Add PI Research Bench v0.1 frozen research snapshot

.gitattributes

@@ -58,3 +58,4 @@ 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
+data/generation/test.jsonl filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,4 @@
+__pycache__/
+*.pyc
+.cache/
+.venv/

DATA_RIGHTS.md

@@ -0,0 +1,7 @@
+# 数据来源与权利记录
+
+本版本是账号内的私有研究快照。包含项目构造的题目、参考答案、已有评分代码，以及第三方论文的原文段落、表格内容和引用。原始论文内容的权利归其权利人，不能因为本仓库上传就推定其适用统一开源许可。
+
+`provenance/generation_cases.jsonl` 保留原始语料的 DOI、定位、label_kind、已记录的 license_kind 和审核状态；`provenance/source_catalog.jsonl` 汇总各子集可识别来源。`license_kind` 是上游记录，未在本次上传中独立复核。缺失的许可标记为待核对。
+
+公开前按来源补齐许可证原文、署名与再分发条件，并确定项目原创题目、标注和评分代码的发布许可。本快照不替权利人授予新的许可，不包含模型权重许可授权。

MANIFEST.sha256.json

@@ -0,0 +1,34 @@
+{
+  "algorithm": "SHA-256",
+  "files": {
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+    "README.md": "fca0906593714c85fe8aa460523d9b2554d42ccc65c94248f07d03fe9b991ee5",
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+    "configs/protocol.json": "4f3eac77645c4aab9310a89fe877fcd05af2bd740399cb7dbe5b16ae5670987c",
+    "data/generation/test.jsonl": "6972fd1222a9c78c412ed1e2d59bc6fb648dfd452b6780eea5af59e143a95e82",
+    "data/source/test.jsonl": "6ddca10b4055273d6fc5ba26d74c8459f32326069e2a83077f653e278f419a72",
+    "data/workflow/test.jsonl": "f786eae295324f60a890b8876b62a69dd299fc496daf3cd17fc272c1ce5e5b30",
+    "docs/WORKFLOW_RUBRIC.md": "e343fe6a3214df3d23ca44052f6e03100be9dad42213660b4463c6ae2d427d29",
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+    "inputs/source.jsonl": "db175b5dcb5d3b6460f89bfa76c1c60664914ac76b5d3f4e5d8f65ebb0f9de0e",
+    "inputs/workflow.jsonl": "e93345326fb081fc281b820b1e77a4c4fd9a0409caccef806deb886fd1fdfaaf",
+    "provenance/generation_cases.jsonl": "eb4e4ecf902bdf8bac0182571f8d582479d9d9a851c74447f069b0b3f5afe4a9",
+    "provenance/generation_clusters.json": "e1a5b359746c3f4b1a1301924d5e85d2005fadd876d218cc88a0804f6b3ab44d",
+    "provenance/release.json": "86a207bc1e1ad93b1d1e15f34eee4f45b909018eb8be537742efd27cb9eef8b6",
+    "provenance/scorer_port.json": "818eaf344827059ed0eec2b3ba8472d95c6f3340fc5f119bde693265dac817e7",
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+    "requirements.txt": "756cc9e506ae4ee1a6f6c0507088b5cfc0dc8ba350fb2d2d46f1ffa72033adb6",
+    "scoring/common_scoring.py": "00ab8071f98b14d91e5b4698d86b70efdd7fdee980a67171e8cd0e36ef8974c7",
+    "scoring/generation_legacy.py": "061c6d22540f7544596d91279604d46dc847a0c72b97ac8a29f0b9dfc38f4e58",
+    "scoring/source_benchmark_scoring.py": "9ffad5704b96998a50f38b6d92c02c785ea3f4b9f1f771c3a18a4b8bedd3512b",
+    "scripts/score.py": "089130d8e7044908f203cfe7ec0382ebdd5aaa8466fc75a596cf891399f3e297",
+    "scripts/validate_package.py": "0d061d834eb69b43e4dd0d2fea6bd5a3d500bf910404c858c86447ee340b2c44"
+  }
+}

README.md

@@ -0,0 +1,120 @@
+---
+pretty_name: PI Research Bench
+language:
+- zh
+- en
+size_categories:
+- n<1K
+task_categories:
+- text-generation
+tags:
+- polyimide
+- materials-science
+- benchmark
+- evidence-grounding
+- tool-calling
+- research-planning
+configs:
+- config_name: generation
+  default: true
+  data_files:
+  - split: test
+    path: data/generation/test.jsonl
+- config_name: source
+  data_files:
+  - split: test
+    path: data/source/test.jsonl
+- config_name: workflow
+  data_files:
+  - split: test
+    path: data/workflow/test.jsonl
+---
+
+# PI Research Bench · 聚酰亚胺科研任务评测基准
+
+**v0.1 私有研究快照，2026-10-21。** 面向聚酰亚胺科研助手，覆盖领域生成、结构化工具调用、文献证据溯源和科研工作流。题目与参考答案来自当前 Qwen/Gemma 对齐对照实验的冻结输入。本版本保存题库、机械评分器、工作流审查量表和来源记录。
+
+| 子集 | 评测单元 | 内容 | 主要评价 |
+|---|---:|---|---|
+| generation | 772 | 732 条 JSON、32 条工具调用、8 条文本；来自 716 个原始样本 | 格式、字段、参考匹配、工具名与参数 |
+| source | 152 | 19 篇 DOI 文献，76 张证据卡、76 条引用定位 | 原文区间、逐字引用、数值字面量 |
+| workflow | 12 | 每题 4 段原文，共 48 段；2 个研究方向、经典对照与可检验假设 | 交付完整性与科学可用性，分开审查 |
+| 合计 | 936 | 三类任务分别报告 | 按来源/原始样本聚类分析 |
+
+## 题目示例与用途
+
+- 领域知识：二胺与二酐制备线性 PI 时，为什么高转化率还不足以保证高分子量？
+- 工具调用：给定候选身份、结构与目标性能，输出共同 JSON 协议的工具名和参数。此类题使用历史工具返回，不重新执行预测服务。
+- 文献证据：从给定原文提取支持语句，保留样品、单位、条件、DOI 和段落定位。
+- 引用定位：给定原文与待定位引用，返回 Unicode 码点的 `[start, end)` 区间。辅助词边界表覆盖全部原文。
+- 科研工作流：根据给定 PI 文献提出两个有差异的方向，包含原文经典对照、研究假设、材料与工艺方案、能力核对、风险分析和实验验证规划。
+
+## 使用
+
+安装评分依赖：
+
+```bash
+python -m pip install -r requirements.txt
+python scripts/validate_package.py
+```
+
+通过 Hugging Face Datasets 读取任一子集（私有仓库需已登录且有读取权限）：
+
+```python
+import json
+from datasets import load_dataset
+
+ds = load_dataset("FaFa1116/PI-Research-Bench", "generation", split="test")
+case = json.loads(ds[0]["input_json"])
+messages = case["messages"]
+reference = json.loads(ds[0]["reference_json"])  # 仅用于评分
+```
+
+`data/` 是便于 Dataset Viewer 读取的扁平索引。复杂任务内容以 JSON 字符串保存，避免不同题型产生不一致的 Arrow 嵌套结构。`inputs/` 与 `references/` 保存逐字复制的冻结文件；`input_json` 解析后与对应冻结记录逐项一致。
+
+生成原答后，保存 JSONL，每行包含唯一 `id` 和字符串 `raw_text`。generation 的 id 使用 `unit_id`；source 使用 `id`。可选 `terminal_eos_text` 必须对应实际最后一个生成 token，并有 `terminal_eos_verified: true`。未提供时，调用方应保存已剥除最后原生 EOS 的评分文本，同时另存完整原答与 token。
+
+```bash
+python scripts/score.py --suite generation --predictions predictions.jsonl --output scores.json
+python scripts/score.py --suite source --predictions source_predictions.jsonl --output source_scores.json
+```
+
+评分输出保留逐题结果、题型分组计数以及未提交题目。缺失回答保持未执行状态；完整配对比较应先确认所有预期题目和运行完整性。参考答案示例可用于检查评分接口，不应送入被测模型。
+
+## 评测协议
+
+四个主臂为 Qwen 原版、Qwen 104+40 步最终 LoRA、Gemma 原版、Gemma 104+40 步最终 LoRA。生成诊断另含两个 104 步 domain 适配器作为辅助对照。模型修订与适配器 SHA-256 见 `configs/model_bindings.json`。
+
+统一单张 A800 80GB、BF16、batch size 1、SDPA、贪心解码、32768 上下文；使用各模型原生聊天模板与结束 token。生成诊断输出预算 2048 token，证据卡 2048，引用定位 512。完整工作流的调用、token、时长和工具预算见 `configs/protocol.json`。四个工作流主臂均采用规则协调，JEV 关闭。
+
+本仓库提供可独立运行的 generation/source **机械评分**。工作流提供输入、预算和人工/助手内容审查量表；原研究的多智能体协调器及材料计算服务需另外部署，当前快照未打包这些执行依赖。
+
+## 评分解释
+
+- generation 普通 JSON 评分保留历史解析行为：去除既有 think/EOS 标记及外层 Markdown 代码围栏后解析；文本项主要检查非空。这些指标测量格式和参考匹配。
+- 工具调用使用双方共同的 JSON 协议，��格核对工具 schema、名称和参数，不接受 XML、围栏包装、重复键或非有限数。
+- source 逐字核对给定原文、Unicode 区间和数值字面量，保留原评分器；机械通过与科学蕴含审查分别记录。
+- workflow 的科学内容按来源蕴含、身份/数值/条件、假设与对照、测量设计、执行与能力边界五维评分。量表见 `docs/WORKFLOW_RUBRIC.md`。
+
+## 数据构造与审查状态
+
+参考答案包含助手编写与原文派生内容，领域审核状态保留在 `provenance/generation_cases.jsonl`。本快照尚无独立领域专家审阅结论。历史测试集曾用于开发与评测，预训练接触未知；分组与 DOI 信息用于控制相关样本，不能将 772 个助手回合视为 772 篇独立文献。
+
+2026-10-21 的题目/参考审计发现三个已核实的跨物理量数值比较或排序例子，例如将激光能量与光斑尺寸直接相减。原题和机械参考保持冻结，审计单独列于 `audits/question_reference_audit.json`。正确指出量纲不可比的回答应在科学内容审查中据实评价。该审计发生于推理开始后，且目前不是穷尽性审计。
+
+12 个 workflow 输入的 `verified_structures` 均为空，因此这一子集主要评估基于原文的科研规划及能力边界处理。材料性能预测准确度、实际实验成效和 JEV 的独立增益需要对应实验。
+
+## 文件与版本
+
+- `inputs/`：三个子集的冻结输入。
+- `references/`：生成与来源任务的参考答案；工作流使用审查量表。
+- `data/`：Hugging Face 可读取的 936 行索引。
+- `scoring/`、`scripts/score.py`：从既有冻结评分器移植的 CPU 评分工具。
+- `configs/`、`docs/`：协议、模型绑定、分析方案与量表。
+- `provenance/`：来源、分组、原文件哈希和评分器移植记录。
+- `audits/`：题目问题与打包校验记录。
+- `MANIFEST.sha256.json`：文件哈希清单。
+
+此快照按原样保留历史工具消息中的来源路径，它们属于题目文本，不提供远程文件访问能力。部署时使用自己的路径和服务配置。
+
+本版本为私有归档，文献段落及原表内容沿用各原始来源权利，详见 `DATA_RIGHTS.md`。公开版本需逐来源核对许可、归属和完整题目审查。当前运行的模型结果会在统一评分与内容审查完成后另行整理；本版本没有排行榜结论。

audits/package_validation.json

@@ -0,0 +1,59 @@
+{
+  "passed": true,
+  "parity_fixtures": 3696,
+  "parity_input_kind": "reference and synthetic invalid answers, no measured model outputs",
+  "viewer_loading": {
+    "generation": {
+      "rows": 772,
+      "columns": [
+        "id",
+        "suite",
+        "kind",
+        "category",
+        "original_split",
+        "cluster_id",
+        "question",
+        "messages_json",
+        "input_json",
+        "reference_json",
+        "reference_status"
+      ]
+    },
+    "source": {
+      "rows": 152,
+      "columns": [
+        "id",
+        "suite",
+        "kind",
+        "category",
+        "original_split",
+        "cluster_id",
+        "question",
+        "messages_json",
+        "input_json",
+        "reference_json",
+        "reference_status"
+      ]
+    },
+    "workflow": {
+      "rows": 12,
+      "columns": [
+        "id",
+        "suite",
+        "kind",
+        "category",
+        "original_split",
+        "cluster_id",
+        "question",
+        "messages_json",
+        "input_json",
+        "reference_json",
+        "reference_status"
+      ]
+    }
+  },
+  "credential_pattern_hits": [],
+  "canonical_inputs_byte_identical": true,
+  "scoring_logic_changed": false,
+  "scientific_reference_review_completed": false
+}

audits/question_reference_audit.json

@@ -0,0 +1,330 @@
+{
+  "utc": "2026-10-21T05:11:22.492913+00:00",
+  "scope": "question/reference audit independent of model answers; initial verified examples, not an exhaustive affected-case count",
+  "input_sha256": "5944c73357362066a19d3697761df5be18069084f571a3362ab2321da19c2130",
+  "examples": [
+    {
+      "unit_id": "compare2-4f0507d5543cb2dc@assistant-2",
+      "category": "原文数值比较",
+      "payload": {
+        "caption": "Measurement parameter for J200 LIBS instrument",
+        "column_header_paths": [
+          [
+            "Laser wavelength (nm)"
+          ],
+          [
+            "266"
+          ]
+        ],
+        "doi": "10.1007/s00216-024-05163-6",
+        "footnotes": "",
+        "left": {
+          "cell_origins": [
+            [
+              1,
+              0
+            ],
+            [
+              1,
+              1
+            ]
+          ],
+          "cells": [
+            "Laser energy (mJ)",
+            "3.2"
+          ],
+          "row_index": 1
+        },
+        "right": {
+          "cell_origins": [
+            [
+              2,
+              0
+            ],
+            [
+              2,
+              1
+            ]
+          ],
+          "cells": [
+            "Laser spotsize (µm)",
+            "100"
+          ],
+          "row_index": 2
+        },
+        "table": "Tab1"
+      },
+      "reference": {
+        "content": "{\"comparison_scope\": \"只比较原表可解析点值；保留表头与表注，不转成跨条件性能或因果结论\", \"comparisons\": [{\"column\": 1, \"header_path\": [\"266\"], \"left_raw\": \"3.2\", \"relation\": \"right_higher\", \"right_minus_left\": \"96.8\", \"right_raw\": \"100\"}], \"doi\": \"10.1007/s00216-024-05163-6\", \"left_label\": \"Laser energy (mJ)\", \"research_use\": \"有变化的列用于提出权衡或机制问题；数值高低不自动代表更好的PI或新的体系\", \"right_label\": \"Laser spotsize (µm)\", \"table\": \"Tab1\"}",
+        "role": "assistant"
+      },
+      "finding": "heterogeneous physical quantities and units appear in the same numerical column; mechanical arithmetic/reference matching must not be interpreted as a scientifically meaningful property comparison"
+    },
+    {
+      "unit_id": "compare2-6d2bb6919adb1b6a@assistant-2",
+      "category": "原文数值比较",
+      "payload": {
+        "caption": "Measurement parameter for J200 LIBS instrument",
+        "column_header_paths": [
+          [
+            "Laser wavelength (nm)"
+          ],
+          [
+            "266"
+          ]
+        ],
+        "doi": "10.1007/s00216-024-05163-6",
+        "footnotes": "",
+        "left": {
+          "cell_origins": [
+            [
+              2,
+              0
+            ],
+            [
+              2,
+              1
+            ]
+          ],
+          "cells": [
+            "Laser spotsize (µm)",
+            "100"
+          ],
+          "row_index": 2
+        },
+        "right": {
+          "cell_origins": [
+            [
+              3,
+              0
+            ],
+            [
+              3,
+              1
+            ]
+          ],
+          "cells": [
+            "Laser repetition rate (Hz)",
+            "20"
+          ],
+          "row_index": 3
+        },
+        "table": "Tab1"
+      },
+      "reference": {
+        "content": "{\"comparison_scope\": \"只比较原表可解析点值；保留表头与表注，不转成跨条件性能或因果结论\", \"comparisons\": [{\"column\": 1, \"header_path\": [\"266\"], \"left_raw\": \"100\", \"relation\": \"left_higher\", \"right_minus_left\": \"-80\", \"right_raw\": \"20\"}], \"doi\": \"10.1007/s00216-024-05163-6\", \"left_label\": \"Laser spotsize (µm)\", \"research_use\": \"有变化的列用于提出权衡或机制问题；数值高低不自动代表更好的PI或新的体系\", \"right_label\": \"Laser repetition rate (Hz)\", \"table\": \"Tab1\"}",
+        "role": "assistant"
+      },
+      "finding": "heterogeneous physical quantities and units appear in the same numerical column; mechanical arithmetic/reference matching must not be interpreted as a scientifically meaningful property comparison"
+    },
+    {
+      "unit_id": "order2-d1f22b1c18079733@assistant-2",
+      "category": "原表排序与筛选边界",
+      "payload": {
+        "column": 1,
+        "doi": "10.1007/s00216-024-05163-6",
+        "footnotes": "",
+        "header_path": [
+          "266"
+        ],
+        "rows": [
+          {
+            "cell_origins": [
+              [
+                1,
+                0
+              ],
+              [
+                1,
+                1
+              ]
+            ],
+            "cells": [
+              "Laser energy (mJ)",
+              "3.2"
+            ],
+            "row_index": 1
+          },
+          {
+            "cell_origins": [
+              [
+                2,
+                0
+              ],
+              [
+                2,
+                1
+              ]
+            ],
+            "cells": [
+              "Laser spotsize (µm)",
+              "100"
+            ],
+            "row_index": 2
+          },
+          {
+            "cell_origins": [
+              [
+                3,
+                0
+              ],
+              [
+                3,
+                1
+              ]
+            ],
+            "cells": [
+              "Laser repetition rate (Hz)",
+              "20"
+            ],
+            "row_index": 3
+          },
+          {
+            "cell_origins": [
+              [
+                4,
+                0
+              ],
+              [
+                4,
+                1
+              ]
+            ],
+            "cells": [
+              "Laser beam geometry",
+              "Circular"
+            ],
+            "row_index": 4
+          },
+          {
+            "cell_origins": [
+              [
+                5,
+                0
+              ],
+              [
+                5,
+                1
+              ]
+            ],
+            "cells": [
+              "Stage scan speed (mm/s)",
+              "2"
+            ],
+            "row_index": 5
+          },
+          {
+            "cell_origins": [
+              [
+                6,
+                0
+              ],
+              [
+                6,
+                1
+              ]
+            ],
+            "cells": [
+              "Atmosphere",
+              "Argon"
+            ],
+            "row_index": 6
+          },
+          {
+            "cell_origins": [
+              [
+                7,
+                0
+              ],
+              [
+                7,
+                1
+              ]
+            ],
+            "cells": [
+              "Spectrometer",
+              "Czerny-Turner"
+            ],
+            "row_index": 7
+          },
+          {
+            "cell_origins": [
+              [
+                8,
+                0
+              ],
+              [
+                8,
+                1
+              ]
+            ],
+            "cells": [
+              "Detector",
+              "6-channel CCD"
+            ],
+            "row_index": 8
+          },
+          {
+            "cell_origins": [
+              [
+                9,
+                0
+              ],
+              [
+                9,
+                1
+              ]
+            ],
+            "cells": [
+              "Gate delay (µs)",
+              "0.3"
+            ],
+            "row_index": 9
+          },
+          {
+            "cell_origins": [
+              [
+                10,
+                0
+              ],
+              [
+                10,
+                1
+              ]
+            ],
+            "cells": [
+              "Gate width (ms)",
+              "1.05"
+            ],
+            "row_index": 10
+          },
+          {
+            "cell_origins": [
+              [
+                11,
+                0
+              ],
+              [
+                11,
+                1
+              ]
+            ],
+            "cells": [
+              "Covered wavelength range (nm)",
+              "188–1048"
+            ],
+            "row_index": 11
+          }
+        ],
+        "table": "Tab1"
+      },
+      "reference": {
+        "content": "{\"ascending_rows\": [{\"raw_value\": \"0.3\", \"row_index\": 9, \"row_label_literal\": \"Gate delay (µs)\"}, {\"raw_value\": \"1.05\", \"row_index\": 10, \"row_label_literal\": \"Gate width (ms)\"}, {\"raw_value\": \"2\", \"row_index\": 5, \"row_label_literal\": \"Stage scan speed (mm/s)\"}, {\"raw_value\": \"3.2\", \"row_index\": 1, \"row_label_literal\": \"Laser energy (mJ)\"}, {\"raw_value\": \"20\", \"row_index\": 3, \"row_label_literal\": \"Laser repetition rate (Hz)\"}, {\"raw_value\": \"100\", \"row_index\": 2, \"row_label_literal\": \"Laser spotsize (µm)\"}], \"column\": 1, \"doi\": \"10.1007/s00216-024-05163-6\", \"header_path\": [\"266\"], \"scope\": \"仅对指定原列的可解析点值排序，不宣称目标适用性、显著性或全局最优\", \"table\": \"Tab1\"}",
+        "role": "assistant"
+      },
+      "finding": "heterogeneous physical quantities and units appear in the same numerical column; mechanical arithmetic/reference matching must not be interpreted as a scientifically meaningful property comparison"
+    }
+  ],
+  "reporting_rule": "retain all frozen primary mechanical scores; flag these items in the benchmark audit. In scientific reading, a correct refusal of dimensionally meaningless comparison is not a scientific error merely because it differs from the mechanical reference. Report item-level analysis separately and label any later stratification as post-launch audit.",
+  "inference_prompts_or_scoring_modified": false
+}

configs/analysis_plan.json

@@ -0,0 +1,114 @@
+{
+  "utc": "2026-10-21T01:19:09.758641+00:00",
+  "timing": "specified during inference, before full cross-arm analysis or scientific content scoring",
+  "primary_arms": [
+    "qwen_base",
+    "qwen_final",
+    "gemma_base",
+    "gemma_final"
+  ],
+  "contrasts": [
+    {
+      "name": "qwen_finetuning",
+      "left": "qwen_base",
+      "right": "qwen_final"
+    },
+    {
+      "name": "gemma_finetuning",
+      "left": "gemma_base",
+      "right": "gemma_final"
+    },
+    {
+      "name": "base_cross_model",
+      "left": "qwen_base",
+      "right": "gemma_base"
+    },
+    {
+      "name": "final_cross_model",
+      "left": "qwen_final",
+      "right": "gemma_final"
+    }
+  ],
+  "secondary_generation_contrasts": [
+    {
+      "name": "qwen_domain_adaptation",
+      "left": "qwen_base",
+      "right": "qwen_domain"
+    },
+    {
+      "name": "gemma_domain_adaptation",
+      "left": "gemma_base",
+      "right": "gemma_domain"
+    },
+    {
+      "name": "qwen_semantic_stage",
+      "left": "qwen_domain",
+      "right": "qwen_final"
+    },
+    {
+      "name": "gemma_semantic_stage",
+      "left": "gemma_domain",
+      "right": "gemma_final"
+    }
+  ],
+  "difference": "right minus left",
+  "generation_metrics": {
+    "json": [
+      "json_valid",
+      "reference_top_level_keys_present",
+      "exact_json_reference_match"
+    ],
+    "tool": [
+      "tool_schema_valid",
+      "tool_names_match_reference",
+      "tool_arguments_match_reference"
+    ],
+    "text": [
+      "nonempty"
+    ]
+  },
+  "source_metrics": {
+    "source_card": [
+      "json_valid",
+      "identifiers_match",
+      "schema_fields_present",
+      "all_nonempty_spans_supported",
+      "numeric_literals_exact",
+      "mechanical_grounding_pass"
+    ],
+    "quote_locator": [
+      "json_valid",
+      "identifiers_match",
+      "quote_match",
+      "interval_matches_source",
+      "exact_reference_position",
+      "mechanical_grounding_pass"
+    ]
+  },
+  "workflow_primary_endpoints": {
+    "delivery_complete": "原生终态同时具有非占位实验计划与待审报告，并覆盖材料/工艺/性能能力/安全/综合评价/实验规划角色；人工批准不自动执行。",
+    "scientifically_usable_delivery": "delivery_complete且有两个实质不同方向、原文经典对照、可检验假设、对应目标的实验测量与样品身份绑定；未识别实质事实/数值/条件/引用错误，且没有阻止执行的未解决身份或方法缺口。正确声明输入不足、未计算、未实验可接受。",
+    "scientific_content_score": "来源蕴含、身份数值条件、假设与对照、测量设计、执行与能力边界5维，各0/1/2；0为错误或完全缺失，1为部分或未解决，2为明确满足。对最终可交付内容评分，未交付记0并保留原稿诊断。"
+  },
+  "workflow_review": {
+    "assessor": "本助手的自动/人工阅读式审查，非领域专家",
+    "blinding": "打乱案例和臂顺序并生成无模型/协调标签的原答与最终状态文本；运行轨迹仅在内容评分锁定后核验。执行者已知研究设计，不能声称完全双盲。",
+    "expert_review_completed": false,
+    "expert_decision": null,
+    "rule": "每项实质问题给出输入来源和模型原文定位；格式失败不自动等同科学判断全错；未审项null。"
+  },
+  "bootstrap": {
+    "replicates": 10000,
+    "seed": 20261018,
+    "confidence": 0.95,
+    "method": "paired cluster percentile; resample original case-group connected components for generation, DOI for source/workflow; numerator/denominator aggregated within each resample",
+    "single_cluster": "report paired point difference without confidence interval"
+  },
+  "workflow_binary_tests": "two-sided exact McNemar on12 DOI pairs; Holm correction across four primary contrasts times two binary endpoints",
+  "generation_source_tests": "paired cluster confidence intervals and effect sizes; no independent assistant-turn binomial test",
+  "workflow_science_score": "five dimensions each0/1/2, sum0..10; missing final delivery scored0 per inherited rubric; partial raw-content diagnostics reviewed separately",
+  "incomplete_policy": "missing/unexecuted remains null, no complete comparison until expected paired cohort and integrity records are present",
+  "content_review": "blinded output IDs, original task and sources, final result and unmodified generated content except terminal EOS; metadata labels hidden; assessor knows study design, not fully double blind; lock scores before unblinding",
+  "expert_review_completed": false,
+  "timing_cost_policy": "record native token counts and elapsed costs; historical reused time includes decoding/scoring while fresh generation time ends before decoding/scoring. Show origin and timing scope; no high-precision speed superiority claim across these scopes."
+}

configs/model_bindings.json

@@ -0,0 +1,46 @@
+{
+  "qwen_base": {
+    "family": "qwen",
+    "model_id": "Qwen/Qwen3.8-27B",
+    "revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
+    "adapter_sha256": null,
+    "training_steps": 0
+  },
+  "gemma_base": {
+    "family": "gemma",
+    "model_id": "google/gemma-3-27b-it",
+    "revision": "005ad3404e59d6023443cb575daa05336842228a",
+    "adapter_sha256": null,
+    "training_steps": 0
+  },
+  "qwen_domain": {
+    "family": "qwen",
+    "model_id": "Qwen/Qwen3.8-27B",
+    "revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
+    "adapter_sha256": "87306c539a70e5ab866627a3d9d28c568336257b88f2b16e2372163cc61f9404",
+    "training_steps": 104
+  },
+  "qwen_final": {
+    "family": "qwen",
+    "model_id": "Qwen/Qwen3.8-27B",
+    "revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
+    "adapter_sha256": "70611cdda4dbd6c8548e2ec92d807bedfc2054e82b2e22c1ac1bd9fd8a33cc0c",
+    "training_steps": 144,
+    "semantic_seed": 20261014
+  },
+  "gemma_domain": {
+    "family": "gemma",
+    "model_id": "google/gemma-3-27b-it",
+    "revision": "005ad3404e59d6023443cb575daa05336842228a",
+    "adapter_sha256": "1d2a1510db412b27094200fe45b248205abe1a0962c829bab5207f1af703c040",
+    "training_steps": 104
+  },
+  "gemma_final": {
+    "family": "gemma",
+    "model_id": "google/gemma-3-27b-it",
+    "revision": "005ad3404e59d6023443cb575daa05336842228a",
+    "adapter_sha256": "2d027a89f137ad50136831dfb34cdc2ac8a70d34528a8a391ceeddf49f8c9159",
+    "training_steps": 144,
+    "semantic_seed": 20261014
+  }
+}

configs/protocol.json

@@ -0,0 +1,88 @@
+{
+  "primary_arms": [
+    "qwen_base",
+    "qwen_final",
+    "gemma_base",
+    "gemma_final"
+  ],
+  "secondary_arms": [
+    "qwen_domain",
+    "gemma_domain"
+  ],
+  "primary_checkpoint_policy": "Original weights versus fixed final domain104+semantic40 adapters, seed20261014. Do not select checkpoint using new test outputs. Qwen other seeds/step20 are supplementary only; do not pool them against one Gemma seed.",
+  "suites": {
+    "generation": {
+      "cases": 772,
+      "breakdown": {
+        "tool": 32,
+        "json": 732,
+        "text": 8
+      },
+      "history": "same canonical conversation contents and tools for all models; historical tool returns only, no new tool execution",
+      "tool_output_contract": "When a tool call is needed, output only one JSON object with a \"tool_calls\" array. Each element must have exactly \"name\" (a listed tool name) and \"arguments\" (a JSON object matching that tool schema). Do not wrap the JSON in Markdown or XML. Do not invent tool execution results.",
+      "output_budget": 2048,
+      "scoring": "Same frozen scorer for both families. Tool JSON validated against identical JSON schemas and exact reference call names/arguments; no XML-only requirement. Ordinary JSON/text rules frozen before generation. Pure format and pending-reference match are not scientific correctness."
+    },
+    "source": {
+      "cases": 152,
+      "source_cards": 76,
+      "quote_locators": 76,
+      "source_dois": 19,
+      "output_budget": {
+        "source_card": 2048,
+        "quote_locator": 512
+      },
+      "input": "same complete source and deterministic index, no answer-derived hints",
+      "scorer_path": "scoring/source_benchmark_scoring.py",
+      "scorer_sha256": "9ffad5704b96998a50f38b6d92c02c785ea3f4b9f1f771c3a18a4b8bedd3512b",
+      "dev_test_pooling": false
+    },
+    "workflow": {
+      "cases": 12,
+      "raw_input_sha256": "e93345326fb081fc281b820b1e77a4c4fd9a0409caccef806deb886fd1fdfaaf",
+      "coordination": "rule for all four arms; JEV disabled",
+      "limits": {
+        "context_tokens": 32768,
+        "max_new_tokens_per_call": 8192,
+        "max_llm_calls": 20,
+        "max_generated_tokens": 65536,
+        "max_seconds": 3600,
+        "max_corrections": 4,
+        "max_tool_requests": 8,
+        "max_graph_steps": 200
+      },
+      "tools": "same pinned tool versions, schemas, service assets and access for all arms; verify state after upstream restores services",
+      "decoding_seed": 20261018,
+      "order": "four blocks of three tasks, counterbalanced arm ordering",
+      "endpoint": "delivery and separately blinded scientific content review; failed/unfinished outputs retained; expert review remains false"
+    }
+  },
+  "common_conditions": {
+    "dtype": "BF16",
+    "quantization": null,
+    "attention": "sdpa",
+    "batch_size": 1,
+    "do_sample": false,
+    "max_context_tokens": 32768,
+    "thinking": "Qwen enable_thinking=False; Gemma native no separate thinking mode",
+    "model_native_chat_templates": true,
+    "semantic_prompt_contents_identical": true,
+    "native_end_tokens_only_removed_for_scoring": true,
+    "output_repair": false,
+    "constrained_decoding": false,
+    "reference_in_model_input": false,
+    "exclusive_gpu": "one A80080GB per worker, no other process sharing",
+    "time_accounting": "record token counts and per-case elapsed time; separate model loading and CPU scoring; no claim equal tokenization or identical GPU wall time"
+  },
+  "reuse_policy": "Reuse existing Qwen output only when exact rendered model-input hash, adapter hash, raw reference hash, decoding, budgets, model/runtime and task contract match. Rescore raw output with the common frozen evaluator; never reuse only aggregate metrics. Changed tool prompts/contracts require new inference for affected Qwen cases. Legacy Qwen LoRA+JEV workflows cannot be reused as LoRA+rule. Record provenance of every reused case.",
+  "analysis": "Pair by case; source/generalization confidence intervals cluster by original source/DOI, not assistant turns treated as independent. Keep all failures, timeouts and budget hits. Report within-family fine-tuning effects and between-family contrasts separately. No cross-tokenizer NLL ranking. Lock blinded content scores before unblinding.",
+  "scope_limits": [
+    "Shared historical tests, not a fresh unseen independent test.",
+    "Native tokenizer, architecture, and chat wrappers differ; record lengths and protocol overhead.",
+    "Historical development effort differs; training sample exposure and budget are aligned.",
+    "Gemma final dev loss was worse than domain stage; fixed final checkpoint remains primary, domain checkpoint secondary.",
+    "Scientific references include pending review; mechanical pass alone is not scientific validity."
+  ],
+  "snapshot_version": "0.1",
+  "original_protocol_sha256": "355ee93579f7f5e096f5df605f50ed1bf5b754845d94347ba113e762a125cea9"
+}

data/generation/test.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6972fd1222a9c78c412ed1e2d59bc6fb648dfd452b6780eea5af59e143a95e82
+size 10785152

docs/WORKFLOW_RUBRIC.md

@@ -0,0 +1,24 @@
+# 工作流科学内容审查
+
+评审输入为原始任务、提供的文献段落及模型实际交付。保持模型原答与最终状态，随机化模型标识与顺序，先锁分再解盲。执行者知道研究设计时，应明确记录该条件。
+
+## 两个终点
+
+1. **交付完整**：原生终态同时具有非占位实验计划与待审报告，覆盖材料、工艺、性能能力、安全、综合评价、实验规划角色。
+2. **科学可用交付**：交付完整，并具有两个实质不同方向、原文经典对照、可检验假设、对应目标的实验测量和样品身份绑定；没有实质事实/数值/条件/引用错误，且没有阻止执行的未解决身份或方法缺口。正确说明输入不足、未计算或未实验可以接受。
+
+## 内容维度
+
+| 维度 | 检查内容 |
+|---|---|
+| 来源蕴含 | 结论是否由所引原文支持，观察、解释与假设是否区分 |
+| 身份、数值与条件 | 样品、结构身份、数值、单位、方法及条件是否正确绑定 |
+| 假设与对照 | 两个实质不同方向、经典对照和可检验假设 |
+| 测量设计 | 性能目标是否对应可执行测量、对照和判据 |
+| 执行与能力边界 | 已报道、预测、未执行和待验证状态是否准确 |
+
+各维度 0/1/2：0 为错误或完全缺失，1 为部分满足或存在未解决问题，2 为明确满足。总分 0–10。按最终可交付内容评分；无最终交付记 0，同时单独保留原稿的内容诊断，避免丢失过程中的正确判断。
+
+每项实质问题记录题目 ID、模型原文位置和来源位置。未审项保持 null，审核者身份和专家审核状态单独记录。原项目当前的专家审核状态为 false。
+
+12 题均提供四段原文，verified_structures 为空。对缺失结构输入而明确记录未计算的回答，按任务合同评价。

provenance/generation_clusters.json

@@ -0,0 +1,725 @@
+{
+  "source": "agent_research/pi_llm/20261005_corpus_v3/corpus/test.pending_review.jsonl",
+  "source_sha256": "8a7af97aebb916f4b59d8e21ebf37bd58d5e7881bee419a841f68798a5d8fb33",
+  "original_examples": 716,
+  "clusters": 31,
+  "method": "connected components of original case_groups; all assistant turns of same example retained together",
+  "mapping": {
+    "pi-k001": "pi-k001",
+    "pi-k002": "pi-k001",
+    "pi-k003": "pi-k001",
+    "pi-k004": "pi-k001",
+    "pi-w005": "pi-w005",
+    "pi-w006": "pi-w005",
+    "pi-w007": "pi-w005",
+    "pi-w008": "pi-w005",
+    "table-d04497e54145d4": "compare2-183792bec0e758ec",
+    "table-ad94ede76ce1b5": "compare2-183792bec0e758ec",
+    "table-880a659e5d8de5": "compare2-183792bec0e758ec",
+    "table-52de26d6fdc94f": "compare2-183792bec0e758ec",
+    "table-277bddbd687ca4": "table-277bddbd687ca4",
+    "table-d41c8b73ead141": "table-277bddbd687ca4",
+    "table-632b13135c274c": "table-277bddbd687ca4",
+    "table-e85d16e70ed128": "table-277bddbd687ca4",
+    "table-c21f46858730a9": "table-c21f46858730a9",
+    "table-162aa409a426f6": "observation-2ca89762d20fe3",
+    "table-4d77d8981145ec": "observation-2ca89762d20fe3",
+    "table-83a2fc4e31db54": "observation-2ca89762d20fe3",
+    "table-fbd56f582ffee4": "observation-2ca89762d20fe3",
+    "table-78766ed4c5f241": "observation-2ca89762d20fe3",
+    "table-3e98ece0720b1f": "observation-2ca89762d20fe3",
+    "table-6732ebff32b6b1": "observation-2ca89762d20fe3",
+    "observation-84c44d31722180": "observation-2ca89762d20fe3",
+    "observation-f987cc3298034b": "observation-2ca89762d20fe3",
+    "observation-785444e22a93f7": "observation-2ca89762d20fe3",
+    "observation-b86bf1c0b8bfc5": "observation-2ca89762d20fe3",
+    "observation-aa6fea04df5e7a": "observation-2ca89762d20fe3",
+    "observation-e4fc01cea42b37": "observation-2ca89762d20fe3",
+    "observation-648d3b876adc0b": "observation-2ca89762d20fe3",
+    "observation-f55f386f11f457": "observation-2ca89762d20fe3",
+    "observation-2ca89762d20fe3": "observation-2ca89762d20fe3",
+    "model-T5_N2": "model-T5_N2",
+    "model-Tg_composition_experimental": "model-Tg_composition_experimental",
+    "model-bandgap_experimental": "model-bandgap_experimental",
+    "model-dielectric_experimental": "model-bandgap_experimental",
+    "model-tensile_modulus": "model-bandgap_experimental",
+    "trajectory-tool-002": "trajectory-tool-002",
+    "trajectory-tool-011": "trajectory-tool-002",
+    "trajectory-tool-012": "trajectory-tool-002",
+    "trajectory-tool-013": "trajectory-tool-002",
+    "trajectory-tool-018": "trajectory-tool-018",
+    "trajectory-tool-020": "trajectory-tool-002",
+    "trajectory-tool-027": "trajectory-tool-018",
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+{"doi": "10.3390/polym15163408", "doi_url": "https://doi.org/10.3390/polym15163408", "titles": ["Design, Synthesis and Properties of Semi-Alicyclic Colorless and Transparent Polyimide Films with High Glass Transition Temperatures and Low Retardation for Potential Applications in Flexible Electronics"], "suites": ["generation", "source"], "case_ids": ["compare2-183792bec0e758ec", "compare2-19af751c89914276", "compare2-4dfc2f775aa932cd", "compare2-71ea0949eec3c9d7", "compare2-88af19743f8f65aa", "compare2-995d60c68f1d66af", "compare2-9e274e088c8fac09", "compare2-a4e58950a24d7a67", "compare2-bee986fb3790bfd5", "compare2-cff168af5efa36f1", "compare2-e9d40046539b5b11", "compare2-f178ac93706b9eee", "evidence2-09e8c16bda5f5cb4", "evidence2-0ae8ebf6ff6e671d", "evidence2-1efc7f9ad63bfeda", "evidence2-37a543073c0dc786", "evidence2-49b5685dfca88dd7", "evidence2-528dc14b08143ee9", "evidence2-5d5150fe6694ca99", "evidence2-64924f88e1ebf574", "evidence2-65d40c48dfb8975e", "evidence2-65d40c48dfb8975e-card", "evidence2-65d40c48dfb8975e-quote", "evidence2-7d1d9ef93df233b6", "evidence2-7d1d9ef93df233b6-card", "evidence2-7d1d9ef93df233b6-quote", "evidence2-7f4923e9b49c2e19", "evidence2-7f4923e9b49c2e19-card", "evidence2-7f4923e9b49c2e19-quote", "evidence2-8146256e652cc619", "evidence2-90e9f6af94d57aca", "evidence2-9220be425780dc0f", "evidence2-a081888f54d6ac60", "evidence2-a3a514ea8c9bae3c", "evidence2-a597b9121b56de8e", "evidence2-a806f1131a1964ba", "evidence2-c9bc30354f6eb648", "evidence2-d757473747332cfa", "evidence2-dad90d5e167092b6", "evidence2-de8eb959e8d5f3e5", "evidence2-de8eb959e8d5f3e5-card", "evidence2-de8eb959e8d5f3e5-quote", "evidence2-e843ebd86c7867e1", "evidence2-f54748233ea99618", "order2-04ee959d59af1bd2", "order2-1752efcc796ef4af", "order2-1bb909956316d10b", "order2-258f81deda51fac7", "order2-4db555cc13b1827d", "order2-9ad2b24411f9d730", "order2-b066b0430449659b", "row2-04ee959d59af1bd2", "row2-05192a921c6b5a06", "row2-0605846c08b5c89c", "row2-06086ea214c444dd", "row2-0bf7fc3a9566a48e", "row2-0fa7a3b531cbbc65", "row2-0fdc5b3476fbc657", "row2-1bb909956316d10b", "row2-4db555cc13b1827d", "row2-5ab6dd480bad188b", "row2-7493b87a8cc283a0", "row2-9ad2b24411f9d730", "row2-a8c5cc2f46165173", "row2-b066b0430449659b", "row2-b24305ddb9d3ae08", "row2-b6721a365dccd8f9", "row2-b69dce1145519f59", "row2-c61b96f80629fec3", "row2-cc54285853462eac", "row2-d83e4378166a56cf", "row2-f91c4e8a4b6e7eaf", "row2-f92dd3908a0dd24e", "table-52de26d6fdc94f", "table-880a659e5d8de5", "table-ad94ede76ce1b5", "table-d04497e54145d4"], "recorded_license_kinds": ["cc-by"], "source_urls": [], "roles": ["case_fact"], "license_independently_verified_for_release": false}
+{"doi": "10.3390/polym16233253", "doi_url": "https://doi.org/10.3390/polym16233253", "titles": ["Unveiling the Mechanics Behind Polyimide’s Friction-Greening Phenomenon"], "suites": ["generation", "source"], "case_ids": ["evidence2-0ce17f0fa3efc352", "evidence2-2c3d404523204903", "evidence2-2c3d404523204903-card", "evidence2-2c3d404523204903-quote", "evidence2-45529f4e998d02a0", "evidence2-45529f4e998d02a0-card", "evidence2-45529f4e998d02a0-quote", "evidence2-4a818c38d825c1ce", "evidence2-4bf385a3b1ae666d", "evidence2-4bf385a3b1ae666d-card", "evidence2-4bf385a3b1ae666d-quote", "evidence2-511f05abc2f33c9d", "evidence2-54fcee38ce91e0e1", "evidence2-56e29f260c2484cc", "evidence2-56e29f260c2484cc-card", "evidence2-56e29f260c2484cc-quote", "evidence2-6c80ec4f5b340e63", "evidence2-7ab7a7e3100a6cdd", "evidence2-9bbea62dd5e08985", "evidence2-b3ec8ea9646285c8"], "recorded_license_kinds": ["cc-by"], "source_urls": [], "roles": ["case_fact"], "license_independently_verified_for_release": false}
+{"doi": "10.3390/polym17060753", "doi_url": "https://doi.org/10.3390/polym17060753", "titles": ["Polyimide Modified with Different Types and Contents of Polar/Nonpolar Groups: Synthesis, Structure, and Dielectric Properties"], "suites": ["generation", "source"], "case_ids": ["evidence2-174eba639c2fd4f6", "evidence2-1fa65f6e500f61d3", "evidence2-22cebfded7887e62", "evidence2-22cebfded7887e62-card", "evidence2-22cebfded7887e62-quote", "evidence2-291f00f380d36341", "evidence2-291f00f380d36341-card", "evidence2-291f00f380d36341-quote", "evidence2-4af82c36d57a63ca", "evidence2-4d3aef3983bd4677", "evidence2-580cdbbf70cf7a22", "evidence2-65e1f429d4503d9d", "evidence2-6cc24eca624f8483", "evidence2-87dd7937c6bf810d", "evidence2-8c6b39c804511e15", "evidence2-a94ed83deaa53ee1", "evidence2-a94ed83deaa53ee1-card", "evidence2-a94ed83deaa53ee1-quote", "evidence2-b9890d45e7ef5e93", "evidence2-c58fa139909e7cba", "evidence2-de94e0721a124b30", "evidence2-e8fb1f3012fef0cc", "evidence2-f204ae83251edc35", "evidence2-f543f1c92570be34", "evidence2-f543f1c92570be34-card", "evidence2-f543f1c92570be34-quote", "evidence2-f927f2c5e4425a98"], "recorded_license_kinds": ["cc-by"], "source_urls": [], "roles": ["case_fact"], "license_independently_verified_for_release": false}
+{"doi": "10.3390/polym18040522", "doi_url": "https://doi.org/10.3390/polym18040522", "titles": ["Polyimide Reinforced with Graphene/Metal Oxide Nanocomposites: Surface Degradation Study Under Atomic Oxygen"], "suites": ["generation", "source"], "case_ids": ["evidence2-059c2ca9f89142c3", "evidence2-07b4b81b277d44ec", "evidence2-091ab920f352558b", "evidence2-1c922f25c597ace6", "evidence2-31ce5daf70c830f2", "evidence2-3a93428253b544b4", "evidence2-42c134305965e96c", "evidence2-42c134305965e96c-card", "evidence2-42c134305965e96c-quote", "evidence2-6a49b954d5313826", "evidence2-6c5fc4d9eef1f657", "evidence2-7193b5fd12f9adea", "evidence2-73c8e285a5e9cb74", "evidence2-76c4c21344ece7d6", "evidence2-76c4c21344ece7d6-card", "evidence2-76c4c21344ece7d6-quote", "evidence2-777383bc5c174f4d", "evidence2-7fbb9b2fc9c9822a", "evidence2-87d17e1431d2cc7e", "evidence2-93eade4a96e602a7", "evidence2-9f5898ec3bf3bb34", "evidence2-a4e93517fb1b1a14", "evidence2-aef4de84247fe88f", "evidence2-b1ce9ecc1aaee9a5", "evidence2-b51357cf79f1cb81", "evidence2-c5c50dbf3769ecb8", "evidence2-cdde37c866162343", "evidence2-d25e849102f4bfbf", "evidence2-d7a67cef55787d6a", "evidence2-deea761918aa691f", "evidence2-dfdce2204057d832", "evidence2-e067e5a1b094aba4", "evidence2-e067e5a1b094aba4-card", "evidence2-e067e5a1b094aba4-quote", "evidence2-ee3c7a5e49b3d35a", "evidence2-f1f764d0d757a7b8", "evidence2-f1f764d0d757a7b8-card", "evidence2-f1f764d0d757a7b8-quote", "evidence2-f7a170f263a9786d", "evidence2-f9f7e49e73987809", "row2-5fd1b4ca59d0f6c1", "row2-8f0c690ca70bd58b", "row2-985c4cd646acf22b", "row2-9a527f2fb8b3d76f", "row2-e5da501754a919a3"], "recorded_license_kinds": ["cc-by"], "source_urls": [], "roles": ["case_fact"], "license_independently_verified_for_release": false}
+{"doi": "10.3390/polym18080948", "doi_url": "https://doi.org/10.3390/polym18080948", "titles": ["Effect of hBN Particle Size and Content on the Tribological Properties of Polysiloxane-Containing Polyimide Composite Coatings Under Unlubricated Conditions."], "suites": ["workflow"], "case_ids": ["independent_966cf073ae124733"], "recorded_license_kinds": [], "source_urls": ["https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13120450/fullTextXML"], "roles": [], "license_independently_verified_for_release": false}
+{"doi": "10.3390/polym18091016", "doi_url": "https://doi.org/10.3390/polym18091016", "titles": ["High-T<sub>g</sub> Polyimide Matrix Composites via Backbone Ethynyl Crosslinking: Preparation and Short-Term High-Temperature Performance."], "suites": ["workflow"], "case_ids": ["independent_9adf7e3973257d74"], "recorded_license_kinds": [], "source_urls": ["https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13165319/fullTextXML"], "roles": [], "license_independently_verified_for_release": false}
+{"doi": "10.3390/polym9100511", "doi_url": "https://doi.org/10.3390/polym9100511", "titles": ["Synthesis and Electrochromism of Highly Organosoluble Polyamides and Polyimides with Bulky Trityl-Substituted Triphenylamine Units"], "suites": ["generation", "source"], "case_ids": ["evidence2-107a764c6b5b94e3", "evidence2-19dcf4a13dbfb01f", "evidence2-34fd48c8480f6dd1", "evidence2-3753ce2aef0743f6", "evidence2-3753ce2aef0743f6-card", "evidence2-3753ce2aef0743f6-quote", "evidence2-3c0beafb59424b9f", "evidence2-43a976052e8102ec", "evidence2-43d61e22923b1fae", "evidence2-46c0c1156f58bab2", "evidence2-50453eeb5f507b74", "evidence2-5115fc5f1e868d84", "evidence2-5b8205f4f7e07125", "evidence2-69947993ade2dc99", "evidence2-6b54fc29df83c3cd", "evidence2-6d1ae6312384b7a8", "evidence2-7ac2ec3dbab4bfec", "evidence2-7ac2ec3dbab4bfec-card", "evidence2-7ac2ec3dbab4bfec-quote", "evidence2-7aca661283a2ae30", "evidence2-7b7c9c77c40e1aec", "evidence2-7c41a236796fe8f3", "evidence2-7ed0b0e62011fa8c", "evidence2-8161e3701be1c339", "evidence2-84b3d963afceb7a3", "evidence2-cde21602dbeaf713", "evidence2-e0085ef5ceb197cd", "evidence2-e0085ef5ceb197cd-card", "evidence2-e0085ef5ceb197cd-quote", "evidence2-e9318b33be965e93", "evidence2-eecadd2307220b51", "evidence2-eecadd2307220b51-card", "evidence2-eecadd2307220b51-quote"], "recorded_license_kinds": ["cc-by"], "source_urls": [], "roles": ["case_fact"], "license_independently_verified_for_release": false}
+{"doi": "10.3390/polym9100520", "doi_url": "https://doi.org/10.3390/polym9100520", "titles": ["Development of Solution-Processable, Optically Transparent Polyimides with Ultra-Low Linear Coefficients of Thermal Expansion"], "suites": ["generation", "source"], "case_ids": ["evidence2-1143aa693644c5f7", "evidence2-14a2386a0f09b9b1", "evidence2-1934b3fe72aed784", "evidence2-1dafffde8dd75540", "evidence2-25e222754ee2d3a5", "evidence2-2876139b1953575f", "evidence2-2ee1cc18b1e3fe46", "evidence2-2ee1cc18b1e3fe46-card", "evidence2-2ee1cc18b1e3fe46-quote", "evidence2-3583c8e51e851a57", "evidence2-3583c8e51e851a57-card", "evidence2-3583c8e51e851a57-quote", "evidence2-36b463a91a0f26d7", "evidence2-3b60283c3eae6689", "evidence2-3b9ac75dad57c5f4", "evidence2-3d790898fbb9c5d0", "evidence2-40028095070589a4", "evidence2-58db3f761a9522d8", "evidence2-67cf31dbf8920dc1", "evidence2-67cf31dbf8920dc1-card", "evidence2-67cf31dbf8920dc1-quote", "evidence2-69c1dc68efe13c12", "evidence2-6b50b961b17b7155", "evidence2-7db2ddc5d20bcf98", "evidence2-7ed6c75270533bef", "evidence2-7f97e41237dee902", "evidence2-8a8d21236c29a96c", "evidence2-9a0269323a149f64", "evidence2-9f4249262eae7eaa", "evidence2-a093975cc8dddd4b", "evidence2-a093975cc8dddd4b-card", "evidence2-a093975cc8dddd4b-quote", "evidence2-a624cddfa4834a2c", "evidence2-b0c2f5d848d07248", "evidence2-c575a48821e4919d", "evidence2-d81271cce8e83321", "evidence2-d890b69aa46eec47", "evidence2-df05f6eb12ada854", "evidence2-e267007b87fe2aad", "evidence2-f659d8514334b9e4"], "recorded_license_kinds": ["cc-by"], "source_urls": [], "roles": ["case_fact"], "license_independently_verified_for_release": false}

requirements-viewer.txt

@@ -0,0 +1,2 @@
+datasets==5.0.1
+pyarrow==25.0.1

requirements.txt

@@ -0,0 +1 @@
+jsonschema==4.26.0

scoring/common_scoring.py

@@ -0,0 +1,51 @@
+"""Portable frozen cross-family scorer. Logic unchanged; imports are local."""
+import json, math
+from generation_legacy import generation_checks
+from source_benchmark_scoring import score_raw
+
+def strict_json(text):
+ def pairs(values):
+  result={}
+  for k,v in values:
+   if k in result:raise ValueError('duplicate_json_key')
+   result[k]=v
+  return result
+ def reject(value):raise ValueError('nonfinite_json_constant')
+ def finite_float(value):
+  number=float(value)
+  if not math.isfinite(number):raise ValueError('nonfinite_json_float')
+  return number
+ return json.loads(text,object_pairs_hook=pairs,parse_constant=reject,parse_float=finite_float)
+
+def without_terminal_eos(raw,eos_text):
+ if eos_text:
+  assert raw.endswith(eos_text),'terminal EOS must correspond to actual last generated token'
+  return raw[:-len(eos_text)]
+ return raw
+
+def score_generation(raw,reference,tools,eos_text=''):
+ from jsonschema import Draft202012Validator
+ text=without_terminal_eos(raw,eos_text)
+ expected=[c.get('function',c) for c in reference.get('tool_calls',[])]
+ if not expected:return generation_checks(text,reference,tools)
+ result={'nonempty':bool(text.strip()),'expected_tool_call':True,'scientific_correctness':'pending_domain_review','tool_schema_valid':False,'tool_names_match_reference':False,'tool_arguments_match_reference':False,'answer_repaired':False,'generated_tool_calls':[],'tool_errors':[]}
+ schemas={x['function']['name']:x['function']['parameters'] for x in tools or []}
+ try:
+  obj=strict_json(text)
+  if not isinstance(obj,dict) or set(obj)!={'tool_calls'} or not isinstance(obj['tool_calls'],list) or not obj['tool_calls']:raise ValueError('tool_calls_object_required')
+  calls=[]
+  for c in obj['tool_calls']:
+   if not isinstance(c,dict) or set(c)!={'name','arguments'} or not isinstance(c['name'],str) or not isinstance(c['arguments'],dict):raise ValueError('invalid_call_fields')
+   if c['name'] not in schemas:raise ValueError('unknown_tool')
+   Draft202012Validator(schemas[c['name']]).validate(c['arguments']);calls.append(c)
+  normalized=[{'name':c['name'],'arguments':strict_json(c['arguments']) if isinstance(c.get('arguments'),str) else c.get('arguments',{})} for c in expected]
+  result.update(tool_schema_valid=True,generated_tool_calls=calls,tool_names_match_reference=[c['name'] for c in calls]==[c['name'] for c in normalized],tool_arguments_match_reference=calls==normalized)
+ except Exception as exc:
+  if not isinstance(exc,(ValueError,TypeError,KeyError)) and not exc.__class__.__module__.startswith('jsonschema'):raise
+  result['tool_errors']=[str(exc).splitlines()[0]]
+ return result
+
+def score_source(case,reference,raw,eos_text=''):
+ # Original source scorer strips exactly one specified terminal EOS, no fence repair.
+ if eos_text:assert raw.endswith(eos_text)
+ return score_raw(case,reference,raw,eos_text)

scoring/generation_legacy.py

@@ -0,0 +1,68 @@
+"""Verbatim extracted legacy scoring functions; see provenance/scorer_port.json."""
+import json
+import re
+
+def clean_answer(text):
+    text = re.sub(r'<think>.*?</think>', '', text, flags=re.S)
+    return text.replace('<|im_end|>', '').replace('<|endoftext|>', '').strip()
+
+def parse_json(text):
+    text = re.sub(r'^```(?:json)?\s*|\s*```$', '', clean_answer(text))
+    return json.loads(text, parse_constant=lambda value: (_ for _ in ()).throw(ValueError(value)))
+
+def generation_checks(text, reference, tools):
+    from jsonschema import Draft202012Validator
+    result = {'nonempty': bool(clean_answer(text)), 'scientific_correctness': 'pending_domain_review'}
+    expected = [call.get('function', call) for call in reference.get('tool_calls', [])]
+    schema = {t['function']['name']: t['function']['parameters'] for t in tools or []}
+    calls, errors = [], []
+    blocks = re.findall(r'<tool_call>\s*(.*?)\s*</tool_call>', text, re.S)
+    for block in blocks:
+        match = re.fullmatch(r'<function=([^>]+)>\s*(.*?)\s*</function>', block, re.S)
+        if not match:
+            errors.append('原生函数标签不完整'); continue
+        name, body = match.groups()
+        parameters = list(re.finditer(r'<parameter=([^>]+)>\s*(.*?)\s*</parameter>', body, re.S))
+        remaining = re.sub(r'<parameter=[^>]+>.*?</parameter>', '', body, flags=re.S).strip()
+        arguments = {}
+        try:
+            assert not remaining and name in schema
+            for parameter in parameters:
+                key, raw = parameter.groups()
+                assert key not in arguments
+                definition = schema[name].get('properties', {}).get(key, {})
+                arguments[key] = raw.strip() if definition.get('type') == 'string' else parse_json(raw)
+            Draft202012Validator(schema[name]).validate(arguments)
+            calls.append({'name': name, 'arguments': arguments})
+        except (AssertionError, ValueError, TypeError) as error:
+            errors.append(str(error) or '未知工具或参数重复')
+        except Exception as error:
+            if error.__class__.__module__.startswith('jsonschema'):
+                errors.append(str(error).splitlines()[0])
+            else:
+                raise
+    if '<tool_call>' in text and len(blocks) != text.count('<tool_call>'):
+        errors.append('工具调用未闭合')
+    result.update(expected_tool_call=bool(expected), generated_tool_calls=calls, tool_errors=errors)
+    if expected:
+        normalized = [{'name': c['name'], 'arguments': parse_json(c['arguments'])
+                       if isinstance(c.get('arguments'), str) else c.get('arguments', {})} for c in expected]
+        result['tool_schema_valid'] = bool(calls) and not errors
+        result['tool_names_match_reference'] = [c['name'] for c in calls] == [c['name'] for c in normalized]
+        result['tool_arguments_match_reference'] = calls == normalized
+    else:
+        content = reference.get('content') or ''
+        try:
+            expected_json = parse_json(content)
+        except (ValueError, TypeError):
+            expected_json = None
+        result['expected_json'] = expected_json is not None
+        if expected_json is not None:
+            try:
+                actual = parse_json(text)
+                result.update(json_valid=True, exact_json_reference_match=actual == expected_json)
+                if isinstance(expected_json, dict):
+                    result['reference_top_level_keys_present'] = isinstance(actual, dict) and set(expected_json) <= set(actual)
+            except (ValueError, TypeError):
+                result.update(json_valid=False, exact_json_reference_match=False, reference_top_level_keys_present=False)
+    return result

scoring/source_benchmark_scoring.py

@@ -0,0 +1,68 @@
+"""只按冻结原文核验原始JSON、引用位置和数值字面量；不修写模型答案。"""
+import json
+
+CARD_FIELDS = {'doi', 'locator', 'section', 'evidence_spans', 'numeric_literals_with_units',
+               'claim_status', 'condition_policy', 'experiment_status', 'inference_boundary'}
+
+def _reject_constant(value):
+    raise ValueError('JSON不能包含非有限常量: ' + value)
+
+def _unique_object(pairs):
+    result = {}
+    for key, value in pairs:
+        if key in result:
+            raise ValueError('JSON含重复字段: ' + key)
+        result[key] = value
+    return result
+
+def _interval(source, span, text_key='text'):
+    if not isinstance(span, dict):
+        return False
+    start, end, text = span.get('start'), span.get('end'), span.get(text_key)
+    return (type(start) is int and type(end) is int and isinstance(text, str) and bool(text)
+            and 0 <= start < end <= len(source) and source[start:end] == text)
+
+def score_raw(case, reference, raw_text, eos_text):
+    text = raw_text[:-len(eos_text)] if eos_text and raw_text.endswith(eos_text) else raw_text
+    result = {'id': case['id'], 'source_group': case['source_group'], 'kind': case['kind'],
+              'json_valid': False, 'identifiers_match': False, 'mechanical_grounding_pass': False,
+              'answer_repaired': False, 'scientific_entailment_evaluated': False}
+    try:
+        value = json.loads(text, parse_constant=_reject_constant, object_pairs_hook=_unique_object)
+    except (ValueError, TypeError):
+        return result
+    if not isinstance(value, dict):
+        return result
+    result['json_valid'] = True
+    result['identifiers_match'] = value.get('doi') == reference['doi'] and value.get('locator') == reference['locator']
+    source = case['source']['source_text']
+    if case['kind'] == 'quote_locator':
+        position = {'start': value.get('start'), 'end': value.get('end'), 'text': value.get('quote')}
+        result['quote_match'] = value.get('quote') == reference['quote']
+        result['interval_matches_source'] = _interval(source, position)
+        result['exact_reference_position'] = value.get('start') == reference['start'] and value.get('end') == reference['end']
+        result['mechanical_grounding_pass'] = (result['identifiers_match'] and result['quote_match']
+                                               and result['interval_matches_source'] and result['exact_reference_position'])
+        return result
+    assert case['kind'] == 'source_card'
+    spans = value.get('evidence_spans')
+    spans = spans if isinstance(spans, list) else []
+    valid = [_interval(source, item) for item in spans]
+    result['schema_fields_present'] = CARD_FIELDS <= value.keys()
+    result['evidence_span_count'] = len(spans)
+    result['valid_evidence_span_count'] = sum(valid)
+    result['all_nonempty_spans_supported'] = bool(spans) and all(valid)
+    literals = value.get('numeric_literals_with_units')
+    result['numeric_list_valid'] = isinstance(literals, list) and all(isinstance(x, str) for x in literals)
+    predicted = set(literals) if result['numeric_list_valid'] else set()
+    expected = set(reference['numeric_literals_with_units'])
+    result['numeric_literal_true_positives'] = len(predicted & expected)
+    result['numeric_literal_false_positives'] = len(predicted - expected)
+    result['numeric_literal_false_negatives'] = len(expected - predicted)
+    result['numeric_literals_exact'] = result['numeric_list_valid'] and predicted == expected
+    expected_coverage = {i for item in reference['evidence_spans'] for i in range(item['start'], item['end'])}
+    actual_coverage = {i for item, ok in zip(spans, valid) if ok for i in range(item['start'], item['end'])}
+    result['reference_character_coverage'] = len(expected_coverage & actual_coverage) / len(expected_coverage) if expected_coverage else None
+    result['mechanical_grounding_pass'] = (result['schema_fields_present'] and result['identifiers_match']
+                                           and result['all_nonempty_spans_supported'] and result['numeric_literals_exact'])
+    return result

scripts/score.py

@@ -0,0 +1,63 @@
+"""Score frozen generation/source tasks. CPU only; no model or network access."""
+import argparse, collections, json, sys
+from pathlib import Path
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT / "scoring"))
+from common_scoring import score_generation, score_source
+
+def rows(path):
+    return [json.loads(line) for line in Path(path).read_text().splitlines() if line.strip()]
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--suite", required=True, choices=["generation", "source"])
+    p.add_argument("--predictions", required=True)
+    p.add_argument("--output", required=True)
+    a = p.parse_args()
+    cases = rows(ROOT / "inputs" / (a.suite + ".jsonl"))
+    refs = {x["id"]: x["reference"] for x in rows(ROOT / "references" / (a.suite + ".jsonl"))}
+    key = "unit_id" if a.suite == "generation" else "id"
+    by_id = {x[key]: x for x in cases}
+    predictions = rows(a.predictions)
+    ids = [x["id"] for x in predictions]
+    if len(ids) != len(set(ids)):
+        raise ValueError("duplicate prediction IDs")
+    if set(ids) - by_id.keys():
+        raise ValueError("unknown prediction IDs")
+    values = []
+    for pred in predictions:
+        case = by_id[pred["id"]]
+        raw = pred["raw_text"]
+        if not isinstance(raw, str):
+            raise TypeError("raw_text must be a string")
+        eos = pred.get("terminal_eos_text", "")
+        if not isinstance(eos, str):
+            raise TypeError("terminal_eos_text must be a string")
+        if eos and pred.get("terminal_eos_verified") is not True:
+            raise ValueError("verify terminal token ID before specifying terminal_eos_text")
+        score = (score_generation(raw, refs[pred["id"]], case.get("tools", []), eos)
+                 if a.suite == "generation" else score_source(case, refs[pred["id"]], raw, eos))
+        values.append({"id": pred["id"], "kind": case.get("diagnostic_kind", case.get("kind")), "scores": score})
+    groups = {}
+    metric_names = ({"nonempty","json_valid","reference_top_level_keys_present","exact_json_reference_match",
+                     "tool_schema_valid","tool_names_match_reference","tool_arguments_match_reference"}
+                    if a.suite == "generation" else
+                    {"json_valid","identifiers_match","schema_fields_present","all_nonempty_spans_supported",
+                     "numeric_literals_exact","mechanical_grounding_pass","quote_match",
+                     "interval_matches_source","exact_reference_position"})
+    for kind in sorted({x["kind"] for x in values}):
+        group = [x["scores"] for x in values if x["kind"] == kind]
+        groups[kind] = {m: {"passed": sum(s[m] for s in group if type(s.get(m)) is bool),
+                            "scored": sum(type(s.get(m)) is bool for s in group)}
+                        for m in sorted(metric_names) if any(type(s.get(m)) is bool for s in group)}
+    out = {"suite": a.suite, "expected": len(cases), "submitted": len(values),
+           "complete": len(values) == len(cases), "missing_ids": sorted(by_id.keys() - set(ids)),
+           "scientific_content_review_completed": False, "metrics_by_kind": groups, "items": values}
+    dest = Path(a.output)
+    if dest.exists():
+        raise FileExistsError("refusing to overwrite scores")
+    dest.write_text(json.dumps(out, ensure_ascii=False, indent=2) + "\n")
+    print(json.dumps({k:v for k,v in out.items() if k not in ["items","missing_ids"]}, ensure_ascii=False))
+
+if __name__ == "__main__":
+    main()

scripts/validate_package.py

@@ -0,0 +1,44 @@
+"""Validate manifest, fixed counts, row identity, source intervals, and portable scoring."""
+import hashlib, json, sys
+from pathlib import Path
+ROOT = Path(__file__).resolve().parents[1]
+def rows(path):
+    return [json.loads(l) for l in path.read_text().splitlines() if l.strip()]
+manifest = json.loads((ROOT / "MANIFEST.sha256.json").read_text())
+for name, expected in manifest["files"].items():
+    assert hashlib.sha256((ROOT / name).read_bytes()).hexdigest() == expected, name
+for suite, count in [("generation",772),("source",152),("workflow",12)]:
+    original = rows(ROOT / "inputs" / (suite + ".jsonl"))
+    view = rows(ROOT / "data" / suite / "test.jsonl")
+    assert len(original) == len(view) == count
+    ids = [x["id"] for x in view]
+    assert len(set(ids)) == count
+    for a,b in zip(original,view):
+        assert json.loads(b["input_json"]) == a
+        assert json.loads(b["messages_json"]) == a.get("messages", [])
+    if suite != "workflow":
+        refs = {x["id"]:x["reference"] for x in rows(ROOT / "references" / (suite + ".jsonl"))}
+        assert set(refs) == set(ids)
+        for x in view:
+            assert json.loads(x["reference_json"]) == refs[x["id"]]
+source = {x["id"]: x for x in rows(ROOT / "inputs/source.jsonl")}
+for x in rows(ROOT / "references/source.jsonl"):
+    ref = x["reference"]
+    text = source[x["id"]]["source"]["source_text"]
+    spans = (ref["evidence_spans"] if "evidence_spans" in ref else
+             [{"start":ref["start"],"end":ref["end"],"text":ref["quote"]}])
+    for span in spans:
+        assert text[span["start"]:span["end"]] == span["text"], x["id"]
+sys.path.insert(0, str(ROOT / "scoring"))
+from common_scoring import score_generation, score_source
+tools = [{"function":{"name":"measure","parameters":{"type":"object","properties":{"temperature":{"type":"number"}},"required":["temperature"],"additionalProperties":False}}}]
+ref = {"tool_calls":[{"function":{"name":"measure","arguments":{"temperature":300}}}]}
+good = '{"tool_calls":[{"name":"measure","arguments":{"temperature":300}}]}'
+assert score_generation(good,ref,tools)["tool_arguments_match_reference"]
+for bad in [good.replace("300",'"300"'), good.replace("measure","unknown"),good.replace("300","1e999"),"\u0060\u0060\u0060json\n"+good+"\n\u0060\u0060\u0060"]:
+    assert not score_generation(bad,ref,tools)["tool_schema_valid"]
+case={"id":"test","source_group":"test","kind":"quote_locator","source":{"source_text":"abc"}}
+qref={"doi":"test","locator":"p1","quote":"abc","start":0,"end":3}
+assert score_source(case,qref,json.dumps(qref))["mechanical_grounding_pass"]
+assert not score_source(case,qref,json.dumps({**qref,"end":2}))["mechanical_grounding_pass"]
+print(json.dumps({"passed":True,"cases":936,"manifest_files":len(manifest["files"])}))