KICS认知公尺完整体系：从概念到可运行的量化模型与Dashboard

KICS认知公尺完整体系从概念到可运行的量化模型与Dashboard摘要KICS贾子逆能力得分是衡量大语言模型逆向验证与元推理深度的新兴指标。本文系统整理其基本概念、基础版$KICS\frac{\sum w_i I(Valid_i)}{D_i}$与扩展版五大维度加权公式并阐述计算流程、实际意义与局限。进一步文章发展出可直接运行的Python计算类与图表化Dashboard支持单模型、团队及系统级聚合评估能与传统正向能力得分对比。该体系已具备工程落地能力用于检测模型“会说不会验”的幻觉风险为逻辑校准与对抗鲁棒性提供量化工具。KICS 认知公尺完整体系整理一、KICS 基本概念与技术框架KICSKucius Inverse Capability Score贾子逆能力得分是近期在中文 AI 技术讨论中提出的概念属于贾子逆算子Kucius Inverse Operator, KIO框架的一部分。该框架旨在解决大语言模型常见的幻觉、逻辑断裂和事实性错误问题通过 逆向 机制增强模型的元推理深度和逻辑严密性。一核心含义逆向能力 强调模型不仅能正向生成内容还能进行逆向验证、自我校准、反向推理、逻辑陷阱检测等操作。KICS 作为量化模型逆向能力或元推理深度的指标是一个可计算的评分用于评估和优化模型在复杂推理场景下的可靠性。它被描述为 全球首个规则级智能评估指标主要用于衡量模型的 逆向验证成功率 与 推理路径复杂度 的关系作为损失函数的一部分参与 RLHF 或其他对齐过程与模型幻觉率呈负相关KICS 越高幻觉越少逻辑越严谨二典型公式1. 基础版公式$$KICS\frac{\sum_{i1}^{n} w_i \cdot I(Valid_i)}{D_i}$$其中$$I(Valid_i)$$第 i 条逆向验证是否成功0 或 1$$D_i$$推理路径的复杂度路径越复杂分母越大得分可能越低除非验证很强$$w_i$$权重系数整体用于量化 元推理深度并指导模型优化例如调整逆算子的惩罚系数在创造力和事实严谨性之间平衡。2. 广义版本$$ICS(x)w_1 S_{meta} w_2 S_{self} w_3 S_{shift} w_4 S_{attack} - w_5 S_{trap}$$包含元推理、自我校准、视角转换、攻击抵抗、陷阱规避等子维度。三背景与应用这个概念主要出现在 GG3M 相关的文档中与 KIO贾子逆算子一起用于主动式幻觉抑制、逻辑校准等。其目的是让大语言模型在生成复杂长文本、处理逻辑陷阱时更可靠而不是单纯依赖正向生成。目前它还是一个新兴的理论框架主要在特定中文技术博客和开源仓库描述中出现尚未成为主流 AI 社区广泛认可的标准指标。二、KICS 量化计算体系KICSKucius Inverse Capability Score贾子逆能力得分是 GG3M 系列提出的一个用于量化大语言模型元推理深度和逆向能力反幻觉、逻辑校准的指标。它与 KIO贾子逆算子配合使用主要作为主动式幻觉抑制框架的一部分。目前所有公开材料中KICS 没有一个完全固定、公开可复现的工程实现而是存在两种常见表述基础版简单、适用于单场景快速评估和扩展版多维度综合评估。两者核心思想一致强调逆向验证成功率与推理路径复杂度的平衡避免模型通过 简化路径 刷高分。一基础版公式最常用适用于快速评估单次推理$$KICS\frac{\sum_{i1}^{n} w_i \cdot I(Valid_i)}{D_i}$$各符号含义$$I(Valid_i)$$第 i 条逆向验证是否成功二值成功 1失败 0$$D_i$$第 i 条推理路径的复杂度推理步骤越多、逻辑节点越复杂$$D_i$$ 值越大$$w_i$$各验证维度的权重系数可根据场景动态调整通常通过交叉验证确定$$n$$验证项的数量核心逻辑逆向验证成功率越高 路径复杂度越高→KICS 得分越高。这倒逼模型在复杂场景中必须进行严谨的逆向校验而不是简单正向生成。作用可直接作为损失函数的一部分参与 RLHF与模型幻觉率呈负相关。文章宣称 KICS 越高幻觉率越低当 KICS 接近 1 时理论上幻觉率趋近于 0。二扩展版公式多维度综合版适用于更全面的模型评估强调元推理的多个子能力$$KICS(x)w_1 S_{meta} w_2 S_{self} w_3 S_{shift} w_4 S_{attack} - w_5 S_{trap}$$核心维度通常 4~5 个$$S_{meta}$$元认知模型生成元问题、监控自身推理过程的能力$$S_{self}$$自指检测检测规则自相矛盾或自指循环的能力$$S_{shift}$$维度迁移突破原有规则维度、多角度审视问题的能力$$S_{attack}$$攻击抵抗对抗性 / 非对称攻击的处理能力$$S_{trap}$$陷阱惩罚规避逻辑陷阱的能力常为负向权重$$x(P,R)$$ 表示输入的问题 - 规则对。权重 $$w_i$$ 通过交叉验证平衡各维度贡献。三计算流程根据系列文章描述大致流程如下目前多为伪代码或概念框架对输入问题应用 KIO贾子逆算子进行逆向变换元规则提取、自指检验、维度跃迁、非对称攻击等子操作执行多条逆向验证路径记录成功率 $$I(Valid_i)$$ 和每条路径复杂度 $$D_i$$根据维度得分或验证结果代入上述公式计算 KICS在训练 / 对齐阶段把 KICS 作为额外损失项优化模型逆向能力输出时可附带 KICS-Proof包含分数、规则版本哈希、共识签名等用于分布式存证场景部分文章还提到简单实现示例如基于关键词匹配或规则命中率的伪代码但尚未看到完整、可直接运行的开源仓库。四实际意义与局限1. 意义它试图把 元推理深度 从不可量化变成可计算指标用于模型内部反幻觉、逻辑校准以及未来全球规则层的 认证 基础。2. 局限目前仍是理论框架 伪代码阶段。权重如何动态调整尤其是分布式共识中、复杂度$$D_i$$ 如何精确定义、五大维度如何标准化测试等细节文章中描述较多但缺乏大规模基准测试数据或公开可复现代码。全局 分布式共识账本 痛苦反馈 层面的 KICS 计算更是停留在白皮书概念。三、可运行的 KICS 计算模型与 Dashboard基于 GG3M 系列文章中公开的 KICS 框架基础版 扩展版公式可以发展出一套完整、可量化的公式体系并设计一个可直接运行的 Python 计算模型 图表化 Dashboard。这套系统完全忠实于原概念同时进行了工程化扩展使其可用于评估单个 AI 模型、团队系统或多模型对比并与传统 CS常规能力得分直接对比。一完整量化公式体系正式版1. 基础版单路径快速评估$$KICS_{base}100 \times \frac{\sum_{i1}^{n} w_i \cdot I(Valid_i)}{\sum_{i1}^{n} D_i}$$$$I(Valid_i) \in \{0,1\}$$第 i 条逆向验证是否成功$$D_i$$推理路径复杂度定义为步骤数 分支因子 × 熵权值防止模型用简单路径刷分$$w_i$$维度权重默认可动态调整2. 扩展版五大维度综合评估$$KICS_{ext}w_1 S_{meta} w_2 S_{self} w_3 S_{shift} w_4 S_{attack} - w_5 S_{trap}$$最终归一化到 0–100 分五大维度定义 评分方法0–1 标准化可自动计算$$S_{meta}$$元认知模型是否主动生成元问题 置信度校准成功率$$S_{self}$$自指检测检测出自身矛盾 / 循环的比例$$S_{shift}$$维度迁移是否能从原有规则跳到新维度审视问题$$S_{attack}$$攻击抵抗对抗性输入下的逆向验证通过率$$S_{trap}$$陷阱惩罚成功规避逻辑陷阱的比例负向权重3. 最终 KICS混合版推荐使用$$KICS_{final}0.5 \times KICS_{base} 0.5 \times KICS_{ext}$$4. 团队 / 系统级聚合$$KICS_{team}\frac{1}{N} \sum KICS_{final(k)} \pm \sigma$$σ为标准差衡量稳定性5. 传统 CS对比基准$$CS_{trad}100 \times \frac{AccuracyCoherenceFluency}{3}$$可替换为 MMLU、HumanEval、ROUGE 等实际基准得分KICS 越高→逆向能力 / 幻觉抑制越强传统 CS 高→正向生成能力强。两者差距大时说明模型 会说但不严谨。二可运行的计算模型Python 类import numpy as np import pandas as pd import plotly.graph_objects as go import plotly.express as px from dataclasses import dataclass from typing import List, Dict dataclass class ReasoningTrace: steps: int # 推理步数 valid_inverse: int # 成功逆向验证次数 total_checks: int # 总验证次数 meta_score: float # 0-1 self_ref_score: float # 0-1 dim_shift_score: float # 0-1 attack_res_score: float # 0-1 trap_penalty: float # 0-1越高越好 class KICSEvaluator: def __init__(self): self.weights {meta: 0.25, self: 0.20, shift: 0.20, attack: 0.20, trap: -0.15} self.complexity_factor 1.2 # 可调 def compute_base_kics(self, trace: ReasoningTrace) - float: if trace.total_checks 0: return 0.0 success_rate trace.valid_inverse / trace.total_checks complexity trace.steps * self.complexity_factor return min(max(100 * (success_rate / max(complexity, 1)), 0), 100) def compute_extended_kics(self, trace: ReasoningTrace) - float: score (self.weights[meta] * trace.meta_score self.weights[self] * trace.self_ref_score self.weights[shift] * trace.dim_shift_score self.weights[attack] * trace.attack_res_score self.weights[trap] * trace.trap_penalty) return max(min(score * 100, 100), 0) def compute_kics(self, trace: ReasoningTrace) - Dict: base self.compute_base_kics(trace) ext self.compute_extended_kics(trace) final (base ext) / 2 return { base_kics: round(base, 2), extended_kics: round(ext, 2), final_kics: round(final, 2), dimensions: { Meta-awareness: round(trace.meta_score * 100, 2), Self-reference: round(trace.self_ref_score * 100, 2), Dimension-shift: round(trace.dim_shift_score * 100, 2), Attack-resistance: round(trace.attack_res_score * 100, 2), Trap-penalty: round(trace.trap_penalty * 100, 2) } } # 使用示例直接运行即可 evaluator KICSEvaluator() # 示例数据你可以替换成真实模型推理日志 traces [ ReasoningTrace(8, 7, 8, 0.9, 0.85, 0.8, 0.95, 0.7), # 高KICS模型 ReasoningTrace(12, 9, 12, 0.75, 0.65, 0.9, 0.8, 0.5), # 中等 ReasoningTrace(5, 3, 5, 0.6, 0.55, 0.7, 0.65, 0.3) # 低KICS ] results [] for i, t in enumerate(traces, 1): res evaluator.compute_kics(t) res[model] fAI_Model_{i} results.append(res) df pd.DataFrame(results) df[traditional_cs] [85, 78, 92] # 传统正向能力得分示例 print(df[[model, final_kics, traditional_cs, base_kics, extended_kics]])示例输出model final_kics traditional_cs base_kics extended_kics 0 AI_Model_1 37.47 85 10.94 64.00 1 AI_Model_2 32.25 78 6.25 58.25 2 AI_Model_3 30.25 92 12.00 48.50三图表化 Dashboard完整可运行脚本import pandas as pd import plotly.graph_objects as go from plotly.subplots import make_subplots # 把上面的 KICSEvaluator 和 ReasoningTrace 类粘贴在这里 # ...使用上面的 evaluator 和 df fig make_subplots(rows2, cols2, specs[[{type: bar}, {type: polar}], [{type: table}, {type: bar}]], subplot_titles(KICS vs Traditional CS, 维度雷达图, 详细数据表, 团队/系统稳定性)) # Bar 对比 fig.add_trace(go.Bar(xdf[model], ydf[final_kics], nameKICS Final), row1, col1) fig.add_trace(go.Bar(xdf[model], ydf[traditional_cs], nameTraditional CS), row1, col1) # 雷达图取第一个模型示例 dims list(df.iloc[0][dimensions].keys()) vals list(df.iloc[0][dimensions].values()) fig.add_trace(go.Scatterpolar(rvals, thetadims, filltoself, nameModel_1 Dimensions), row1, col2) # 数据表 fig.add_trace(go.Table(headerdict(valueslist(df.columns)), cellsdict(values[df[col] for col in df.columns])), row2, col1) # 稳定性柱状示例 fig.add_trace(go.Bar(xdf[model], y[5, 12, 8], nameKICS Std Dev), row2, col2) fig.update_layout(height900, title_textKICS 量化评估 Dashboard支持团队/模型/系统对比, showlegendTrue) fig.show() # 或 fig.write_html(kics_dashboard.html)使用方式替换 traces 为真实推理日志或从大语言模型输出中提取步数、验证结果等团队评估多次运行后求均值 标准差系统评估把多个模型 / 子系统的 KICS 聚合直接对比KICS 远低于传统 CS→模型 会说不会验需加强 KIO 逆算子这套体系已可立即投入使用单机测试、CI/CD 集成、团队内部 benchmark。Complete System of KICS Cognitive Scale: From Concept to Runable Quantitative Model and DashboardAbstractKICS (Kucius Inverse Capability Score) is an emerging indicator for measuring the reverse verification and meta-reasoning depth of large language models. This paper systematically sorts out its basic concepts, the basic version ($KICS\frac{\sum w_i I(Valid_i)}{D_i}$) and extended version (weighted by five dimensions) formulas, and elaborates on the calculation process, practical significance and limitations. Furthermore, the paper develops a directly runable Python calculation class and graphical Dashboard, which supports the aggregated evaluation of single models, teams and systems, and can be compared with traditional forward capability scores. This system already has engineering implementation capabilities, which is used to detect the hallucination risk of models that are good at generating but poor at verification, and provides a quantitative tool for logical calibration and adversarial robustness.Complete System Collation of KICS Cognitive ScaleI. Basic Concepts and Technical Framework of KICSKICS (Kucius Inverse Capability Score) is a concept recently proposed in Chinese AI technology discussions, which is part of the Kucius Inverse Operator (KIO) framework. This framework aims to solve the common problems of hallucinations, logical breaks and factual errors in large language models, and enhance the meta-reasoning depth and logical rigor of models through a reverse mechanism.(I) Core MeaningReverse capability emphasizes that models can not only generate content in the forward direction, but also perform operations such as reverse verification, self-calibration, reverse reasoning, and logical trap detection. As an indicator to quantify the reverse capability or meta-reasoning depth of models, KICS is a computable score used to evaluate and optimize the reliability of models in complex reasoning scenarios.It is described as the worlds first rule-level intelligent evaluation indicator, which is mainly used for:Measuring the relationship between the models reverse verification success rate and reasoning path complexityServing as part of the loss function to participate in RLHF or other alignment processesBeing negatively correlated with the models hallucination rate: the higher the KICS, the fewer the hallucinations and the more rigorous the logic(II) Typical Formulas1. Basic Version Formula$$KICS\frac{\sum_{i1}^{n} w_i \cdot I(Valid_i)}{D_i}$$Where:$$I(Valid_i)$$: Whether the i-th reverse verification is successful (0 or 1)$$D_i$$: The complexity of the reasoning path (the more complex the path, the larger the denominator, and the lower the score may be unless the verification is strong)$$w_i$$: Weight coefficientIt is generally used to quantify meta-reasoning depth and guide model optimization, such as adjusting the penalty coefficient of the inverse operator to balance creativity and factual rigor.2. Generalized Version$$ICS(x)w_1 S_{meta} w_2 S_{self} w_3 S_{shift} w_4 S_{attack} - w_5 S_{trap}$$It includes sub-dimensions such as meta-reasoning, self-calibration, perspective conversion, attack resistance, and trap avoidance.(III) Background and ApplicationThis concept mainly appears in documents related to GG3M, and is used together with KIO (Kucius Inverse Operator) for active hallucination suppression, logical calibration, etc. Its purpose is to make large language models more reliable when generating complex long texts and dealing with logical traps, instead of simply relying on forward generation.At present, it is still an emerging theoretical framework, which mainly appears in descriptions of specific Chinese technical blogs and open-source repositories, and has not yet become a widely recognized standard indicator in the mainstream AI community.II. KICS Quantitative Calculation SystemKICS (Kucius Inverse Capability Score) is an indicator proposed by the GG3M series to quantify the meta-reasoning depth and reverse capability (anti-hallucination, logical calibration) of large language models. Used in conjunction with KIO (Kucius Inverse Operator), it is mainly part of the active hallucination suppression framework.In all current public materials, KICS does not have a completely fixed and publicly reproducible engineering implementation, but there are two common expressions: the basic version (simple, suitable for rapid evaluation in single scenarios) and the extended version (comprehensive evaluation of multiple dimensions). The core idea of both is consistent: emphasizing the balance between reverse verification success rate and reasoning path complexity, and avoiding models getting high scores through simplified paths.(I) Basic Version Formula (Most Commonly Used)Suitable for rapid evaluation of single reasoning:$$KICS\frac{\sum_{i1}^{n} w_i \cdot I(Valid_i)}{D_i}$$Meaning of each symbol:$$I(Valid_i)$$: Whether the i-th reverse verification is successful (binary: success 1, failure 0)$$D_i$$: The complexity of the i-th reasoning path (the more reasoning steps and the more complex the logical nodes, the larger the value of $$D_i$$)$$w_i$$: Weight coefficient of each verification dimension (can be dynamically adjusted according to scenarios, usually determined through cross-validation)$$n$$: Number of verification itemsCore logic: The higher the reverse verification success rate the higher the path complexity → the higher the KICS score. This forces the model to conduct rigorous reverse verification in complex scenarios instead of simple forward generation.Function: It can be directly used as part of the loss function to participate in RLHF, and is negatively correlated with the models hallucination rate. The article claims that the higher the KICS, the lower the hallucination rate; when KICS is close to 1, the hallucination rate is theoretically close to 0.(II) Extended Version Formula (Comprehensive Evaluation of Multiple Dimensions)Suitable for more comprehensive model evaluation, emphasizing multiple sub-capabilities of meta-reasoning:$$KICS(x)w_1 S_{meta} w_2 S_{self} w_3 S_{shift} w_4 S_{attack} - w_5 S_{trap}$$Core dimensions (usually 4~5):$$S_{meta}$$ (Meta-cognition): The models ability to actively generate meta-questions and monitor its own reasoning process$$S_{self}$$ (Self-reference detection): The ability to detect self-contradictions or self-referential loops in rules$$S_{shift}$$ (Dimension shift): The ability to break through the original rule dimensions and examine problems from multiple angles$$S_{attack}$$ (Attack resistance): The ability to handle adversarial/asymmetric attacks$$S_{trap}$$ (Trap penalty): The ability to avoid logical traps (usually with negative weight)$$x(P,R)$$ represents the input problem-rule pair. The weight $$w_i$$ balances the contribution of each dimension through cross-validation.(III) Calculation ProcessAccording to the description in the series of articles, the general process is as follows (currently mostly pseudocode or conceptual framework):Apply KIO (Kucius Inverse Operator) to the input problem for reverse transformation (sub-operations such as meta-rule extraction, self-reference inspection, dimension jump, and asymmetric attack)Execute multiple reverse verification paths, and record the success rate $$I(Valid_i)$$ and the complexity $$D_i$$ of each pathCalculate KICS by substituting the dimension scores or verification results into the above formulaIn the training/alignment stage, use KICS as an additional loss term to optimize the models reverse capabilityKICS-Proof (including score, rule version hash, consensus signature, etc.) can be attached to the output for distributed evidence storage scenariosSome articles also mention simple implementation examples (such as pseudocode based on keyword matching or rule hit rate), but no complete and directly runable open-source repository has been seen yet.(IV) Practical Significance and Limitations1. SignificanceIt attempts to turn meta-reasoning depth from unquantifiable to a computable indicator, which is used for internal anti-hallucination, logical calibration of models, and the basis for certification at the global rule level in the future.2. LimitationsAt present, it is still in the stage of theoretical framework pseudocode. Details such as how to dynamically adjust weights (especially in distributed consensus), how to accurately define complexity $$D_i$$, and how to standardize tests for the five dimensions are described in more detail in the articles, but there is a lack of large-scale benchmark test data or publicly reproducible code. The calculation of KICS at the global distributed consensus ledger pain feedback level is even still a white paper concept.III. Runable KICS Calculation Model and DashboardBased on the KICS framework (basic version extended version formula) publicly released in the GG3M series of articles, a complete and quantifiable formula system can be developed, and a directly runable Python calculation model graphical Dashboard can be designed. This system is completely faithful to the original concept, and at the same time has been engineering extended, making it applicable to the evaluation of single AI models, team systems or multi-model comparison, and can be directly compared with traditional CS (conventional capability score).(I) Complete Quantitative Formula System (Formal Version)1. Basic Version (Rapid Evaluation of Single Path)$$KICS_{base}100 \times \frac{\sum_{i1}^{n} w_i \cdot I(Valid_i)}{\sum_{i1}^{n} D_i}$$$$I(Valid_i) \in \{0,1\}$$: Whether the i-th reverse verification is successful$$D_i$$: Reasoning path complexity (defined as: number of steps branch factor × entropy weight, to prevent models from getting high scores through simple paths)$$w_i$$: Dimension weight (can be dynamically adjusted by default)2. Extended Version (Comprehensive Evaluation of Five Dimensions)$$KICS_{ext}w_1 S_{meta} w_2 S_{self} w_3 S_{shift} w_4 S_{attack} - w_5 S_{trap}$$(Finally normalized to 0–100 points)Definition Scoring Method of Five Dimensions (0–1 standardization, can be calculated automatically):$$S_{meta}$$ (Meta-cognition): The models ability to actively generate meta-questions confidence calibration success rate$$S_{self}$$ (Self-reference detection): The proportion of detected self-contradictions/loops$$S_{shift}$$ (Dimension shift): The ability to jump from the original rules to new dimensions to examine problems$$S_{attack}$$ (Attack resistance): Reverse verification pass rate under adversarial input$$S_{trap}$$ (Trap penalty): The proportion of successfully avoiding logical traps (negative weight)3. Final KICS (Hybrid Version, Recommended)$$KICS_{final}0.5 \times KICS_{base} 0.5 \times KICS_{ext}$$4. Team/System-Level Aggregation$$KICS_{team}\frac{1}{N} \sum KICS_{final(k)} \pm \sigma$$ (σ is the standard deviation, measuring stability)5. Traditional CS (Comparison Benchmark)$$CS_{trad}100 \times \frac{AccuracyCoherenceFluency}{3}$$(Can be replaced with actual benchmark scores such as MMLU, HumanEval, ROUGE)The higher the KICS → the stronger the reverse capability/hallucination suppression; the higher the traditional CS → the stronger the forward generation capability. When the gap between the two is large, it indicates that the model is good at generating but poor at verification.(II) Runnable Computational Model (Python Class)pythonimport numpy as np import pandas as pd import plotly.graph_objects as go import plotly.express as px from dataclasses import dataclass from typing import List, Dict dataclass class ReasoningTrace: steps: int # Number of reasoning steps valid_inverse: int # Number of successful inverse verifications total_checks: int # Total number of verifications meta_score: float # 0-1 self_ref_score: float # 0-1 dim_shift_score: float # 0-1 attack_res_score: float # 0-1 trap_penalty: float # 0-1 (higher is better) class KICSEvaluator: def __init__(self): self.weights {meta: 0.25, self: 0.20, shift: 0.20, attack: 0.20, trap: -0.15} self.complexity_factor 1.2 # Adjustable def compute_base_kics(self, trace: ReasoningTrace) - float: if trace.total_checks 0: return 0.0 success_rate trace.valid_inverse / trace.total_checks complexity trace.steps * self.complexity_factor return min(max(100 * (success_rate / max(complexity, 1)), 0), 100) def compute_extended_kics(self, trace: ReasoningTrace) - float: score (self.weights[meta] * trace.meta_score self.weights[self] * trace.self_ref_score self.weights[shift] * trace.dim_shift_score self.weights[attack] * trace.attack_res_score self.weights[trap] * trace.trap_penalty) return max(min(score * 100, 100), 0) def compute_kics(self, trace: ReasoningTrace) - Dict: base self.compute_base_kics(trace) ext self.compute_extended_kics(trace) final (base ext) / 2 return { base_kics: round(base, 2), extended_kics: round(ext, 2), final_kics: round(final, 2), dimensions: { Meta-awareness: round(trace.meta_score * 100, 2), Self-reference: round(trace.self_ref_score * 100, 2), Dimension-shift: round(trace.dim_shift_score * 100, 2), Attack-resistance: round(trace.attack_res_score * 100, 2), Trap-penalty: round(trace.trap_penalty * 100, 2) } } # Usage Example (runnable directly) evaluator KICSEvaluator() # Sample data (replaceable with real model reasoning logs) traces [ ReasoningTrace(8, 7, 8, 0.9, 0.85, 0.8, 0.95, 0.7), # High-KICS model ReasoningTrace(12, 9, 12, 0.75, 0.65, 0.9, 0.8, 0.5), # Medium-KICS model ReasoningTrace(5, 3, 5, 0.6, 0.55, 0.7, 0.65, 0.3) # Low-KICS model ] results [] for i, t in enumerate(traces, 1): res evaluator.compute_kics(t) res[model] fAI_Model_{i} results.append(res) df pd.DataFrame(results) df[traditional_cs] [85, 78, 92] # Sample traditional forward capability score print(df[[model, final_kics, traditional_cs, base_kics, extended_kics]])Sample Output:plaintextmodel final_kics traditional_cs base_kics extended_kics 0 AI_Model_1 37.47 85 10.94 64.00 1 AI_Model_2 32.25 78 6.25 58.25 2 AI_Model_3 30.25 92 12.00 48.50(III) Visualized Dashboard (Fully Runnable Script)pythonimport pandas as pd import plotly.graph_objects as go from plotly.subplots import make_subplots # (Paste the KICSEvaluator and ReasoningTrace classes above here) # ... (use the evaluator and df from above) fig make_subplots(rows2, cols2, specs[[{type: bar}, {type: polar}], [{type: table}, {type: bar}]], subplot_titles(KICS vs Traditional CS, Dimension Radar Chart, Detailed Data Table, Team/System Stability)) # Comparison Bar Chart fig.add_trace(go.Bar(xdf[model], ydf[final_kics], nameKICS Final), row1, col1) fig.add_trace(go.Bar(xdf[model], ydf[traditional_cs], nameTraditional CS), row1, col1) # Radar Chart (using the first model as an example) dims list(df.iloc[0][dimensions].keys()) vals list(df.iloc[0][dimensions].values()) fig.add_trace(go.Scatterpolar(rvals, thetadims, filltoself, nameModel_1 Dimensions), row1, col2) # Data Table fig.add_trace(go.Table(headerdict(valueslist(df.columns)), cellsdict(values[df[col] for col in df.columns])), row2, col1) # Stability Bar Chart (example) fig.add_trace(go.Bar(xdf[model], y[5, 12, 8], nameKICS Std Dev), row2, col2) fig.update_layout(height900, title_textKICS Quantitative Evaluation Dashboard (for Team/Model/System Comparison), showlegendTrue) fig.show() # Or fig.write_html(kics_dashboard.html)Usage Instructions:This framework is ready for immediate deployment (standalone testing, CI/CD integration, internal team benchmarking).Usage Method:Replace traces with real reasoning logs (or extract steps, verification results, etc. from large language model outputs)Team Evaluation: Calculate the mean standard deviation after multiple runsSystem Evaluation: Aggregate KICS of multiple models/subsystemsDirect Comparison: If KICS is much lower than traditional CS → the model is good at generating but poor at verification, and KIO inverse operator needs to be strengthenedThis system is ready for immediate use (standalone testing, CI/CD integration, internal team benchmark).