Publish GI-TraceBench with verified train and test splits

.gitattributes

@@ -58,3 +58,7 @@ 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
+source_splits/test.jsonl filter=lfs diff=lfs merge=lfs -text
+source_splits/train.jsonl filter=lfs diff=lfs merge=lfs -text
+test.jsonl filter=lfs diff=lfs merge=lfs -text
+train.jsonl filter=lfs diff=lfs merge=lfs -text

LICENSE

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

@@ -1,3 +1,124 @@
----
-license: apache-2.0
----
+---
+pretty_name: GI-TraceBench
+license: apache-2.0
+language:
+- en
+task_categories:
+- question-answering
+- text-generation
+size_categories:
+- 10K<n<100K
+tags:
+- science
+- biomedical
+- provenance
+- evidence-grounded-reasoning
+configs:
+- config_name: default
+  default: true
+  data_files:
+  - split: train
+    path: data/train.parquet
+  - split: test
+    path: data/test.parquet
+---
+
+# GI-TraceBench
+
+GI-TraceBench is a source-linked benchmark for evidence-grounded scientific reasoning
+in gastrointestinal oncology. It connects provenance question answering, knowledge
+discovery, and hypothesis evaluation through shared Paper, Claim, and Evidence Span
+records. The benchmark contains 10,282 English questions across 50 thematic clusters.
+
+## Splits
+
+| Split | Questions | Thematic clusters |
+| --- | ---: | ---: |
+| train | 8,737 | 42 |
+| test | 1,545 | 8 |
+
+The public training split is the finalized adaptation pool used in the paper.
+The two splits have disjoint question IDs, normalized question texts, and thematic
+clusters. Only `train` and `test` are defined in the default HF configuration.
+The benchmark has 5,907 provenance QA, 1,910 knowledge discovery QA, and 2,465
+hypothesis evaluation QA instances in total.
+
+## Load the dataset
+
+```python
+import json
+from datasets import load_dataset
+
+dataset = load_dataset("Sereints/FindLink")
+assert len(dataset["train"]) == 8737
+assert len(dataset["test"]) == 1545
+
+example = dataset["train"][0]
+answer = json.loads(example["answer"])
+full_record = json.loads(example["record_json"])
+```
+
+The same configuration works with `streaming=True`.
+
+## Files and representations
+
+- `data/train.parquet`, `data/test.parquet`: the default HF loading and viewer
+  files. They expose the question, structured answer encoded as a JSON string,
+  task metadata, source-ID lists, and the complete record in `record_json`.
+- `train.jsonl`, `test.jsonl`: complete structured publication records. Answers
+  and nested evidence objects retain their native JSON structure. Read these
+  with Python's `json` module; their heterogeneous nested types are not intended
+  for automatic Arrow schema inference.
+- `source_splits/train.jsonl`, `source_splits/test.jsonl`: the 7,671-question
+  source training subset and the same 1,545-question test set. They are download
+  artifacts for tracing the source snapshot, excluded from the default HF config.
+  The default training pool is the 8,737-question root training file.
+- `evidence_store/`: 1,614 paper-ID records, 5,475 Claims, 4,968 Evidence Spans,
+  733 associations, and 2,048 hypothesis records. These are structured support
+  records, not a collection of complete paper PDFs. They are download artifacts,
+  excluded from the default QA loading configuration.
+- `dataset_info.json`, `release_alignment.json`, `SHA256SUMS.txt`: representation
+  metadata, source checksums, and checksums for every uploaded file respectively.
+
+The default viewer keeps `qa_id`, `qa_group_id`, `qa_family`, `subtask_type`,
+`qa_subtype`, `cluster_id`, `language`, `split`, and `question` as strings, and
+`claim_ids`, `paper_ids`, `evidence_span_ids`, `association_ids`, and `hypothesis_ids`
+as lists of strings. Optional fields may be null. `answer` and `record_json` are
+JSON strings. Decoding `record_json` reconstructs the corresponding structured
+JSONL record without dropping fields or changing scientific content.
+
+Publication records normalize split and partition metadata to the public
+train/test scheme. The frozen source snapshot remains unchanged. This is a
+publication view of that snapshot, not a byte-for-byte copy of the source files.
+Natural-language uses of terms such as clinical validation are preserved.
+
+## Evaluation and limitations
+
+The dataset supports provenance QA, knowledge discovery, and hypothesis
+evaluation under supplied evidence. Reference answers are provided for training
+and evaluation; they must not be supplied as model input during held-out testing.
+Use `record_json` or the structured JSONL for task-specific assessment fields.
+
+Construction uses AI-assisted processing and structured reference records.
+The benchmark is specific to English gastrointestinal oncology literature;
+its generated assessment targets and evidence coverage have limitations.
+Candidate hypotheses are assessment objects and do not establish clinical
+efficacy. This dataset is intended for scientific-reasoning research.
+
+## Licensing and source attribution
+
+The authors' contributions to GI-TraceBench are licensed under the
+[Apache License 2.0](LICENSE), as selected for the `Sereints/FindLink` repository.
+This grant applies only to rights the authors are entitled to license.
+Third-party paper excerpts and source-derived material retain their applicable
+original terms and are excluded from any additional grant of rights by this
+dataset license. Paper IDs and Evidence Span links support source attribution.
+Selecting this license does not establish permission to redistribute source
+material; the applicable source terms must also be satisfied.
+
+## Provenance
+
+This publication view is derived from the frozen
+`trusted_reasoning_release_20260927` snapshot. `release_alignment.json` records
+the source-relative file paths and hashes. No model API credentials or local
+authentication files are part of the dataset package.

SHA256SUMS.txt

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+50c71c79ebb23a66f654f3225153172017e793ce6df81c3a7694ced6c626455d  evidence_store/manifest.json
+e65a2f4345eec8d6ba0faf074ad8ccd933278478f8bf97ea58679aeff56d5e49  evidence_store/paper_records.jsonl
+cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30  LICENSE
+0691137d359340e82a782704ddeeb1d60e30203d0fef7918a86e1c7777ac9af3  README.md
+e7e65b52b7aeede107551fefd060ce1ef91a8baf14d954bf99921cc6cfd021d5  release_alignment.json
+ddde9d0b11635ada42669e7bf6cb3b87194689290ffb78d90d947b3ee60c4874  source_splits/test.jsonl
+55a57af80c2e7a2deb20ec8688b7d6cd8638f1a8919eb9fbe23a68559a3b5879  source_splits/train.jsonl
+ddde9d0b11635ada42669e7bf6cb3b87194689290ffb78d90d947b3ee60c4874  test.jsonl
+c1c2362593809a851526ed22ccdba6c89384fb6eceee64acefa03d4630d01e10  train.jsonl

data/test.parquet

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data/train.parquet

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dataset_info.json

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+{
+  "dataset_name": "GI-TraceBench",
+  "version": "1.2",
+  "source_release": "trusted_reasoning_release_20260927",
+  "domain": "gastrointestinal oncology",
+  "task_families": [
+    "provenance_qa",
+    "knowledge_discovery_qa",
+    "hypothesis_evaluation_qa"
+  ],
+  "splits": {
+    "train": {
+      "path": "data/train.parquet",
+      "structured_path": "train.jsonl",
+      "rows": 8737,
+      "clusters": 42,
+      "families": {
+        "provenance_qa": 5009,
+        "knowledge_discovery_qa": 1624,
+        "hypothesis_evaluation_qa": 2104
+      }
+    },
+    "test": {
+      "path": "data/test.parquet",
+      "structured_path": "test.jsonl",
+      "rows": 1545,
+      "clusters": 8,
+      "families": {
+        "provenance_qa": 898,
+        "knowledge_discovery_qa": 286,
+        "hypothesis_evaluation_qa": 361
+      }
+    }
+  },
+  "source_splits": {
+    "train": {
+      "path": "source_splits/train.jsonl",
+      "rows": 7671
+    },
+    "test": {
+      "path": "source_splits/test.jsonl",
+      "rows": 1545
+    }
+  },
+  "json_string_columns": [
+    "answer",
+    "record_json"
+  ],
+  "record_json_is_lossless": true,
+  "evidence_store": "evidence_store/",
+  "repository": "Sereints/FindLink",
+  "license": "apache-2.0",
+  "license_scope": "rights_the_authors_are_entitled_to_license",
+  "third_party_materials": "Original source terms apply; no additional rights granted."
+}

evidence_store/manifest.json

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+{
+  "version": "formal-agent-evidence-store-v2.0",
+  "task": "read_only_agent_evidence_snapshot",
+  "source": "agent_sft_all.jsonl",
+  "source_sha256": "b39ec4f5bb9832a941769132cf67770b5c0066c15ce7559989bf9b66071a8365",
+  "qa_source": "qa_all.jsonl",
+  "qa_source_sha256": "01a1f9b6baec85d8c1667dd270241d88d00aab32f87b591a039d493aa1887dba",
+  "source_trajectory_rows": 10733,
+  "tool_responses_scanned": 70048,
+  "counts": {
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+    "evidence_spans": 4968,
+    "associations": 733,
+    "hypotheses": 2048,
+    "paper_records": 1614
+  },
+  "conflicting_duplicate_records": 0,
+  "canonical_claims_included": false,
+  "canonical_claims_note": "The Agent runtime operates on Claim, Evidence Span, Association, Hypothesis, and paper indexes; no canonical Claim records were fabricated from trajectory observations.",
+  "formal_qa_rows_in_snapshot": false,
+  "passed": true,
+  "files": {
+    "claims": {
+      "path": "claims.jsonl",
+      "sha256": "f10f913efbc213a44ef594e29f4cf0d9d94541679511a747c8e525df984df37d"
+    },
+    "evidence_spans": {
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+    },
+    "associations": {
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+      "sha256": "bf9916f67e43fe153e590a2b3524f034717be4c1f1d48b9401096ff5b8f41bdc"
+    },
+    "hypotheses": {
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+      "sha256": "169ca1e728aaf8507ffcf0f366da0674cc1c57f5984e0ced41f4b02e0cd92ba9"
+    },
+    "paper_records": {
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+      "sha256": "e65a2f4345eec8d6ba0faf074ad8ccd933278478f8bf97ea58679aeff56d5e49"
+    }
+  }
+}

release_alignment.json

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+  "publication_version": "1.2",
+  "repository": "Sereints/FindLink",
+  "dataset_counts": {
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+    "test": 1545,
+    "total": 10282
+  },
+  "source_files": {
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+  },
+  "transformations": [
+    "Combine the source training and development pools into the public train split.",
+    "Normalize split/partition metadata to the public train/test scheme without rewriting scientific text.",
+    "Export the complete structured records plus a reversible, typed HF Parquet view.",
+    "Use source-relative provenance and omit unrelated experiment metadata."
+  ],
+  "frozen_source_sha256_manifest": "583fa326ddf64b300c7884071c0ef2ee9bdc551961b2adf7f85995e7b78e66c0"
+}

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test.jsonl

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train.jsonl

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