_family.parquet - split: test_population_prior path: data/appendix_dag/test_population_prior.parquet - config_name: appendix_dag_zero_shot data_files: - split: test path: data/appendix_dag/zero_shot_scores.parquet - config_name: appendix_mechanism_transfer data_files: - split: train path: data/appendix_mechanism/train_*.parquet - split: test path: data/appendix_mechanism/test_*.parquet - config_name: appendix_mechanism_scores data_files: - split: test path: data/appendix_mechanism/scores.parquet --- # Inductive Forecasting Study — Anonymous Data Release This repository is the anonymous data companion to a paper studying behavioral signatures of inductive reasoning in language-model forecasts. It packages the frozen inputs, model responses, row-level scores, and aggregate result artifacts used by the paper's four main experiments, together with synthetic appendix transfer studies. The release is organized as Hugging Face dataset configurations so each study can be loaded independently: ```python from datasets import load_dataset updates = load_dataset("od2961/inductive-forecasting-data", "exp1_updates") coin_city = load_dataset( "od2961/inductive-forecasting-data", "exp2_coin_city_responses", ) historical = load_dataset( "od2961/inductive-forecasting-data", "exp4_historical_markets", ) ``` ## Contents ### Experiment 1: selective updating `exp1_updates` contains one row per frozen model × market × evidence packet × run record from the June 17 numerical authority. It includes initial and updated probabilities, evidence direction, EHC/HFC/ICS indicators, and parsing status. The source report and threshold analysis are under `artifacts/exp1/`. `exp1_review_materials` contains the 18 blinded fictional reports shown in the human materials review. Individual participant ratings and timestamps are not released because participation consent did not explicitly establish consent for public row-level redistribution. Aggregate review results remain available under `artifacts/exp1/`. ### Experiment 2: Coin City `exp2_coin_city_tasks` contains the frozen target-only, no-context, correct-context, misleading-context, and arbitrary-symbol prompts joined to their answer key. `exp2_coin_city_responses` contains all production model responses, including the matched arbitrary-symbol control set, joined to the same task and gold fields. Failed pilots and corrupt pre-repair files are excluded. ### Experiment 3: trained Coin City transfer `exp3_coin_city_transfer` contains the 4,800-row causal, population-prior, and structureless training sets plus the common 1,440-row held-out evaluation set. `exp3_coin_city_scores` contains the registered greedy and five-draw Qwen3-4B endpoint score rows for the base, causal, population-prior, and structureless conditions. Checkpoints are not part of this dataset repository. ### Experiment 4: historical-market transfer `exp4_historical_markets` contains the checksum-locked 1,736/512/1,024 train/development/test tasks derived from a public Polymarket archive. `exp4_locked_test_outputs` contains base and three-seed final forecasts. `exp4_evidence_updates` contains the secondary paired evidence-update outputs. The 21 GB upstream scrape is not redistributed. ### Appendix studies The remaining configurations contain the registered Coin-* domain ladder, DAG family, and mechanism-composition train/evaluation datasets. Final score ledgers are included when the paper's frozen analysis manifest names an exact ledger. ## Provenance and construction - Real-market questions and historical prices were collected from Polymarket's public interfaces. The derived task rows preserve the study's point-in-time filtering and family-disjoint split. - Coin City, Coin-*, DAG, and mechanism-family tasks are synthetic and generated by the study code. - Model response rows are machine-generated outputs from the deployments named in each record or accompanying artifact. - `release_manifest.json` records row counts and explicit exclusions. - `SHA256SUMS` authenticates every published file. Absolute cluster paths and author-identifying endpoint names are not included. Authentication secrets were never intentionally written to the source records; the release builder also performs a fail-closed secret and identity scan. ## Limitations and responsible use The synthetic studies are behavioral benchmarks, not demonstrations of a particular internal representation. The historical-market tasks inherit selection biases and limitations of prediction-market archives. Static question/rules text cannot always be proven point-in-time, as described in the paper. Model outputs may contain errors or unsupported claims and should not be treated as factual or as financial advice. Some Experiment 1 records contain fictional, explicitly labeled evidence packets about real people and events. Preserve the fictional notice when displaying or redistributing these materials. ## Licensing See `LICENSE.md`. The repository uses `license: other` because it combines original synthetic benchmarks with third-party market-derived fields and model outputs that are subject to their respective source/provider terms. No blanket relicensing of third-party material is asserted. ## Citation During double-blind review, cite this repository as: > Anonymous Authors (2026). *Inductive Forecasting Study — Anonymous Data > Release*. Hugging Face Datasets. The card and citation should be updated after deanonymization.