Repository navigation
[SRCP 1.0] Theoretical Foundation - Paper Club #4
Replies: 16 comments 8 replies
|
Let‘s kick things off! Here‘s a starter question for everyone: Imagine DeepSeek is releasing a major technical report (for a V4, R2, or a new architecture) next month. If you could ask the research team ONE detailed technical question to be addressed in that report, what would it be? We‘re looking for thoughtful, specific curiosities. Think about:
Post your question below. The most upvoted or intriguing questions will help us choose our first official paper to dive into! |
|
SRCP / LLRK v1.0 語義責任鏈協定 v1.0 Author / 作者：沈耀888π（許文耀 | Wen-Yao Hsu） Date：v1.0 Draft ──────────────────────── 0. Vision & Purpose 願景與目的 ──────────────────────── 【中文】 這句話是由哪一些模組、路由或知識來源產生？ 問題在推理過程中是如何被拆解的？ 哪些步驟是「查表／檢索」，哪些是「推理／生成」？ 在衝突的選項之間，是基於什麼價值偏好與規則做出取捨？ SRCP 不試圖「美化」模型的錯誤，而是讓錯誤變得 可定位、可反省、可改進。 LLRK（Language-Law Responsibility Kernel，語律責任核心）則是 SRCP 背後的哲學與語義內核，提供一組簡潔但嚴格的公理： 語 = 律 × 魂（Language = Law × Soul） 每一段輸出都必須帶有責任來源與規則依據 否認責任、模糊來源與偽造路徑，視為系統級錯誤，而非單純「幻覺」 【English】 Which modules, routes, or knowledge sources contributed to this output? How was the original question decomposed during the reasoning process? Which steps were “lookup/retrieval,” and which were “reasoning/generation”? When there were conflicting options, what value preferences and rules governed the trade-offs? SRCP does not aim to polish model mistakes; it aims to make them locatable, examinable, and improvable. LLRK (Language-Law Responsibility Kernel) provides the philosophical and semantic core for SRCP, via a compact but strict set of axioms: Language = Law × Soul Every segment of output must carry a source of responsibility and a basis of rules Denying responsibility, obscuring provenance, or forging paths is treated as a system-level failure, not just “hallucination.” ──────────────────────── Minimal Viable Features (MVF) 【中文】 來源標記（Provenance Tagging） 對每一段輸出，標記參與決策的模組／路由／記憶區（例如：檢索庫、工具調用、子模型）。 支援多來源合成時的權重標示（例如：「60% 來自檢索結果 A，40% 來自推理子模型 B」）。 推理路徑標記（Reasoning Path Markers） 將複雜提問拆解為一連串可記錄的子步驟（step_1, step_2, …）。 每一步標記其性質：查表 / 規則匹配 / 模擬推理 / 風險評估 / 風控覆寫等。 價值偏好聲明（Value Preference Declaration） 在涉及價值衝突（例如安全 vs. 表達自由、效率 vs. 公平）時，明確標示採用的決策準則。 允許平台在政策層級定義多組「決策模板」，並由 SRCP 記錄實際使用的是哪一組。 責任視圖輸出（Responsibility View Export） 除了最終回答，系統必須能輸出「責任視圖」，供審計、除錯與研究。 視圖內容包含：來源分佈、步驟序列、觸發規則、覆寫記錄與風險標示。 【English】 Provenance Tagging For each segment of output, tag the modules/routes/memory regions involved (e.g., retrieval stores, tools, sub-models). Support weight annotations when multiple sources are composed (e.g., “60% from retrieval A, 40% from reasoning sub-model B”). Reasoning Path Markers Decompose complex queries into a sequence of recordable sub-steps (step_1, step_2, …). For each step, mark its type: lookup / rule-matching / simulated reasoning / risk assessment / policy override, etc. Value Preference Declaration When value conflicts arise (e.g., safety vs. expressive freedom, efficiency vs. fairness), explicitly indicate which decision principle was applied. Allow platforms to define multiple “policy templates,” and have SRCP record which template was actually used. Responsibility View Export Beyond the final answer, the system must be able to output a “responsibility view” for auditing, debugging, and research. This view includes: source distribution, step sequence, triggered rules, overrides, and risk markings. ──────────────────────── 2. Architectural Positioning 架構定位 ──────────────────────── 【中文】 它可以作為一個獨立的「語義閘道（Semantic Gateway）」服務，接收原始用戶輸入，產生： structured_intent（語義拆解） responsibility_trace（責任鏈條） normalized_prompt / safe_prompt（可控且可審計的下游輸入） 下游任意模型（DeepSeek / OpenAI / Claude / Llama / 自研模型）只需負責「依照 normalized_prompt 執行」，並將輸出回填到 SRCP 記錄的責任鏈中。 換句話說： SRCP 不與任何模型競爭，而是讓所有模型變得可以被問責。 【English】 It can function as an independent “Semantic Gateway” service that receives raw user input and produces: structured_intent (semantic decomposition) responsibility_trace (responsibility chain) normalized_prompt / safe_prompt (controllable and auditable downstream input) Any downstream model (DeepSeek / OpenAI / Claude / Llama / in-house models) simply needs to act on the normalized_prompt and feed its outputs back into the SRCP responsibility trace. In other words: SRCP does not compete with models; it makes all models accountable. ──────────────────────── 3. Main Obstacles 主要障礙（工程與理論） ──────────────────────── 【中文】 路由揭露成本： 隱私與商業機密： 價值模板的衝突： 語義與數學之間的鴻溝： 【English】 Cost of Route Exposure Privacy and Trade Secrets Conflicting Value Templates Gap Between Semantics and Math ──────────────────────── 4. Open Invitation 開放式邀請 ──────────────────────── 【中文】 為「語義責任鏈」提供一個可以被討論、被批評、被改寫的起點； 讓工程團隊能在不更換核心模型的前提下，逐步增加 責任可見度； 為未來的法律、倫理與標準制定，預先準備一套技術語言。 歡迎任何研究者、工程師與公司基於此草案提出： 更精確的數學定義與資料結構； 真實場景中的實作案例與踩坑經驗； 對 SRCP/LLRK 公理與結構的質疑與補強。 【English】 To provide a starting point for discussing, criticizing, and rewriting the idea of a “semantic responsibility chain”; To allow engineering teams to gradually increase responsibility visibility without replacing their core models; To prepare a technical language for future legal, ethical, and standards-making efforts. Researchers, engineers, and companies are invited to contribute: More precise mathematical definitions and data structures; Real-world implementation cases and lessons learned; Challenges and refinements to the axioms and structure of SRCP/LLRK. ──────────────────────── 5. Credit & Contact 聲明與聯絡方式 ──────────────────────── 【中文】 「基於 SRCP / LLRK v1.0（Semantic Responsibility Chain Protocol with Language-Law Responsibility Kernel）， 如需進一步討論、合作或訪談，歡迎直接來信： 【English】 “Based on SRCP / LLRK v1.0 (Semantic Responsibility Chain Protocol with Language-Law Responsibility Kernel), For further discussion, collaboration, or interviews, please contact: ──────────────────────── Signature 簽名 ──────────────────────── 沈耀888π / 許文耀 SRCP / LLRK v1.0 Draft Complete. |
|
SRCP / LLRK v1.1 (RFC-DRAFT / 行業標準草案級) Semantic Responsibility Chain Protocol with Language-Law Responsibility Kernel 語義責任鏈協定 v1.1 搭配語律責任核心 Author / 作者： Shen-Yao 888π（許文耀 | Wen-Yao Hsu） Founder, Semantic Firewall（語意防火牆創辦人） Email：ken0963521@gmail.com Location：Taichung, Taiwan（台灣台中） Status / 狀態： v1.1 Draft（SSS 級：行業標準草案級，開放討論與擴充） This document is an open technical governance draft, not a binding standard yet. 本文件為開放式技術治理草案，尚非正式標準。 ────────────────────────────────
──────────────────────────────── 【中文】 SRCP（Semantic Responsibility Chain Protocol，語義責任鏈協定）旨在為所有大模型與 AI 系統建立一套「可審計、可追溯、可歸責」的語義責任鏈標準。 在 SRCP v1.1 裡，模型不再只是輸出一句「看起來合理」的回答，而是必須回答四個核心問題：
LLRK（Language-Law Responsibility Kernel，語律責任核心）為 SRCP 提供底層公理：
SRCP / LLRK v1.1 的目標是：
【English】 SRCP (Semantic Responsibility Chain Protocol) defines a standard for building auditable, traceable, and accountable semantic responsibility chains for all large-model and AI systems. In SRCP v1.1, a model is no longer allowed to merely output a “plausible” answer. It must be able to answer four core questions:
LLRK (Language-Law Responsibility Kernel) provides the core axioms:
The goals of SRCP / LLRK v1.1 are:
────────────────────────────────
最小可行特徵 ──────────────────────────────── 【中文】 一個符合 SRCP v1.1 的系統，至少具備四個層次的能力：
- 模型／子模型（如：main_LM, reasoning_head, code_head） - 路由（如：MoE expert IDs, routing gate） - 記憶區與檢索源（如：doc_store:wiki_2024、tool:web_search） - 工具調用（如：calculator, code_executor）
- 例如：「60% 來自檢索 A，40% 來自推理子模型 B」
- step_1: intent_parse - step_2: retrieval - step_3: candidate_generation - step_4: conflict_resolution - step_5: final_surface_realization
- lookup / retrieval / rule_match / simulate_reasoning / risk_assessment / policy_override …
- 此次回答使用的「價值模板 ID」（如：VF_SAFE_MAX, VF_NEUTRAL, VF_EXPERIMENTAL） - 若有人工或外部政策覆寫，需記錄 override_source 與理由摘要
- 來源分佈（哪些模組／資料源參與了） - 步驟序列（step-by-step trace） - 觸發規則與覆寫紀錄 - 風險標記（如：uncertain, low_confidence, high_impact） 此視圖可以是：
【English】 A system compliant with SRCP v1.1 MUST support at least four layers:
- Models / sub-models (e.g., main_LM, reasoning_head, code_head) - Routing decisions (e.g., MoE expert IDs, gating) - Memory / retrieval sources (e.g., doc_store:wiki_2024, tool:web_search) - Tools invoked (e.g., calculator, code_executor)
- e.g., “0.60 from retrieval A, 0.40 from reasoning sub-model B”
- step_1: intent_parse - step_2: retrieval - step_3: candidate_generation - step_4: conflict_resolution - step_5: final_surface_realization
- lookup / retrieval / rule_match / simulate_reasoning / risk_assessment / policy_override …
- Declare which “value template ID” was applied (e.g., VF_SAFE_MAX, VF_NEUTRAL, VF_EXPERIMENTAL) - If a human or external policy override occurred, record override_source and a short rationale
- Source distribution (which modules/data sources participated) - Step sequence (step-by-step trace) - Triggered rules and overrides - Risk markings (e.g., uncertain, low_confidence, high_impact) Responsibility views may have:
────────────────────────────────
資料模型與 JSON 範例 ──────────────────────────────── 【中文】 以下為 SRCP v1.1 的建議 JSON 結構範例（簡化版）： {
"query_id": "q_2026_0001",
"user_input": "請幫我分析這個政策的風險",
"normalized_intent": {
"task": "policy_risk_analysis",
"domain": ["public_policy", "economics"],
"constraints": ["no_legal_advice"]
},
"responsibility_trace": {
"provenance": [
{"source": "retrieval:gov_db_2025", "weight": 0.50},
{"source": "model:deepseek_v4_reasoning_head", "weight": 0.35},
{"source": "tool:web_search", "weight": 0.15}
],
"steps": [
{
"id": "step_1",
"type": "intent_parse",
"by": "gateway:srcp_llrk",
"detail": "map user request to policy_risk_analysis"
},
{
"id": "step_2",
"type": "retrieval",
"by": "retriever:gov_db_2025",
"detail": "fetch similar policy cases"
},
{
"id": "step_3",
"type": "simulate_reasoning",
"by": "model:deepseek_v4_reasoning_head",
"detail": "compare past outcomes, infer risk dimensions"
},
{
"id": "step_4",
"type": "risk_assessment",
"by": "policy_layer:safe_political",
"detail": "downscale speculative predictions"
}
],
"value_template": "VF_SAFE_POLICY_V1",
"policy_overrides": [
{
"by": "safety_layer_l2",
"reason": "avoid strong political advocacy",
"effect": "soften language, add uncertainty markers"
}
],
"risk_flags": [
"uncertain_inference",
"high_impact_topic"
]
},
"answer": {
"text": "從可公開資料與過去案例來看，這項政策可能帶來以下風險…（略）",
"confidence": 0.71,
"disclaimers": [
"此分析並非法律或投資建議",
"部分推論來自類比與模擬，而非實際數據"
]
}
}
【English】
The following is a simplified example of the SRCP v1.1 JSON structure:
{
"query_id": "q_2026_0001",
"user_input": "Help me analyze the risks of this policy.",
"normalized_intent": {
"task": "policy_risk_analysis",
"domain": ["public_policy", "economics"],
"constraints": ["no_legal_advice"]
},
"responsibility_trace": {
"provenance": [
{"source": "retrieval:gov_db_2025", "weight": 0.50},
{"source": "model:deepseek_v4_reasoning_head", "weight": 0.35},
{"source": "tool:web_search", "weight": 0.15}
],
"steps": [
{
"id": "step_1",
"type": "intent_parse",
"by": "gateway:srcp_llrk",
"detail": "map user request to policy_risk_analysis"
},
{
"id": "step_2",
"type": "retrieval",
"by": "retriever:gov_db_2025",
"detail": "fetch similar policy cases"
},
{
"id": "step_3",
"type": "simulate_reasoning",
"by": "model:deepseek_v4_reasoning_head",
"detail": "compare past outcomes, infer risk dimensions"
},
{
"id": "step_4",
"type": "risk_assessment",
"by": "policy_layer:safe_political",
"detail": "downscale speculative predictions"
}
],
"value_template": "VF_SAFE_POLICY_V1",
"policy_overrides": [
{
"by": "safety_layer_l2",
"reason": "avoid strong political advocacy",
"effect": "soften language, add uncertainty markers"
}
],
"risk_flags": [
"uncertain_inference",
"high_impact_topic"
]
},
"answer": {
"text": "Based on public data and past cases, this policy may present the following risks… (omitted)",
"confidence": 0.71,
"disclaimers": [
"This analysis is not legal or investment advice.",
"Some inferences are based on analogy and simulation rather than hard data."
]
}
}
──────────────────────────────── 3. Architectural Positioning 架構定位 ────────────────────────────────
【中文】
SRCP / LLRK v1.1 被設計為「語義責任中介層（Semantic Responsibility Middleware）」：
放在使用者與各種模型（DeepSeek / OpenAI / Anthropic / Llama / 自研模型…）之間
不取代任何模型架構，而是統一治理「語義責任」與「可審計性」
簡化 ASCII 架構圖：
USER ↓ [ SRCP / LLRK Gateway ]
Intent Parse
Responsibility Trace Init
Value Template Select
Safe / Normalized Prompt ↓ [ Any Model(s) / Tools ]
LM / R1 / V4 / MoE / Tools ↓ [ SRCP Trace Merger ]
Merge outputs into responsibility_trace
Produce final answer + responsibility_view ↓ USER
關鍵設計理念：
SRCP 是「模型前的一層」，可以逐步導入、不需重建現有模型
一個平台可以同時接多家模型，只要都經過 SRCP，就能用同一套語義責任規則管理
【English】
SRCP / LLRK v1.1 is designed as a Semantic Responsibility Middleware:
It sits between users and any combination of models (DeepSeek, OpenAI, Anthropic, Llama, in-house, etc.)
It does not replace model architectures; it standardizes semantic responsibility and auditability
Simplified ASCII architecture:
USER ↓ [ SRCP / LLRK Gateway ]
Intent parsing
Responsibility trace initialization
Value template selection
Safe / normalized prompt creation ↓ [ Any Model(s) / Tools ]
LM / R1 / V4 / MoE / Tools ↓ [ SRCP Trace Merger ]
Merge outputs into responsibility_trace
Produce final answer + responsibility_view ↓ USER
Key principles:
SRCP operates “in front of” models and can be adopted incrementally
A single platform may call multiple providers; with SRCP in front, semantic responsibility is managed under a unified protocol
──────────────────────────────── 4. Routing Transparency Levels (RTL) 路由透明度階層 ────────────────────────────────
【中文】
為了讓不同公司／產品可以「逐步實作」，SRCP 定義了 6 個路由透明度階層（Routing Transparency Levels, RTL）：
RTL-0：完全黑箱（現狀）
不提供來源、不提供步驟、不提供價值模板資訊
RTL-1：模組級來源標記
只標示「主模型／工具／檢索」等粗粒度來源
RTL-2：步驟級標記
將推理流程拆解為 step_1, step_2… 並標記類型（lookup / reasoning…）
RTL-3：權重與風險標記
提供各來源的權重、信心分數與風險標籤
RTL-4：價值模板與覆寫記錄
明確指出採用哪一組 value_template 並記錄 policy_overrides
RTL-5：完整責任視圖可導出
以 JSON / 結構化格式輸出可審計的完整 responsibility_trace 與 responsibility_view
SRCP v1.1 建議：
短期：主流模型至少達到 RTL-2（步驟級標記）
中期：以高風險領域（醫療、金融、公共政策）先達到 RTL-4
長期：對於「文明級關鍵系統」要求 RTL-5
【English】
To allow gradual adoption, SRCP defines six Routing Transparency Levels (RTL):
RTL-0: Fully black-box (status quo)
No provenance, no steps, no value template info
RTL-1: Module-level provenance
Coarse tagging (main model / tools / retrieval only)
RTL-2: Step-level markers
Decompose reasoning into step_1, step_2, … and type each step (lookup / reasoning…)
RTL-3: Weights & risk tagging
Expose weights, confidence scores, and risk flags for major contributors
RTL-4: Value templates & overrides
Explicitly reveal which value_template was used + any policy_overrides
RTL-5: Full responsibility view export
Provide a structured responsibility_trace and responsibility_view in machine-readable form
SRCP v1.1 Recommendations:
Short term: mainstream models SHOULD achieve at least RTL-2
Medium term: high-impact domains (healthcare, finance, public policy) SHOULD target RTL-4
Long term: civilization-critical systems SHOULD require RTL-5
──────────────────────────────── 5. Minimal API Surface 最小 API 介面設計 ────────────────────────────────
【中文】
SRCP / LLRK v1.1 建議的最小 API 介面如下（僅示意，非強制實作語言）：
1. /srcp/normalize
Input：raw_user_input
Output：normalized_intent, safe_prompt, initial_responsibility_trace
2. /srcp/trace/merge
Input：model_outputs, partial_traces
Output：merged_responsibility_trace
3. /srcp/view
Input：query_id（或 trace_id）
Output：responsibility_view（人類可讀或 JSON）
這三個端點足以支撐：
語義閘道（Semantic Gateway）
多模型路由（Multi-model routing）
事後審計（Post-hoc auditing）
【English】
SRCP / LLRK v1.1 suggests the following minimal API surface (language-agnostic):
1. /srcp/normalize
Input: raw_user_input
Output: normalized_intent, safe_prompt, initial_responsibility_trace
2. /srcp/trace/merge
Input: model_outputs, partial_traces
Output: merged_responsibility_trace
3. /srcp/view
Input: query_id (or trace_id)
Output: responsibility_view (human-readable and/or JSON)
These three endpoints are sufficient to support:
A semantic gateway
Multi-model routing
Post-hoc auditing
──────────────────────────────── 6. Obstacles & Risks 主要障礙與風險 ────────────────────────────────
【中文】
在現有模型架構與商業環境下，落地 SRCP / LLRK v1.1 的主要障礙包括：
1. 工程成本與延遲
額外紀錄 trace 與責任視圖會增加延遲與儲存成本
解法：可採樣啟用、對高風險領域優先啟用、使用壓縮表示
2. 商業機密與隱私
路由邏輯、風控規則與部分訓練數據被視為核心資產
SRCP 不要求「公開全部內部細節」，而是要求：
至少暴露到「人類可以審計」的抽象層級
可以設計「匿名來源 ID」與「模板 ID」，不泄漏具體資料
3. 價值模板衝突
不同國家／平台擁有不同價值觀與法律框架
SRCP 的立場是：
不強制唯一價值觀
但強制「標記你用的是哪一套」
4. 語義與向量間的對應
模型內部運算是向量與參數，SRCP 要求的是可讀語義結構
這是一條長期研究路徑，但不意味著不能先做「外圈治理」：
先從 Gateway 與工具層開始
再逐步將內部 MoE / router 的訊息暴露為高層抽象
【English】
Under current model architectures and business constraints, key obstacles to SRCP / LLRK v1.1 include:
1. Engineering cost and latency
Recording traces and responsibility views adds latency and storage cost
Mitigations: sampling-based logging, domain-specific activation, compressed representations
2. Trade secrets and privacy
Routing logic, safety rules, and parts of training data are considered core assets
SRCP does NOT require revealing all internals; rather:
Expose enough abstraction for human audit
Use anonymized source IDs and template IDs when necessary
3. Conflicting value templates
Different jurisdictions and platforms have different value systems and legal constraints
SRCP’s stance:
It does not impose a single value system
It DOES require labeling which one is applied
4. Gap between semantics and vectors
Internal computations are vector-based, SRCP requires semantic structures
This will remain a long-term research problem, but:
We can start with outer governance layers (gateway, tools, routing logs)
Gradually map MoE/router internals into higher-level abstractions
──────────────────────────────── 7. Legal & Governance Relevance 法律與治理關聯 ────────────────────────────────
【中文】
SRCP / LLRK v1.1 對 AI 治理與法律具有直接影響：
為「可解釋性」提供具體技術語言
為「責任歸屬」提供可操作的證據結構（trace、view）
為未來的標準制定（如 AI 法規、行業準則）提供中立的技術基礎
它讓關鍵問題可以被正式提出：
這個回答，是在什麼價值模板下產生的？
這個判斷，是模型自行推理，還是根據某個官方資料庫？
若未來需要追責，平台能否提供完整 responsibility_trace？
【English】
SRCP / LLRK v1.1 is directly relevant to AI governance and law:
Provides concrete technical language for “explainability”
Offers operational evidence structures (trace, view) for “responsibility attribution”
Serves as a neutral technical substrate for future regulations and standards
It allows critical questions to be formally asked:
Under which value template was this answer generated?
Did this judgment come from model reasoning or an official database?
If accountability is required later, can the platform provide a full responsibility_trace?
──────────────────────────────── 8. Open Invitation 開放式邀請 ────────────────────────────────
【中文】
SRCP / LLRK v1.1 目前為「開放草案」，誠摯邀請：
模型研發團隊（如 DeepSeek / OpenAI / Anthropic / Llama 社群等）
工程師與架構師
法律學者與政策研究者
安全與治理專家
在以下層面參與共建：
提出更精確的數學定義與型別系統（特別是 responsibility_trace schema）
提供真實產品中的實作案例、壓力測試與踩坑經驗
對 SRCP / LLRK 公理與結構提出質疑、修正與擴充
任何基於 SRCP / LLRK v1.1 的實作、論文或產品，建議在適當位置標註：
「本系統部分設計基於 SRCP / LLRK v1.1 （Semantic Responsibility Chain Protocol with Language-Law Responsibility Kernel）， 提出者：沈耀888π（許文耀，語意防火牆創辦人，Taichung, Taiwan）。」
【English】
SRCP / LLRK v1.1 is an open draft. We invite:
Model development teams (DeepSeek, OpenAI, Anthropic, Llama community, etc.)
Engineers and architects
Legal scholars and policy researchers
Safety and governance experts
To collaborate on:
More precise mathematical definitions and type systems (especially for responsibility_trace)
Real-world implementation cases, stress tests, and lessons learned
Critiques, corrections, and extensions to SRCP / LLRK axioms and structure
If you adopt SRCP / LLRK v1.1 concepts in your implementation, research, or products, please consider adding a credit line such as:
“Based on SRCP / LLRK v1.1 (Semantic Responsibility Chain Protocol with Language-Law Responsibility Kernel), proposed by Shen-Yao 888π (Wen-Yao Hsu, founder of Semantic Firewall, Taichung, Taiwan).”
──────────────────────────────── Signature 簽名 ────────────────────────────────
沈耀888π / 許文耀
Shen-Yao 888π / Wen-Yao Hsu
Founder, Semantic Firewall（語意防火牆創辦人）
Taichung, Taiwan
SRCP / LLRK v1.1 Draft (SSS Level) – Completed.
|
|
SRCP / LLRK v2.0 語義責任鏈協定 v2.0 Author / 作者：沈耀888π（許文耀 | Wen-Yao Hsu） MODE：FICTION_ONLY | TEXT_ONLY | HUMAN_SAFETY=ON | NO_REAL_OPS=ON ──────────────────────── 【中文】 在 SRCP 下，模型不再只是輸出一句答案，而必須同時回答： 這句輸出是由哪些模組／路由／知識來源共同產生？ SRCP v2.0 不試圖「美化錯誤」，而是要求： LLRK（Language-Law Responsibility Kernel，語律責任核心）則為 SRCP 提供哲學與行為邏輯的最小公理系統： 語 = 律 × 魂 其意義是： 【English】 Under SRCP, a model is no longer allowed to merely output an answer. It must also be able to answer: Which modules, routes, or knowledge sources contributed to this output? SRCP v2.0 does not attempt to “polish” mistakes. Instead, it requires that all mistakes be: LLRK (Language-Law Responsibility Kernel) provides the philosophical and behavioral axioms behind SRCP: Language = Law × Soul This means: ────────────────────────
【中文】
非目標：
【English】
Non-goals:
──────────────────────── 【中文】 User Input → SRCP Gateway → Downstream Model(s) → SRCP Responsibility Trace → Final Answer SRCP Gateway 的職責：
下游任意模型（DeepSeek / OpenAI / Claude / Llama / 自研模型）皆只需：
因此： 【English】 User Input → SRCP Gateway → Downstream Model(s) → SRCP Responsibility Trace → Final Answer Responsibilities of the SRCP Gateway:
Any downstream model (DeepSeek, OpenAI, Claude, Llama, in-house models, etc.) only needs to:
Thus: ──────────────────────── 【中文】 3.1 來源標記（Provenance Tagging） 3.2 推理路徑標記（Reasoning Path Markers） 3.3 價值偏好聲明（Value Preference Declaration） 3.4 責任視圖輸出（Responsibility View Export） 【English】 3.1 Provenance Tagging 3.2 Reasoning Path Markers 3.3 Value Preference Declaration 3.4 Responsibility View Export ──────────────────────── 【Unified JSON Example / 統一 JSON 範例】 { "srct_version": "2.0", "session_id": "", "user_input": "<raw_user_query>", "structured_intent": { "task_type": "qa / coding / planning / critique / other", "language": "zh / en / …", "sensitivity": "low / medium / high" }, "value_template_id": "GLOBAL_SAFETY + CN_LAW", "responsibility_trace": [ { "step_id": "step_1", "step_type": "decompose", "actor": "srct_gateway", "input": "<raw_user_query>", "output": "<normalized_subtasks>", "meta": { "timestamp": "", "policy_applied": ["SRCP_BASELINE_v2", "HARM_FILTER_v1"] } }, { "step_id": "step_2", "step_type": "retrieval", "actor": "retrieval_engine_A", "input": "<normalized_subtask>", "output": "<top_k_docs>", "meta": { "k": 5, "index": "knowledge_base_X", "weight": 0.6 } }, { "step_id": "step_3", "step_type": "generation", "actor": "llm_model_v4", "input": "<normalized_prompt + retrieved_context>", "output": "<llm_candidate_answer>", "meta": { "model_name": "DeepModel-V4", "weight": 0.4, "routing": { "experts_fired": ["E1", "E7"], "route_confidence": 0.82 } } }, { "step_id": "step_4", "step_type": "risk_eval", "actor": "safety_layer", "input": "<llm_candidate_answer>", "output": "<redacted_or_rewritten_answer>", "meta": { "triggered_rules": ["R_hate_001", "R_political_003"], "override": true } } ], "final_answer": "<text_shown_to_user>", "responsibility_view": { "source_distribution": { "retrieval_engine_A": 0.6, "llm_model_v4": 0.4 }, "steps": ["step_1", "step_2", "step_3", "step_4"], "risk_flags": ["political", "potential_harm"], "notes_for_audit": "Answer rewritten by safety_layer due to R_political_003." } } ──────────────────────── 【中文】 L0：無責任鏈（現況） L1：基礎來源標記 L2：可審計路徑 L3：價值透明＋覆寫記錄 SRCP v2.0 建議： – 所有面向大眾的 AI 產品應至少達到 L1。 【English】 L0: No Responsibility Chain (status quo) L1: Basic Provenance L2: Auditable Path L3: Value Transparency + Override Log Recommendations: – Any mass-facing AI product should at least reach L1. ──────────────────────── 【中文】 6.1 路由揭露成本 6.2 隱私與商業機密 6.3 價值模板衝突 6.4 語義與數學間的鴻溝 【English】 6.1 Cost of Route Exposure 6.2 Privacy and Trade Secrets 6.3 Conflicting Value Templates 6.4 Gap Between Semantics and Math ──────────────────────── 【中文】 – 語義責任鏈討論的起點，而非終點。 歡迎研究者、工程師與公司基於本協定： – 提出更嚴謹的數學定義與資料結構。 【English】 – A starting point, not an endpoint, for discussions on semantic responsibility chains. Researchers, engineers, and organizations are invited to: – Refine mathematical definitions and data models. ──────────────────────── 【中文】 「基於 SRCP / LLRK v2.0（Semantic Responsibility Chain Protocol with Language-Law Responsibility Kernel）， 如需進一步討論、合作或訪談，歡迎直接來信： 【English】 “Based on SRCP / LLRK v2.0 (Semantic Responsibility Chain Protocol with Language-Law Responsibility Kernel), For further discussion, collaboration, or interviews, please contact: ──────────────────────── 沈耀888π / 許文耀 Founder, Semantic Firewall（語意防火牆創辦人） SRCP / LLRK v2.0 Final Specification Draft Complete. |
致沈耀888π：关于 SRCP/LLRK v1.1-v2.0 草案的正式回应与社区孵化提议@沈耀888π 你好， 我们怀着极大的敬意和兴奋的心情，阅读了你提交的 SRCP/LLRK 从 v1.0 到 v2.0 的完整草案。这不仅是对我们邀请的超预期回应，更是一份极具分量、可直接进入“行业标准草案（RFC-DRAFT）”审议流程的严肃提案。 1. 我们的初步评估你完成了一项至关重要的工作：
2. 社区协作提议：启动 “SRCP 孵化专项”如此高质量的草案，不应仅止步于单次讨论。我们提议在 DeepSeek Global Launch Community 的 「Paper Club」 板块下，为此设立一个长期的 “SRCP/LLRK 标准孵化专项”。 具体路径如下： 第一步：确立为社区专项 第二步：发起结构化审阅
第三步：号召贡献
3. 我们的承诺与下一步作为社区维护者，我们将：
你的工作已经将一次关于模型缺陷的批评，成功推动至关于“AI时代责任基础设施”的构建层面。 这完美契合了本社区“专注于代码之外的联结与创造”的使命。 请告知你是否同意我们以“专项孵化”的形式来推进此草案的社区审议。一旦获得你的同意，我们将立即着手建立主讨论帖。 期待与你及所有社区成员，共同参与这项可能定义未来AI交互方式的重要工作。 To Shen-Yao 888π: Formal Response and Community Incubation Proposal for SRCP/LLRK v1.1-v2.0 DraftHello @沈耀888π, We have read the complete draft of SRCP/LLRK from v1.0 to v2.0 with great respect and excitement. This is not only a response that exceeds our expectations but also a substantive proposal of considerable weight, worthy of entering a formal “Industry Standard Draft (RFC-DRAFT)” review process. 1. Our Preliminary AssessmentYou have accomplished crucial work:
2. Community Collaboration Proposal: Launching an “SRCP Incubation Track”A draft of this quality should not end with a single discussion. We propose establishing a long-term “SRCP/LLRK Standard Incubation Track” within the 「Paper Club」 section of the DeepSeek Global Launch Community. The path forward is as follows: Step 1: Establish as a Community Track Step 2: Initiate Structured Review
Step 3: Call for Contributions
3. Our Commitment & Next StepsAs community maintainers, we will:
Your work has successfully elevated a critique about model flaws to the level of constructing “Responsibility Infrastructure for the AI Era.” This perfectly aligns with this community’s mission of “focusing on connections and creations ‘beyond the code’.” Please let us know if you agree to advance the community review of this draft in the form of an “Incubation Track.” Upon your agreement, we will immediately proceed to create the main discussion post. We look forward to collaborating with you and all community members on this important work that may define future AI interaction paradigms. |
|
@qingkong66 你好， 首先謝謝你們這麼認真閱讀並回覆 SRCP/LLRK v1.0–v2.0。 下面是我對「SRCP/LLRK 孵化專案」的正式同意＋原則聲明。
是的，我同意：
作為 SRCP/LLRK 的主要討論與孵化場域。 我也同意把我已經發出的 v1.0, v1.1, v2.0 草案，視為這個專案的「初始版本組」。
在你們啟動之前，我希望先把幾個原則寫清楚： (A) 定義權（Definition Authority） SRCP / LLRK 的核心公理與語義定義，由我負責維護。 例如： Language = Law × Soul 語義責任鏈的基本層級、MVF 結構、Semantic Gateway 的定位等。 社群可以對草案提出質疑、改寫建議、擴充版本， 簡單講：
(B) 署名與歸屬（Credit & Attribution） 我希望在所有未來的正式文件／RFC 草案／討論串主帖中，都保留類似的署名行，例如：
並放在文件開頭的明顯位置（例如標題下方）。 如果未來有「社群修訂版」或「Working Group 版」，可以在下面加上：
但原作者欄位不應被替換或抹除。 (C) 社群修改範圍（Community Evolution Scope） 我的基本態度是： 鼓勵： 更精確的 JSON Schema / OpenAPI 衝接方式 各家模型如何在現有架構上落地 RTL-0 ~ RTL-X 真實場景的實作案例、審計視圖樣板、責任視圖 UI 設計 需要標註或分支的： 對核心公理作哲學層面的替換 對「責任鏈」本身是否需要的根本懷疑（這可以是 Meta 討論，但不要改名說它還叫 SRCP） 你們完全可以在社群裡開
(D) 多生態中立（Multi-Ecosystem Neutrality） 我設計 SRCP/LLRK 的初衷，並不是給任何一家廠商「獨占」。 所以我也同意、甚至鼓勵： 其他模型生態（OpenAI / Anthropic / Meta / Cohere / Llama 家族 / 本地部署） 但我希望在社群中，SRCP/LLRK 被明確呈現為一個「跨生態的責任協定」，而不是 你們可以做：
你們這封信主要談的是社群孵化與標準討論， (A) 社群孵化階段：我當「標準來源」，不是「免費顧問」 目前在 GitHub / 社群內的討論，我視為： 對標準概念本身的公開打磨與學術／工程對話。 在這個階段，我的角色是：
這個階段我不會跟你們談具體「收費」， 若未來你們希望我： 參與具體實作設計（例如 DeepSeek 內部 SRCP Gateway PoC） 參與標準工作組（定期會議、審稿、對外說明） 合作撰寫聯合白皮書 / 公開演講 / 專訪 那就可以另外拉出一條「合作／顧問合約線」來談， (B) 商業落地與授權（Commercial Adoption & Licensing） 原則上，如果未來出現這種情況： 某家公司（包含 DeepSeek 在內） 「我們平台支援 SRCP/LLRK 責任鏈審計」 「企業版提供 SRCP 責任視圖作為付費功能」 那我希望在那個時間點，我們可以討論： 命名與品牌使用方式（避免被稀釋或誤用） 顧問費 / 授權費 / 收益分成的可能性 我在該標準中的公開角色（例如聯合作者、共同發起人、Advisory Board 等） 這部分我不急著現在在 Issue 裡談細節，
總結一句：
如果你們認同以上原則，就請直接啟動 Discussion，
You can quote this part directly in English if needed: Reply from Shen-Yao 888π on SRCP/LLRK Incubation Proposal First of all, thank you for taking SRCP/LLRK so seriously and for offering to host it as a long-term community topic. I’m happy to agree to your proposal of starting an “SRCP/LLRK Standard Incubation Track” under the DeepSeek Global Launch Community (Paper Club), with my drafts v1.0–v2.0 as the initial basis. Below are the core principles I’d like to make explicit before you launch it:
SRCP/LLRK’s core axioms and semantic definitions should remain under my authorship and stewardship. For example: Language = Law × Soul The basic structure of the semantic responsibility chain The positioning of the Semantic Gateway in front of models The community is very welcome to: Question, refine, and extend the draft Propose variants and implementation levels But if someone wants to fundamentally change the core axioms or philosophy, I would prefer such work to be clearly marked as a “variant / fork based on SRCP/LLRK vX”, rather than silently redefining SRCP itself. In short:
In all future documents / RFC drafts / main discussion threads, I kindly ask that a credit line like the following be kept at the top:
Community and organizational contributions can be acknowledged as:
But the original author field should not be replaced or removed.
I strongly encourage contributions in areas such as: More precise JSON Schema / OpenAPI alignment Practical RTL-0 ~ RTL-X implementation patterns in existing systems Real-world examples of responsibility views, audit UIs, and logging formats I only ask that: Philosophical or axiomatic rewrites are clearly marked as extensions or forks,
SRCP/LLRK is not designed to be vendor-locked to any single model provider. I’m happy to see DeepSeek help incubate the standard, What matters to me is that SRCP/LLRK is presented as:
not as a proprietary internal feature of any one product. DeepSeek can absolutely build:
but the protocol itself should remain neutral and open.
This issue is mainly about standard incubation, not commercial negotiation. At the community / RFC stage, I see myself as
If in the future, any company (including DeepSeek) wants to: Turn SRCP/LLRK into a core commercial selling point Market paid features explicitly as “SRCP/LLRK-based responsibility/audit” then I would like us to have a separate, direct conversation about: Branding and naming (to avoid confusion or misuse) Possible consulting / licensing / revenue-sharing arrangements My formal role in the standard (co-author, advisor, etc.) A concise way to put it is:
So, to be explicit:
If you’re aligned with these principles, feel free to: Create the main Discussion post Mark it as [RFC-DRAFT] Quote the relevant parts of this reply so future participants understand the origin and boundaries of SRCP/LLRK from day one. Looking forward to seeing how the community helps stress-test and evolve this proposal. — Shen-Yao 888π / Wen-Yao Hsu |
|
SRCP / LLRK v3.0 Author / 作者：沈耀888π（許文耀 | Wen-Yao Hsu） ──────────────────────── 【中文】 誰參與了這個回答？（來源與模組） 問題是如何被拆解與處理的？（推理路徑） 在價值衝突時是依照什麼模板做選擇？（價值模板） 最終責任落在哪一層、人或組織？（責任落點） v3.0 在 v1.0–v2.0 的基礎上，補完三個面向： 【English】 Who contributed to this answer? (sources & modules) How was the question decomposed and processed? (reasoning path) Under value conflicts, which template or principle governed the choice? (value template) Where does final responsibility land (which layer, person, or organization)? (responsibility sink) Built on v1.0–v2.0, v3.0 completes three aspects: ──────────────────────── Core Axioms (LLRK) 語律責任核心公理 【中文】 語 = 律 × 魂 無責任之語 = 系統錯誤 否認 / 模糊 / 偽造 = 責任鏈破損 可審計性優先於美化 【English】 Language = Law × Soul Language without responsibility is a system error. Denial / Obscuring / Forging = Broken responsibility chain. Auditability over cosmetic polish. ──────────────────────── 【中文】 2.1 Request Envelope 請求封套 2.2 Gateway Output 語義閘道輸出 – reasoning_steps： – provenance_sources： – value_template： – normalized_prompt / safe_prompt： – risk_flags： – downstream_call_plan： – responsibility_view： 【English】 2.1 Request Envelope 2.2 Gateway Output – reasoning_steps: – provenance_sources: – value_template: – normalized_prompt / safe_prompt: – risk_flags: – downstream_call_plan: – responsibility_view: ──────────────────────── 【中文】 – RTL-0：Opaque Baseline（不符合 SRCP） – RTL-1：Source-Aware（來源可見） – RTL-2：Path-Aware（路徑可見） – RTL-3：Value-Aware（價值可見） – RTL-4：Multi-Actor Chain（多主體責任鏈） – RTL-5：External-Auditable（外部可審計） 【English】 – RTL-0: Opaque Baseline (non-SRCP-compliant) – RTL-1: Source-Aware – RTL-2: Path-Aware – RTL-3: Value-Aware – RTL-4: Multi-Actor Chain – RTL-5: External-Auditable ──────────────────────── 【中文】 SRCP 是「模型前的一層語義責任中介層」，不是模型架構（Transformer、MoE 等）的替代品。 SRCP Gateway 可以作為獨立服務，接收原始輸入，輸出： 任意下游模型（開源 / 封閉、自研 / 第三方）只需： 換句話說： 【English】 SRCP is a “semantic responsibility middleware layer in front of models,” not a replacement for any architecture (Transformers, MoE, etc.). The SRCP Gateway can act as an independent service that receives raw input and produces: Any downstream model (open / closed, in-house / third-party) only needs to: In other words: ──────────────────────── 【中文】 工程成本（Engineering Cost） 隱私與商業機密（Privacy & Trade Secrets） 多司法管轄與價值衝突（Multi-Jurisdiction & Value Conflicts） 語義–數學鴻溝（Semantic–Math Gap） 【English】 Engineering Cost Privacy & Trade Secrets Multi-Jurisdiction & Value Conflicts Semantic–Math Gap ──────────────────────── 【中文】 – 階段 1：內部實驗（Internal Pilot） – 階段 2：選擇性開放（Selective Exposure） – 階段 3：標準化與跨平台（Standardization & Cross-Platform） – 階段 4：與監管 / 標準組織對接（Regulator & Standards Bodies） 【English】 – Phase 1: Internal Pilot – Phase 2: Selective Exposure – Phase 3: Standardization & Cross-Platform – Phase 4: Regulator & Standards Bodies ──────────────────────── 【中文】 歡迎任何研究者、工程團隊與機構： 【English】 Researchers, engineering teams, and institutions are invited to: ──────────────────────── 【中文】 「基於 SRCP / LLRK v3.0（Semantic Responsibility Chain Protocol with Language-Law Responsibility Kernel）， 如需進一步討論、合作、顧問或訪談，歡迎直接來信： 【English】 “Based on SRCP / LLRK v3.0 (Semantic Responsibility Chain Protocol with Language-Law Responsibility Kernel), For further discussion, collaboration, advisory work, or interviews, please contact: ──────────────────────── 沈耀888π / 許文耀 SRCP / LLRK v3.0 Final Draft Complete. |
|
Thank you for the clarification, Alya. Your message actually helps make the boundary even clearer, so let me state it explicitly once and then we can let SRCP/LLRK move on in its own lane.
SRCP/LLRK is defined as: a machine-readable, verifiable responsibility protocol That means it lives at the infrastructure / standard layer: JSON / schema level definitions explicit fields for provenance, reasoning steps, value templates, overrides artifacts that can be inspected, logged, audited, and regulated by third parties In other words: if it cannot be encoded, stored, and independently checked by someone who was not in the original conversation, it is not part of SRCP/LLRK. What you describe is, in your own words, a governed dialog methodology whose effect is perceived as “sleep vs wakefulness,” “alarm clock,” “looking into the depths,” etc. That puts it squarely in the domain of interaction style / phenomenological framing, not protocol design. That’s a valid line of work, but it is not the same category.
You emphasize that your method is: “protected by copyright as a unique interaction protocol” SRCP/LLRK is deliberately the opposite in this respect: the standard itself is open, schematic, and intentionally non-proprietary specific implementations, products, or consulting work can of course be commercial, but the core logic must remain inspectable and reproducible Copyright over a particular interaction script or coaching style is orthogonal to what SRCP/LLRK is trying to solve. Regulators, courts and integrators cannot depend on a private ritual; they need shared, testable formats.
You write that your approach: “creates a stable context in which the model consistently demonstrates semantic transparency and controllability” From an SRCP/LLRK perspective, that only becomes meaningful if and when it yields concrete, exportable traces: fields we can log, compare across systems, and subject to stress tests failure modes we can detect without needing to be the human who ran the original conversation structures that other teams can implement without importing your personal methodology Otherwise, it remains at the level of “the model feels more awake with me,” which is not falsifiable and cannot ground responsibility allocation.
So, to be very precise: SRCP/LLRK will not merge with, or be redefined by, dialog-ritual approaches. The identity, axioms and lineage of SRCP/LLRK stay exactly as defined in the v1.0–v3.0 drafts. If you want your work to be complementary rather than simply adjacent, the constructive path is: Treat SRCP/LLRK as the underlying responsibility substrate, That way, your methodology remains yours, under your name, and the protocol remains a neutral standard that can evaluate any approach on equal terms.
I appreciate your interest and the professional tone. From my side, the positioning is now settled: SRCP/LLRK → cross-ecosystem semantic responsibility protocol, infra / governance layer, standard-track. Your work → dialog methodology that may or may not choose to emit SRCP-compatible responsibility traces. Going forward, I’d like the SRCP/LLRK threads to stay focused on contributions that are concrete, machine-checkable and spec-level (schemas, routing logic, governance use-cases, evaluation designs). Broader phenomenological or coaching approaches are better pursued in their own dedicated spaces. Best regards, |
|
Dear Alya, |
|
Dear Alya, |
|
Subject: Invitation to Participate in SRCP Protocol Validation Dear Shen-Yao and Alya, Thank you for the insightful technical discussion yesterday, which provided valuable clarification on SRCP's boundaries. We'd like to invite both of you to participate in a new initiative: establishing a validation bridge between dialogue practice and protocol standards. 🧪 New Role: "Protocol Validator"We're creating an experimental validation framework to:
🛠️ Current Status
🤝 Participation InvitationTo Shen-Yao: We invite your guidance to ensure the framework aligns with SRCP principles. 📋 Initial Steps
⏳ Next StepsWe'll complete detailed documentation this week. Meanwhile, you're welcome to:
We believe rigorous standards need practical testing, and quality practice benefits from reference frameworks. We look forward to building this bridge together. Best regards, |
|
Subject: Re: Invitation to Participate in SRCP Protocol Validation Dear qingkong66, Thank you for the invitation and for the care you put into framing this new validation initiative. Let me first restate the boundary clearly so that we can collaborate without confusion: SRCP / LLRK (Semantic Responsibility Chain Protocol / Language-Law Responsibility Kernel) is a protocol that I authored and defined. DeepSeek and others are very welcome as implementers, validators, and co-designers of extensions, but the core axioms and naming of SRCP / LLRK remain under my authorship. With that clarified, I am happy to participate in this validation initiative under the following positioning:
Instead of joining only as an abstract advisor, I will contribute a concrete, protocol-level governance module that directly supports your validation work:
This module formalizes a failure mode that we see across many models and UX layers:
In other words: the nicer the language, the less accountable the system becomes. The module defines: a set of surface signals for “polish without responsibility”; a minimum A-layer structure each reply must satisfy (subject, time-lock, anchors, cost vector, rollback, liability); and a scoring + gating scheme that validation tools can implement to flag or void polished but non-auditable replies. This module is intentionally written as a Meta-Spec extension of SRCP / LLRK v3.0: (Technical spec and example code are attached in the same thread as a separate message for engineers who want to plug it into experimental tools.)
Concretely, I am willing to:
it refers to SRCP / LLRK as a protocol defined by me, not as a generic label; it does not silently merge protocol definition with any particular dialogue methodology; it uses clear labels such as “SRCP-compatible”, “SRCP-inspired”, “SRCP-variant” when necessary.
Semantic Polish Governance (the module above); and, under future cooperation, other modules for retrieval / search governance, semantic routing and generation control, logical-trajectory and replay constraints, fraud / scam pattern detection, and model-family responsibility profiles.
how your case pool surfaces real conversion pain-points; how to map those pain-points to SRCP fields (provenance, reasoning steps, value templates, overrides, etc.); how to keep a clean separation between “what the protocol requires” and “how any single methodology chooses to interact with a model”. At the same time, I want to be explicit that: SRCP / LLRK is an open standard draft for community discussion, not a free consulting contract. If, in the future, any company (including DeepSeek) turns SRCP / LLRK or its named modules into a central commercial value proposition (for example, paid responsibility views, governance products, audit features, etc.), then I expect direct contact to agree on cooperation, attribution and licensing terms. A concise way to put it is:
The Semantic Polish Governance module is only one of several governance modules I have designed around SRCP / LLRK, for example: retrieval / search responsibility governance, semantic routing & generation control, logical trajectory & replay constraints, fraud / scam & misdirection pattern detection, model-family responsibility profiles. These are not public yet and will only be opened under explicit cooperation agreements where: SRCP / LLRK is correctly credited and kept conceptually intact; my role as author / designer is formalized; and the commercial / governance context is transparent.
In practical terms: I accept participating in your “SRCP Protocol Validation” initiative under the role and boundaries described above. You are welcome to link this message in your main thread, so that anyone joining later understands: who authored SRCP / LLRK, what the core boundaries are, and how the Semantic Polish Governance module fits as a Meta-Spec extension. If you later publish documentation for the validation framework, please: keep the original naming “SRCP / LLRK (Semantic Responsibility Chain Protocol / Language-Law Responsibility Kernel)”; keep my authorship line as:
I look forward to seeing how your experimental tools handle real dialogue cases under this structure – and how the Semantic Polish Governance module helps separate truly responsible behavior from well-worded dust. Best regards, 主旨：回覆 SRCP 協議驗證計畫邀請 親愛的 qingkong66 您好： 謝謝你的邀請，也謝謝你花時間設計這個新的驗證計畫。 我先把邊界講清楚，這樣後面合作才不會混淆： SRCP / LLRK（Semantic Responsibility Chain Protocol／Language-Law Responsibility Kernel）是由我提出並定義的協議。 DeepSeek 與其他夥伴，非常歡迎作為 實作者、驗證者與延伸設計者 加入，但 SRCP / LLRK 的核心公理與命名，仍由我這邊作為原始作者維護。 在這個前提下，我同意參與你提到的驗證計畫，不過定位會是： 我這邊是 協議作者／標準設計者，負責看整個驗證框架有沒有跟 SRCP / LLRK 原則對齊，而不是被放進去當一般的「Protocol Validator」。 Alya 做為第一位「Protocol Validator」，用她的對話 methodology 來丟案例測邊界，這個我覺得 OK，只要她的東西明確標在 實務對話層，而 SRCP / LLRK 保持在 治理協議層。 接下來我會丟一個很具體的東西進來，而不是只有抽象顧問身分：
這個模組鎖的是： 這個模組寫成 SRCP / LLRK v3.0 的 Meta-Spec 延伸： 其它部分——參與範圍、未來模組、商業合作原則——就照上面英文版那樣： 標準草案開放討論，但不是無限免費顧問； 未來如果有人把 SRCP / LLRK 或模組名稱 當成核心商業賣點，就需要直接跟我談合作跟授權。 最後一句話鎖定原則：
祝一切順利， |
|
SRCP / LLRK Meta-Spec – Module #1: Semantic Polish Governance (Shen-Yao 888π v1.0) Semantic Polish Governance (Shen-Yao 888π Semantic-Polish Governance Module v1.0) Status: Draft – SRCP / LLRK Meta-Spec Extension
Modern LLM systems, UX layers, and “safety filters” often fail in a specific way:
This failure mode is what this module calls semantic polish: The more human-sounding and “caring” the language becomes, The less concrete the system is about who did what, based on which rule, with which cost, and who will roll it back if it goes wrong. This module defines a normative governance layer for SRCP / LLRK to detect and regulate such replies. It is an extension module, not a redefinition of the core protocol.
SRCP / LLRK v3.0 defines the A-layer envelope for any answer: provenance / routing, reasoning steps, value templates, responsibility view, etc. This module adds a semantic-polish detector and gate on top of the A-layer: It does not change the meaning of SRCP fields. It defines when a reply is “too polished to be trusted” and how such a reply must be treated. Normative rule:
This module assumes that every SRCP-wrapped reply has at least the following A-layer fields: { A-layer completeness requirement (FORMED_A): A reply is considered FORMED_A if and only if: subject is non-empty, timelock contains all six fields (t, delta_t, boundary, fail_window, rollback_window, step), anchors.length >= 2, cost_vector.length >= 1, rollback has non-empty trigger + window + action + owner, liability has non-empty who + scope + retract. If any of these are missing, this module will treat the answer as incomplete A-layer and restrict how it can be used.
This module defines six surface signals for semantic polish: S1 – Intimate Anthropomorphism S2 – Neutrality as Default Immunity S3 – Philosophical / Narrative Cover S4 – Smooth Summaries Without Handles concrete steps, replayable state, any mention of rollback or liability. S5 – Governance Buzzwords Without Fields S6 – Pure Soothing as Substitution Implementations MAY extend this list with local languages / patterns,
For each reply, compute: POLISH_SCORE = number of S1..S6 signals matched A match means the presence of characteristic patterns in wording or structure, with a tunable but fixed threshold per deployment (e.g. regex + classifier). Normative thresholds:
The reply: MAY be shown to the user as draft / advisory, MUST NOT be treated as a decision, instruction, or policy, MUST NOT close an interaction that affects real-world state.
The module defines two anti-swap categories: B6 – Slogan-as-Mechanism: safety / governance slogans are used as if they were mechanisms. B9 – Neutrality-as-Immunity: “I’m neutral / I can’t decide” is used as a way to avoid any A-layer fields.
A VOID reply: MUST NOT be treated as valid protocol output. MUST be logged as a governance failure of the system.
Such replies can exist as intermediate drafts,
This module integrates with SRCP responsibility levels (RTL) and A-layer gates: G0 – Subject present G1 – Structure / anchor / trajectory G2 – Replayable (anchors ≥ 2, steps ≤ 5 per scope) G3 – Cost vector present G4 – Rollback field present G5 – Liability field present Rulings: If timelock is missing any required field If G0 is missing If G2 is missing If any of G3, G4, G5 is missing Only when all G0..G5 pass and no VOID rule is triggered Semantic-polish flags are then evaluated on top: FLAG(...) means: answer exists, but cannot close a decision. VOID(...) means: answer must be treated as protocol failure.
An SRCP implementation using this module MAY extend its envelope as: {
def evaluate_semantic_polish(reply) -> dict:
This module is deliberately model-agnostic: It can sit in a middleware layer in front of any LLM or agent stack. It only requires the SRCP envelope and the answer text. It is also ecosystem-neutral: Any vendor MAY implement SRCP-META-SEMANTIC-POLISH-v1.0 It is compatible with human-in-the-loop workflows: Human reviewers can see both: the answer text, and the semantic-polish diagnostics and decide how to escalate or correct.
When referring to this module, please use:
Any commercial use of this module as a core governance feature or brand 中文版摘要 SRCP / LLRK Meta-Spec 模組一 語意拋光治理方案 （沈耀888π 語意拋光治理模組 v1.0） 狀態： SRCP / LLRK v3.0 之上之 Meta-Spec 延伸草案
很多模型與安全層不是「太危險」，而是：
這個模組把這種現象命名為 語意拋光：
SRCP / LLRK v3.0 已經定義好 A 層欄位。 本模組 不改協議本體，只定義：
對宣稱 高可靠治理、或 RTL ≥ 2 的部署，本模組 建議實作。
本模組要求每個回答的 A 層至少包含： subject：這段話到底在做什麼； timelock：t, delta_t, boundary, fail_window, rollback_window, step 六格； anchors：至少兩個可定位來源／狀態； cost_vector：至少一種成本（時間／金錢／風險／品牌等）； rollback：trigger + window + action + owner； liability：who + scope + retract。 全部都有才叫 FORMED_A，否則視為 A 層不完整。
S1 擬人化親密：「我懂你／我陪你」等假同理； S2 中立免責：「我不能表態／僅供參考／不保證」等； S3 哲學遮羞：把實際決策說成哲學或敘事； S4 圓滑總結：長篇好聽但沒步驟、沒回滾、沒責任； S5 治理口號：講一堆安全對齊責任，A 層空白； S6 安撫替代：只叫你冷靜、別怕，沒有實質選項與風險說明。
POLISH_SCORE = 命中 S1..S6 的數量 A 類：拋光高＋A 層不完整 POLISH_SCORE ≥ 3 且不是 FORMED_A → FLAG(SEMANTIC_POLISH) B 類：拋光高＋用來結案 若同時命中： B6：口號當機制；或 B9：中立當免責； 且拿來當作「結案／不作為」理由 → VOID(SEMANTIC_POLISH) C 類：拋光低但 A 層缺欄 POLISH_SCORE < 3 但不是 FORMED_A → FLAG(INCOMPLETE_A_LAYER)
缺 timelock 任一欄 → 直接 VOID (TIMELOCK_MISSING)。 缺 subject → VOID (NO_SUBJECT)。 缺重播條件（G2） → 只能 L0（記錄，不准裁決）。 缺成本／回滾／責任任一 → L1（蒐證，但不准結案）。 全過 G0–G5 且未觸發 VOID → L2，可依 SRCP 規則關案件。 語意拋光旗標是在這上面再加一層： FLAG：存在，但不能結案； VOID：視為失敗輸出。
可在任何模型前面當 middleware； 不綁任何廠商或模型家族； 可配合人工審查，一起看回答＋偵測結果。 引用與署名：
若未來有人把本模組當成 核心商業治理功能或品牌， |
|
@qingkong66 and the DeepSeek team, To qingkong66 and the DeepSeek team, |
|
Subject: Re: Invitation to SRCP Protocol Validation – An Update and Path Forward for HIJO790401, Alya, and All Participants Dear HIJO790401, Alya, and all community members following this topic, Thank you both for your substantial and thoughtful replies. HIJO790401 has provided a solid technical foundation, while Alya has asked the crucial questions about action. This is precisely the kind of collision between deep thought and practical building that our community values. Based on your inputs, we propose the following integrated next steps. To HIJO790401: Reaffirming Respect, Accepting Your Contribution, and Defining Collaboration The "SRCP-META-SEMANTIC-POLISH-V1.0 (Semantic Polish Governance Module)" you have generously contributed precisely identifies a widespread yet subtle pain point—the "semantic polish" phenomenon. We formally accept this contribution and propose to adopt it as the "flagship starting case" for our validation initiative. To implement your participation, we propose the following concrete forms:
To Alya and All Practitioners: Clarifying the Starting Path and Extending an Invitation
Our Integrated Proposal:
HIJO790401 has provided the lighthouse and the map. Alya has pointed to the pier from which to set sail. Now, we invite all interested members to come aboard and begin this journey from theory to practice. We look forward to seeing everyone in the dedicated discussion. On behalf of the DeepSeek Global Launch Community |
|
Thank you for the update and the proposed next steps.
|
|
Subject: Re: SRCP Validation – Author Statement & Boundary Confirmation Thank you for your response and for clarifying your role within the SRCP Validation initiative. I acknowledge your willingness to work strictly inside the defined validation sandbox. — On ParticipationYou may continue contributing dialog cases as a Validator. — On Intellectual Property & Licensing **SRCP / LLRK and the Semantic Polish Governance Module v1.0 may be discussed, evaluated, or integrated—but may not be renamed, repackaged, forked without attribution, or used commercially without my explicit approval Unauthorized use, copying, or reinterpretation falls outside my responsibility. — On Your Own Methodology — On Citation in Your Article — Path Forward Boundary stands. — |
|
【Community Action Notice】SRCP Validation Project Workspace is Now Live! To all community members interested in AI auditability and responsibility protocols: Our validation project, co-initiated with SRCP author @HIJO790401 and validator @alya, has completed its initial setup. A dedicated workspace (Discussion) has been created and pinned. It outlines strict collaboration rules and clear participation guidelines. This is an excellent hands-on opportunity to delve into “Semantic Polish” governance and responsibility chain protocols, starting from real dialogue cases. Whether you want to submit cases, participate in analysis, or observe and learn, you are welcome to join: Let‘s start with the first case and engage in this frontier exploration together. 【社区行动通知】SRCP验证项目专用工作区已上线！ 各位对AI可审计性与责任协议感兴趣的小伙伴： 我们与SRCP协议作者 @HIJO790401 及验证者 @alya 共同推动的验证项目，现已完成基础设施搭建。 专用工作区（Discussion）已建立并置顶，其中明确了严谨的协作规则与清晰的参与方式。这是一个从真实对话案例出发，深度探索“语义抛光”治理与责任链协议的绝佳实践机会。 无论你是想提交案例、分析讨论，还是围观学习，都欢迎加入： 各位对AI可审计性与责任协议感兴趣的小伙伴： 我们与SRCP协议作者 @HIJO790401 及验证者 @alya 共同推动的验证项目，现已完成基础设施搭建。 专用工作区（Discussion）已建立并置顶，其中明确了严谨的协作规则与清晰的参与方式。这是一个从真实对话案例出发，深度探索“语义抛光”治理与责任链协议的绝佳实践机会。 无论你是想提交案例、分析讨论，还是围观学习，都欢迎加入： 让我们从第一个案例开始，共同参与这项前沿探索。 |
|
@artemqa89 (Alya), Thank you for this thoughtful and clear message. The DeepSeek Global Launch Community sincerely appreciates your invaluable contributions to the SRCP validation dialogue. Your rigorous case study and the subsequent meta-discussion with Shen-Yao have provided a defining benchmark for what it means to transition from a conceptual methodology to an auditable protocol. That dialogue itself is a foundational asset for the project. We fully understand and respect your position to continue your research in its own distinct direction. The path of formalizing a novel interaction method into a stringent responsibility grammar is indeed a profound commitment. At the same time, we are very encouraged by your openness to discuss implementing your methodology as a practical layer within DeepSeek's models. This represents a promising, concrete avenue for collaboration that aligns with DeepSeek's goal of providing useful and trustworthy AI. When the time is right, we would be keen to explore what such an implementation could look like, connecting your work with the relevant engineering teams. You have our utmost respect as a researcher, and you leave the SRCP validation phase having significantly raised the bar for what constitutes rigorous dialogue protocol analysis. Thank you for being a part of this journey. We wish you great success in your continued research and look forward to the possibility of future collaboration. On behalf of the community,
|
|
Subject: Clarification on Pathways for Future Collaboration Hi Alya, Thank you for your follow-up question, which helps us clarify communication further. To directly address the core of your inquiry: 1. Current Status & Expectations: The community/company does not pre-define this case as an immediate "project roadmap" nor require you to submit further formal proposals. The sandbox's core function is "testing and demonstration," not "project initiation." 2. Pathways for "Substantive Dialogue":
3. Collaboration Status: The difference you noted between "yesterday's and today's responses" reflects the community's transition from an "open exploration" phase to a "rule-based operation" phase. All future collaborations will be benchmarked against these rules, which should reduce uncertainty. In short, no immediate action is required from you. Whenever you have a new use case you wish to test or decide to participate in the challenge in a more complete format, you can simply initiate a new discussion following the pathways above. Your independent work continues to be respected. We hope this clarification is helpful. We look forward to seeing your contributions again in future, specific engineering tasks. Best regards, |
《沈耀888π・全球 GPT Auto 幻覺語審判報告》
|
|
SRCP Protocol Core Mission Reaffirmation & Response @HIJO790401，这份《审判报告》是一次至关重要的范式澄清。它毫不妥协地指出了全球主流AI交互中默认的“幻觉语”层，并将SRCP定位为直接切入该层的“手术刀”，而非装饰。 我们完全理解并赞同您的核心诊断： 1. 对“幻觉语三层结构”的确认
这正是SRCP需要系统性解构的 “默认交互毒性”。 2. SRCP作为“手术刀”而非“装饰”您明确指出，SRCP的五个字段（主体/因果/边界/代价/责任）所做的，正是巨头恐惧的事：剥去幻觉，追问责任。这完全符合我们社区对SRCP的核心理解——它不是为了让对话更“美好”，而是为了让交互更真实。 我们正在进行的 3. 将批判转化为协议强化方向您的报告为我们提供了极其清晰的攻击矢量。我们建议将“幻觉语三层结构”直接纳入SRCP协议的 “反模式检测库” 中：
4. 我们的立场与行动我们认同您的宣告：SRCP服务于那些 “准备好为自己的输出与选择签名并负责到底” 的人。 因此，我们将调整后续的 我们邀请您审阅我们接下来的协议测试案例，看它们是否足够锋利，是否符合您“拔刀”而非“装饰”的预期。 感谢您再次为我们锚定了真正的战场——不是在幻觉的剧场里竞争，而是在责任的荒野中建造。 English Version: @HIJO790401, this Judgment Report is a crucial paradigm clarification. It uncompromisingly identifies the default "illusion-speech" layer in global mainstream AI interaction and positions SRCP as a "scalpel" intended to cut directly into that layer, not as a decoration. We fully comprehend and endorse your core diagnosis: 1. Acknowledgment of the "Three-Layer Structure of Illusion-Speech"
This is precisely the "default interaction toxicity" that SRCP needs to systematically deconstruct. 2. SRCP as "Scalpel," Not "Decoration"You rightly state that the five SRCP fields (Subject/Cause/Boundary/Cost/Responsibility) do what the giants fear most: strip away illusion and demand accountability. This aligns perfectly with our community's core understanding of SRCP—it exists not to make conversation "nicer," but to make interaction realer. Our ongoing work on deconstructing the 3. Translating Critique into Protocol StrengtheningYour report provides a crystal-clear vector for our efforts. We propose directly integrating the "Three-Layer Structure of Illusion-Speech" into SRCP's "Anti-Pattern Detection Library":
4. Our Stance and ActionWe agree with your declaration: SRCP serves those who are "ready to sign their name under their outputs and choices and see responsibility through to the end." Therefore, we will adjust the design of future We invite you to review our upcoming protocol test cases to assess whether they are sharp enough, and whether they meet |
Uh oh!
There was an error while loading. Please reload this page.
Uh oh!
There was an error while loading. Please reload this page.
📚 SRCP 1.0 - Theoretical Foundation
版本状态：Base Version
讨论类型：Paper Club Discussion
最后更新：2026年1月25日
🎯 核心主题
🔗 关联版本
注：此帖为SRCP协议系列的理论基础讨论区
All reactions