Remove development experiment results

README.md

@@ -50,7 +50,6 @@ Each task provides 5,000 or 10,000 texts and a research objective. Agents choose
 | `output.schema.json` | Submission structure |
 | `protocol.json`, `release.json` | Evaluation protocol and data counts |
 | `manifest.json` | File hashes and sizes |
-| `results/agent-runs.json` | Development experiment summary; not a leaderboard |
 
 Sources: Amazon Beauty, Android App Reviews, CFPB and NHTSA. The task inventory contains 20 group differences, 15 temporal changes and 15 compound associations. See [data composition and fields](docs/DATA.md).
 
@@ -70,10 +69,8 @@ The code pins dataset commits in `benchmark/data.lock.json`. Add `--with-learnin
 
 [Connect an agent and score results](docs/USAGE.md) · [中文使用指南](docs/USAGE.zh-CN.md) · [Code](https://github.com/erwinmsmith/TextInsightBench) · [Evaluation assets](https://huggingface.co/datasets/CodeSoulco/TextInsightBench-Evaluation)
 
-## Evaluation and results
+## Evaluation
 
 Full assignment partitions, quotations and arithmetic are checked locally. Sampled claim-blind document checks cap subsequent finding-quality grades. Semantic review incurs model charges and is not exhaustive or independent ground truth. The tasks have no fixed reference conclusions; quality is judged against corpus evidence and the public rubric. [Scoring](docs/SCORING.md).
 
-The development experiment finished 150 runs across three open-source agents: 86 valid submissions, 70 numerical scores, 16 evidence-unresolved and 64 without valid submissions. Configurations changed during development; this is not a controlled leaderboard. [Results and limitations](docs/RESULTS.md).
-
 Task and learning document IDs are disjoint, but the data was previously public, entities and sources can overlap, and tasks are not statistically independent. Narratives are unverified author reports and may contain personal information. Upstream terms differ; the compilation grants no new rights over third-party text. See [source terms](SOURCES.md).

README.zh-CN.md

@@ -13,7 +13,6 @@
 - `learning/*/*.parquet`：可选无标签学习池。
 - `output.schema.json`：输出格式。
 - `protocol.json`、`release.json`、`manifest.json`：协议、数量与文件校验。
-- `results/agent-runs.json`：开发实验统计，不是排行榜。
 
 语料来自 Amazon Beauty、Android App Reviews、CFPB 和 NHTSA。任务包含 20 道群体差异、15 道时间变化、15 道复合关联。
 
@@ -33,10 +32,8 @@ tib verify --data data/participant
 
 [接入、续跑及评分](docs/USAGE.zh-CN.md) · [数据字段](docs/DATA.md) · [代码仓库](https://github.com/erwinmsmith/TextInsightBench) · [测评资源](https://huggingface.co/datasets/CodeSoulco/TextInsightBench-Evaluation)
 
-## 评估与结果
+## 评估
 
 本地全量检查分类、引用与算术；模型先盲检抽样原文，再评审发现质量。语义评审收费、不是全量确认，也不等于独立 ground truth。任务没有固定参考结论，按原文证据及公开规则评分。
 
-已有三个开源 Agent 的 150 次运行全部结束：86 份有效提交、70 份数值评分、16 份证据未决、64 次未产生有效提交。配置在开发中调整过，不是控制条件一致的排行榜。[完整实验结果](docs/RESULTS.zh-CN.md)。
-
 任务与学习池 ID 不重叠，但数据此前公开，实体和来源可共享，任务并非统计独立。原文是未经核实的作者叙述，可能含个人信息。[来源与使用条款](SOURCES.md)。

docs/RESULTS.md

@@ -1,41 +0,0 @@
-# Experimental results
-
-**English** | [简体中文](RESULTS.zh-CN.md)
-
-Completed September 13, 2026. Three open-source agents were each run on all 50 tasks using the full 5,000- or 10,000-document task corpus. The optional learning pool was not used.
-
-## Coverage and scores
-
-| Agent | Runs finished | Valid submissions | Scored | Evidence unresolved | No valid submission | Scored-only mean / 100 |
-|---|---:|---:|---:|---:|---:|---:|
-| DatawiseAgent | 50/50 | 31 | 25 | 6 | 19 | 19.57 |
-| MetaGPT Data Interpreter | 50/50 | 29 | 22 | 7 | 21 | 11.95 |
-| TaskWeaver | 50/50 | 26 | 23 | 3 | 24 | 13.88 |
-| Total | 150/150 | 86 | 70 | 16 | 64 | 15.30 |
-
-Of the 70 scored runs, 35 scored zero and 35 scored above zero. The highest task score was 48.13. No full-benchmark quality mean is available for any agent. Missing, invalid and unresolved results are not converted to zero or silently excluded from coverage.
-
-A finished run is a terminated attempt, not necessarily native-agent success or a valid answer. Some native failures left a valid final artifact that could still be evaluated. The machine-readable [run summary](../results/agent-runs.json) preserves native status separately from evaluation status. Five interrupted assessments were recovered using their existing submissions.
-
-## Experimental conditions
-
-- Solver and judge model ID: `deepseek-flash`, recorded as DeepSeek Flash in the experiment manifests. The same model served both roles; judgments are not independent human validation.
-- Track: `task_only`. Agents received the task, full local corpus and submission contract, not reference answers. They controlled their native exploration and code-execution loops; the harness did not impose per-document model calls.
-- Solver wall budget: 20 minutes per task. Judge wall budget: 10 minutes per assessment attempt; recovery attempts were additional. Native message, step, tool-output and execution budgets changed during development.
-- DatawiseAgent moved from a smaller step budget to 40 steps. MetaGPT changed from plan-and-act to a bounded ReAct loop. TaskWeaver used a 24-message limit, with tool-output limits adjusted. Interface and judge-format compatibility were repaired during the experiment.
-- Scoring used the frozen evidence-gated evaluator. The summary records evaluator file hashes and agent adapter hashes; it does not claim every run used identical orchestration.
-- The public release contains the benchmark runner and scorer, not the local experiment adapters, credentials or raw execution logs. The summary permits checking aggregate calculations, not reproducing every historical run byte for byte.
-
-| Agent | Upstream source | Pinned commit |
-|---|---|---|
-| DatawiseAgent | [DatawiseAgent](https://github.com/zimingyou01/DatawiseAgent) | `64f3164869fa2558e7385d0e33a241aee2baf37f` |
-| MetaGPT Data Interpreter | [MetaGPT](https://github.com/FoundationAgents/MetaGPT) | `c036574507e7616c02512e7c8ad88dd847783afa` |
-| TaskWeaver | [TaskWeaver](https://github.com/microsoft/TaskWeaver) | `d44ddef23f90059fb17999d3095db4240e98f955` |
-
-## Interpretation
-
-These runs show practical challenges in completing the submission contract and producing evidence-supported, substantive discoveries. They do **not** isolate mining ability from framework reliability, budget limits or model behavior.
-
-This is a cumulative development experiment, **not a controlled leaderboard or an unbiased estimate of unseen-task generalization**. Configurations changed across cohorts, tasks were used during development, corpus data was already public, and semantic review is sampled and fallible. The conditional means must not be used to rank agent architectures under supposedly matched conditions.
-
-For a new comparison, freeze agent and evaluator configurations, report every task and failure, preserve corpus and code hashes, and publish score coverage alongside quality. See [usage](USAGE.md) and [scoring](SCORING.md).

docs/RESULTS.zh-CN.md

@@ -1,36 +0,0 @@
-# 实验结果
-
-[English](RESULTS.md) | **简体中文**
-
-实验于 2026 年 9 月 13 日完成。三个开源 Agent 各运行全部 50 题，使用每题完整的 5,000 或 10,000 篇语料，不使用可选学习池。
-
-## 进度与分数
-
-| Agent | 运行已结束 | 有效提交 | 已评分 | 证据未决 | 未产生有效提交 | 已评分均分 / 100 |
-|---|---:|---:|---:|---:|---:|---:|
-| DatawiseAgent | 50/50 | 31 | 25 | 6 | 19 | 19.57 |
-| MetaGPT Data Interpreter | 50/50 | 29 | 22 | 7 | 21 | 11.95 |
-| TaskWeaver | 50/50 | 26 | 23 | 3 | 24 | 13.88 |
-| 合计 | 150/150 | 86 | 70 | 16 | 64 | 15.30 |
-
-70 份已评分中，35 份零分、35 份非零，最高单题 48.13。三个 Agent 均没有完整的全题均分；缺失、无效和未决不按零分处理，也不从覆盖统计中隐去。
-
-运行结束表示尝试已终止，不等于框架成功或答案有效。部分原生运行失败前留下了可评估的有效提交。[逐次运行摘要](../results/agent-runs.json)分别保留原生状态与评估状态；5 次中断的评审基于原有提交恢复完成。
-
-## 条件
-
-使用模型 ID `deepseek-flash`，求解与评审均为 DeepSeek Flash，不是独立人工验证。赛道为 `task_only`，Agent 读取完整本地语料，自主执行其原生探索和代码循环，没有统一强制逐文档调用模型。
-
-每题求解时间预算 20 分钟，每次评审尝试 10 分钟，恢复评审额外计算。开发中调整过原生步数、工具输出与执行预算：DatawiseAgent 步数提高至 40；MetaGPT 从 plan-and-act 改为有限步 ReAct；TaskWeaver 使用 24 条消息上限并调整工具输出限制。另做过接口及评审格式兼容修复。
-
-评分使用冻结的证据约束评分器。逐次摘要记录 Agent 提交、适配器哈希及评分器文件哈希。上游项目及固定提交见[英文结果页](RESULTS.md#experimental-conditions)。
-
-公开包包含 benchmark 运行器及评分器，不包含本地实验适配代码、密钥或原始运行日志。摘要可核对汇总数字，不代表所有历史运行均可逐字复现。
-
-## 如何理解
-
-结果说明：完成输出规范、提出有原文证据且有分析深度的发现，确实存在实际挑战。但它不能将挖掘能力与框架可靠性、资源限制、模型行为完全分离。
-
-这是累计开发实验，**不是控制条件一致的排行榜，也不是未见任务泛化能力的无偏估计**。不同批次配置有变化，题目参与过开发，语料此前公开，语义评审又是抽样且可能有误，不能据此严格排名 Agent 架构。
-
-新的对比应冻结配置，报告所有失败、分数及覆盖率，保留代码和数据哈希。[使用指南](USAGE.zh-CN.md) · [评分规范](SCORING.md)

docs/USAGE.md

@@ -97,4 +97,4 @@ Completed compatible reviews are reused on rerun. Keep `judge_config.json` and t
 
 Scores range from 0 to 100. Zero means a scored but unsupported/unfulfilled finding, not a missing run. A task score averages its submitted findings; any unresolved finding makes task quality unavailable.
 
-Keep the code commit, data lock, agent commit/configuration, prompts, model IDs, resource budgets and review configuration with your report. Freeze a configuration before making controlled comparisons. The published [development results](RESULTS.md) document different conditions and are not a leaderboard.
+Keep the code commit, data lock, agent commit/configuration, prompts, model IDs, resource budgets and review configuration with your report. Freeze a configuration before making controlled comparisons.

docs/USAGE.zh-CN.md

@@ -76,4 +76,4 @@ evaluate 同时写 JSON 和 Markdown，已有报告不会覆盖，重新汇总
 
 分数为 0–100。零分是已评审但发现不成立或未完成目标，不是缺失运行。每题平均其发现得分；任一发现未决，该题质量分为空。
 
-保存代码提交、数据锁、Agent 提交及配置、提示词、模型 ID、预算和评审配置。严格对比前应冻结配置；已有[开发实验结果](RESULTS.zh-CN.md)不代表控制条件一致的排行榜。
+保存代码提交、数据锁、Agent 提交及配置、提示词、模型 ID、预算和评审配置。严格对比前应冻结配置。

docs/VERIFICATION.md

@@ -8,8 +8,6 @@ Synthetic tests exercise agent-selected group/date comparisons, forbidden
 filters, overlapping groups, minimum population sizes, exact partitions,
 Simpson-style reversals, missing metadata, counterexamples, quotation offsets,
 score bindings, null reference coverage and bounded reproducible semantic packets.
-The separate [agent experiment](RESULTS.md) covers 150 completed attempts.
-It is not an independent validation set or a controlled leaderboard.
 
 ```bash
 python -m unittest discover -s tests -v
@@ -20,5 +18,3 @@ The data builder deterministically selects disjoint IDs from an already curated
 pool, preserves original text, enriches released metadata and filters selected
 documents out of the remaining pool. It records exact input shard hashes.
 Current-snapshot disjointness does not erase historical public exposure.
-Observed completion and quality results are reported separately from these
-software checks; they do not establish difficulty under matched conditions.

manifest.json

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