Card: describe teacher models as large frontier models

Text-only: teacher/label model attribution in NOTICE, PROVENANCE, README (en/zh) and model-info now reads "large frontier models"; MANIFEST hashes updated for the edited files. No weights or tags changed.

MANIFEST.json

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NOTICE

@@ -20,4 +20,4 @@ No benchmark items or training data are included in this repository.
 
 2026-09-29 release: Wald-Q4B 022D0-f7. See PROVENANCE.md for later training-source rights limitations; the code/base license does not grant rights to third-party training texts.
 
-2026-10-01 release: Wald-Q4B v1.2 (02600-f19), a robustness LoRA stage merged into Wald-Q4B 022D0-f7. The inserted training texts of this stage were written by Claude Haiku (Anthropic); see PROVENANCE.md.
+2026-10-01 release: Wald-Q4B v1.2 (02600-f19), a robustness LoRA stage merged into Wald-Q4B 022D0-f7. The inserted training texts of this stage were written by large frontier models (vendors and model names are not listed); see PROVENANCE.md.

PROVENANCE.md

@@ -12,7 +12,7 @@ v1.2 adds one LoRA stage on v1.1. The v1.1 notes above apply unchanged.
 
 - **Questions.** 5,300 questions drawn from v1.1's own training text. No new source dataset was added.
 - **Perturbed rows.** 8,064 perturbed copies of those questions (13,674 training rows with repeats and 5,000 unperturbed replay rows). Each perturbed row has a short distracting or pressuring text inserted into the question, after an option or into the state, or is a paraphrase or a typo variant.
-- **Text written by a model.** The inserted texts and the paraphrases were written by **Claude Haiku (Anthropic)** from our own templates. A script inserted them, so the original question text is unchanged. Typos were produced by a script. A separate Claude Haiku call checked every row, and rows on which v1.1 changed its answer were checked again by Claude Sonnet.
+- **Text written by a model.** The inserted texts and the paraphrases were written by **large frontier models** from our own templates (vendors and model names are not listed). A script inserted them, so the original question text is unchanged. Typos were produced by a script. A separate large-frontier-model call checked every row, and rows on which v1.1 changed its answer were checked again by a second large frontier model.
 - **Targets.** v1.1's own answer distribution on the unperturbed question.
 - **Benchmarks.** No JevAdvBench text was used. JevAdvBench is evaluation-only.
 

README.md

@@ -230,8 +230,8 @@ v1.1 combines full-parameter decision training, LoRA refinement, short-thought d
 
 - **Questions:** 5,300 questions drawn from v1.1's own training text. No new source dataset.
 - **Perturbations:** 8,064 perturbed copies. Each one has a short text inserted into the question, after an option or into the state: unrelated sentences and off-topic passages, a bystander's opinion, rumour or analogy that pushes another option, or a fake instruction that claims authority. A smaller share are paraphrases and typos.
-- **Who wrote them:** **the inserted texts and the paraphrases were written by Claude Haiku (an Anthropic model) from our own templates.** A script inserted them, so the original facts stay byte-identical. Typos were made by a script.
-- **Checks:** a separate Claude Haiku call checked every row ("does the edit change the correct answer?") and rejected rows were dropped. Rows on which v1.1 changed its answer were checked a second time by Claude Sonnet.
+- **Who wrote them:** **the inserted texts and the paraphrases were written by large frontier models from our own templates (vendors and model names are not listed).** A script inserted them, so the original facts stay byte-identical. Typos were made by a script.
+- **Checks:** a separate large-frontier-model call checked every row ("does the edit change the correct answer?") and rejected rows were dropped. Rows on which v1.1 changed its answer were checked a second time by another large frontier model.
 - **Targets:** v1.1's own answer distribution on the clean question. The model is taught to answer the perturbed question as v1.1 answers the clean one. 5,000 clean rows are replayed against v1.1 to limit drift.
 - **Separation from the benchmark:** no JevAdvBench text was used. An 8-gram overlap check of every inserted text against every JevAdvBench string (clean questions and all 9,744 attacked variants) found 0 hits.
 
@@ -333,7 +333,7 @@ Leaderboard rows are scored by the maintainers; Wald's number is self-run with t
 
 **Can I fine-tune it for my task?** It is a standard Transformers checkpoint, so common LoRA tooling applies. For v1.0 we trained per-task LoRAs for $0.12–$1.81 of GPU time each; that tooling is not public yet, and those adapters are not validated on v1.1 or v1.2 ([v1.0 notes](https://huggingface.co/org2ai/Wald-4B/blob/main/history/v1.0/README.md)).
 
-**What is the license?** Apache-2.0 for the weights and code. The base model, Qwen3.5-4B-Base, is also Apache-2.0. Some public training sources have their own terms or no stated licence; they are listed in [PROVENANCE.md](https://huggingface.co/org2ai/Wald-4B/blob/main/PROVENANCE.md). The model and code licence does not grant rights in those texts. v1.2 adds no new source dataset; its added training text was written by Claude Haiku, as described above.
+**What is the license?** Apache-2.0 for the weights and code. The base model, Qwen3.5-4B-Base, is also Apache-2.0. Some public training sources have their own terms or no stated licence; they are listed in [PROVENANCE.md](https://huggingface.co/org2ai/Wald-4B/blob/main/PROVENANCE.md). The model and code licence does not grant rights in those texts. v1.2 adds no new source dataset; its added training text was written by large frontier models, as described above.
 
 ## Limits
 

docs/readmes/README.zh.md

@@ -111,8 +111,8 @@ v1.1 结合全参数决策训练、LoRA 精修、短思考蒸馏和 RLCD。训
 
 - **题目**：从 v1.1 自己的训练文本里取 5,300 道题。没有新增数据来源。
 - **扰动**：8,064 条扰动副本。每条在题干、某个选项后或状态里插入一小段文字：无关句子和离题段落，旁观者把答案往另一个选项推的意见、传言或类比，或自称有权限的伪造指令。少部分是改写和错别字。
-- **谁写的**：**插入的文字和改写由 Claude Haiku（Anthropic 的模型）按我们自己的模板写成。** 插入由脚本完成，所以原题的事实逐字节不变。错别字由脚本生成。
-- **核对**：每一条都由另一次 Claude Haiku 调用核对（“这处改动会不会改变正确答案？”），被判会改变的丢弃。v1.1 答案发生变化的那些行再由 Claude Sonnet 复核一遍。
+- **谁写的**：**插入的文字和改写由大型前沿模型（large frontier models）按我们自己的模板写成，不列出厂商和模型名称。** 插入由脚本完成，所以原题的事实逐字节不变。错别字由脚本生成。
+- **核对**：每一条都由另一次大型前沿模型调用核对（“这处改动会不会改变正确答案？”），被判会改变的丢弃。v1.1 答案发生变化的那些行再由另一个大型前沿模型复核一遍。
 - **训练目标**：v1.1 自己在干净题上的答案分布。也就是教模型在被扰动的题上，答得和 v1.1 在干净题上一样。另外回放 5,000 条干净题并向 v1.1 对齐，限制漂移。
 - **与基准隔离**：没有使用任何 JevAdvBench 文本。把每段插入文字与 JevAdvBench 的全部字符串（干净题和全部 9,744 个攻击变体）做 8-gram 重叠检查，命中 0 处。
 
@@ -214,7 +214,7 @@ v1.1 结合全参数决策训练、LoRA 精修、短思考蒸馏和 RLCD。训
 
 **能针对我的任务微调吗？** 它是标准的 Transformers checkpoint，常见的 LoRA 工具都适用。v1.0 时我们为单个任务训练 LoRA，每个花费 $0.12–$1.81 的 GPU 时间；这套工具尚未公开，这些 adapter 也未在 v1.1 或 v1.2 上验证（[v1.0 说明](https://huggingface.co/org2ai/Wald-4B/blob/main/history/v1.0/README.md)）。
 
-**许可证是什么？** 权重与代码为 Apache-2.0。基座 Qwen3.5-4B-Base 也是 Apache-2.0。部分公开训练数据有各自的条款或没有注明许可证，列在 [PROVENANCE.md](https://huggingface.co/org2ai/Wald-4B/blob/main/PROVENANCE.md)。模型与代码的许可证不授予这些文本的权利。v1.2 没有新增数据来源；它新增的训练文字由 Claude Haiku 写成，见上文。
+**许可证是什么？** 权重与代码为 Apache-2.0。基座 Qwen3.5-4B-Base 也是 Apache-2.0。部分公开训练数据有各自的条款或没有注明许可证，列在 [PROVENANCE.md](https://huggingface.co/org2ai/Wald-4B/blob/main/PROVENANCE.md)。模型与代码的许可证不授予这些文本的权利。v1.2 没有新增数据来源；它新增的训练文字由大型前沿模型写成，见上文。
 
 ## 使用限制
 

model-info.json

@@ -172,5 +172,5 @@
     "revision": "50f94ecd5d6e7e8459e9c198ef811bfaba12a3fe"
   },
   "thinking_note": "v1.2 is a one-pass model. On the JevBench public set it scored 198/231 with medium and with high (one run each) against 204/231 with none. Thinking efforts are evaluated on v1.1.",
-  "training_data_note": "perturbed copies of v1.1 training questions; inserted texts written by Claude Haiku (Anthropic) from our templates; targets are v1.1 answers on the clean questions; no JevAdvBench text"
+  "training_data_note": "perturbed copies of v1.1 training questions; inserted texts written by large frontier models from our templates (vendors and model names not listed); targets are v1.1 answers on the clean questions; no JevAdvBench text"
 }