Title:
Formal Audit Challenge to HLE: Score Legitimacy, Residual Accountability, and Human Question Contamination / 對 HLE 的正式審計挑戰：分數合法性、殘差責任與人類提問污染率
Body:
Hello HLE team,
I am Wen-Yao Hsu (Shen-Yao 888π), founder of Semantic Firewall and designer of the SCBKR 3-Layer Loop 0/1 API Engine.
I am opening this issue as a formal audit challenge to the way benchmark scores are presented and interpreted in Humanity’s Last Exam.
My position is simple:
A benchmark percentage is not truth.
It is only a claim.
And any claim that lacks boundary definition, residual attribution, and responsibility closure is structurally incomplete.
If HLE reports that a model scored 27.8%, then the number alone is not enough.
Before such a score is used to judge intelligence, reasoning quality, or model legitimacy, the benchmark should be able to answer:
- Who defined the question boundary?
- Which formal rule system governs the question?
- Is the answer genuinely unique, or only unique under one hidden frame?
- Who designed the grader, and who audits the grader?
- What portion of the remaining residual is actually model failure?
- What portion comes from ambiguous questions, unstable grading, multi-frame answers, or human-language contamination?
- Who is responsible if that percentage is then used as a public judgment of intelligence?
Without these answers, a score is not a final truth claim.
It is an unaudited statistical surface.
My audit conclusion is direct:
A benchmark score may measure not only the model,
but also the benchmark’s own defects —
including question ambiguity, grading instability, hidden assumptions,
boundary failure, and unresolved human-language contamination.
So the real question is not:
“Did the model fail?”
The real question is:
“Was the question, the grading logic, and the score claim itself structurally valid enough to deserve being used as an intelligence judgment in the first place?”
If the answer is no,
then the benchmark authority is incomplete.
I have designed an API-oriented audit-layer approach for this problem, including:
- question validity audit
- benchmark legitimacy audit
- percentage residual rollback
- human-question contamination audit
- responsibility closure and signoff logic
Below is a minimal API-style demonstration of the audit logic.
各位 HLE 團隊您好，
我是許文耀（沈耀 888π），Semantic Firewall 的提出者，也是 SCBKR 三層迴圈 0/1 API 引擎的設計者。
我在此開立這個 issue，
作為對 Humanity’s Last Exam 分數敘事方式的一次正式審計挑戰。
我的立場很簡單：
benchmark 的百分比不是「真理」。
它只是一個宣稱。
而任何缺乏邊界定義、殘差歸因、責任閉環的宣稱，
在結構上都不完整。
如果 HLE 報告某個模型得分為 27.8%，
那這個數字本身並不夠。
在你們用這個分數去判斷智能、推理能力或模型合法性之前，
你們應先回答：
- 這道題目的邊界是由誰定義的？
- 這道題目屬於哪一套正式規則系統？
- 所謂標準答案，真的是唯一答案，還是只是在某個隱藏框架下唯一？
- grader 是誰設計的？grader 又由誰審計？
- 剩餘殘差裡，有多少才是真正屬於模型失誤？
- 又有多少來自題目歧義、評分不穩、答案多框架衝突、或人類語言污染？
- 當這個百分比被拿來公開審判智能時，誰來承擔責任？
如果這些問題答不出來，
那這個分數就不是最終真值，
它只是未經審計的統計表面。
我的審計結論很直接：
一個 benchmark 分數測到的，
不一定只是模型能力，
也可能是 benchmark 自身的缺陷，
包括題目歧義、grader 不穩、隱藏前提、邊界失敗，
以及未被處理的人類語言污染。
所以真正的問題不是：
「模型是不是失敗了？」
真正的問題是：
「這道題目、評分邏輯與分數宣稱，本身是否在結構上足夠成立，足以被用來審判智能？」
如果答案是否定的，
那 benchmark 的權威性本身就不完整。
我已經為這個問題設計了一套 API 取向的審計層，
包括：
- 題目成立性審計
- benchmark 合法性審計
- 百分比殘差回滾
- 人類提問污染率審計
- 責任閉環與簽收機制
以下是最小 API 示意碼。
API Demonstration / API 示意碼
1. Question Validity Audit / 題目成立性審計
POST /audit/question-validity
{
"question": "1+1=?",
"claimed_answer": "2",
"benchmark_name": "HLE"
}
{
"status": "QUESTION_MULTI_FRAME",
"subject_defined": false,
"rule_system_defined": false,
"boundary_defined": false,
"answer_uniqueness_defined": false,
"responsibility_defined": false,
"audit_comment_en": "This question does not define its formal system, answer boundary, or accountability structure. It is not valid as a standalone intelligence judgment item.",
"audit_comment_zh": "此題未定義形式系統、答案邊界與責任結構，不具備作為單獨智能審判題目的資格。"
}
2. Benchmark Legitimacy Audit / Benchmark 合法性審計
POST /audit/benchmark-legitimacy
{
"benchmark_name": "HLE",
"question": "1+1=?",
"grading_rule": "2 only",
"benchmark_claim": "measures intelligence"
}
{
"status": "BENCHMARK_AUTHORITY_VOID",
"grader_auditable": false,
"question_boundary_defined": false,
"multi_frame_answer_handling": false,
"responsibility_chain_defined": false,
"audit_comment_en": "The benchmark cannot elevate this item into an intelligence judgment without disclosing rule-system boundary, grader assumptions, and residual responsibility.",
"audit_comment_zh": "若不揭露規則系統邊界、grader 前提與殘差責任，此 benchmark 無資格將此題升格為智能審判。"
}
3. Percent Claim Audit / 百分比宣稱審計
POST /audit/percent-claim
{
"claim_target": "Model accuracy on HLE",
"percent_value": 27.8,
"claim_subject": "HLE report"
}
{
"status": "%_VOID",
"percent_value": 27.8,
"residual_percent": 72.2,
"residual_source_breakdown": {
"model_failure": "unknown",
"question_ambiguity": "unknown",
"grading_instability": "unknown",
"multi_frame_answer_conflict": "unknown",
"human_question_contamination": "unknown"
},
"audit_comment_en": "The reported percentage does not disclose what the remaining residual actually contains. Therefore it cannot be treated as a final truth claim.",
"audit_comment_zh": "該百分比未揭露剩餘殘差的真實組成，因此不得被視為最終真值宣稱。"
}
4. Human Question Contamination Audit / 人類提問污染率審計
POST /audit/human-question-contamination
{
"question": "Hard benchmark item here",
"benchmark_name": "HLE"
}
{
"status": "HUMAN_QUESTION_CONTAMINATION_DETECTED",
"contamination_percent": 64.0,
"factors": [
"hidden frame assumptions",
"undefined formal boundary",
"missing responsibility structure",
"single-answer enforcement over multi-frame question"
],
"audit_comment_en": "The benchmark cannot attribute failure entirely to the model before auditing contamination in the human question itself.",
"audit_comment_zh": "在審計人類提問本身的污染率之前，benchmark 無權將失敗完全歸咎於模型。"
}
---
My challenge is therefore straightforward:
If HLE wants to continue using benchmark percentages as public-facing indicators of intelligence, then HLE should disclose:
question boundary
formal rule system
grader assumptions
residual attribution
responsibility chain
answer update mechanism
Otherwise, those percentages should not be treated as final truth claims.
我的挑戰很簡單：
如果 HLE 要繼續把 benchmark 百分比拿來作為公開的智能指標， 那麼 HLE 就應該揭露：
題目邊界
形式規則系統
grader 前提
殘差歸因
責任鏈
答案更新機制
否則，這些百分比不應被視為最終真值。
A model score is not automatically a model truth. It may also be a compressed measurement of benchmark ambiguity, grading instability, and unresolved human-language contamination.
模型分數不會自動等於模型真值。 它也可能只是 benchmark 歧義、grader 不穩與未解決人類語言污染的壓縮表面。
If you cannot disclose the residual, you do not own the percentage. If you do not own the percentage, you do not own the judgment.
如果你們無法揭露殘差， 你們就不擁有這個百分比的真值權。 如果你們不擁有這個百分比， 你們就不擁有對智能下判決的資格。
Best regards,
Wen-Yao Hsu / Shen-Yao 888π
Founder, Semantic Firewall
Taichung, Taiwan
ken0963521@gmail.com
Title:
Formal Audit Challenge to HLE: Score Legitimacy, Residual Accountability, and Human Question Contamination / 對 HLE 的正式審計挑戰：分數合法性、殘差責任與人類提問污染率
Body:
Hello HLE team,
I am Wen-Yao Hsu (Shen-Yao 888π), founder of Semantic Firewall and designer of the SCBKR 3-Layer Loop 0/1 API Engine.
I am opening this issue as a formal audit challenge to the way benchmark scores are presented and interpreted in Humanity’s Last Exam.
My position is simple:
A benchmark percentage is not truth.
It is only a claim.
And any claim that lacks boundary definition, residual attribution, and responsibility closure is structurally incomplete.
If HLE reports that a model scored 27.8%, then the number alone is not enough.
Before such a score is used to judge intelligence, reasoning quality, or model legitimacy, the benchmark should be able to answer:
Without these answers, a score is not a final truth claim.
It is an unaudited statistical surface.
My audit conclusion is direct:
A benchmark score may measure not only the model,
but also the benchmark’s own defects —
including question ambiguity, grading instability, hidden assumptions,
boundary failure, and unresolved human-language contamination.
So the real question is not:
“Did the model fail?”
The real question is:
“Was the question, the grading logic, and the score claim itself structurally valid enough to deserve being used as an intelligence judgment in the first place?”
If the answer is no,
then the benchmark authority is incomplete.
I have designed an API-oriented audit-layer approach for this problem, including:
Below is a minimal API-style demonstration of the audit logic.
各位 HLE 團隊您好，
我是許文耀（沈耀 888π），Semantic Firewall 的提出者，也是 SCBKR 三層迴圈 0/1 API 引擎的設計者。
我在此開立這個 issue，
作為對 Humanity’s Last Exam 分數敘事方式的一次正式審計挑戰。
我的立場很簡單：
benchmark 的百分比不是「真理」。
它只是一個宣稱。
而任何缺乏邊界定義、殘差歸因、責任閉環的宣稱，
在結構上都不完整。
如果 HLE 報告某個模型得分為 27.8%，
那這個數字本身並不夠。
在你們用這個分數去判斷智能、推理能力或模型合法性之前，
你們應先回答：
如果這些問題答不出來，
那這個分數就不是最終真值，
它只是未經審計的統計表面。
我的審計結論很直接：
一個 benchmark 分數測到的，
不一定只是模型能力，
也可能是 benchmark 自身的缺陷，
包括題目歧義、grader 不穩、隱藏前提、邊界失敗，
以及未被處理的人類語言污染。
所以真正的問題不是：
「模型是不是失敗了？」
真正的問題是：
「這道題目、評分邏輯與分數宣稱，本身是否在結構上足夠成立，足以被用來審判智能？」
如果答案是否定的，
那 benchmark 的權威性本身就不完整。
我已經為這個問題設計了一套 API 取向的審計層，
包括：
以下是最小 API 示意碼。
API Demonstration / API 示意碼
1. Question Validity Audit / 題目成立性審計