KINA 解决了 LLM 知识评估中学科代表性不足和排名不稳定的痛点，做模型评测或研究 LLM 知识边界的团队可以直接用这个基准来更可靠地对比模型，建议点开看看具体的设计和排名细节。
针对现有LLM知识基准存在的三个问题（学科代表性不足、标注激励不当、排名不稳定），研究者提出了KINA基准，包含899道题目，覆盖261个细粒度学科。该基准通过贪心近似算法确保学科代表性，并设计了锦标赛式奖励机制以提升标注质量。在13个实验室的42个模型评估中，Gemini-3.1-Pro-Preview以53.17%的准确率领先，Claude-Opus-4.6和GPT-5.4紧随其后，整体排名呈现分层结构，远未达到饱和。工具增强平均提升5.17个百分点，但模型间差异显著。该基准还提供了自举排名稳定性统计，避免对相邻排名的过度解读。
Knowledge Index of Noah's Ark
Knowledge benchmarks for LLMs face three issues: scaling-driven designs that do not operationalize disciplinary representativeness; flat-payment annotation that permits lazy consensus; and unaudited ranking instability under bounded test budgets. We introduce KINA, an 899-item benchmark across 261 fine-grained disciplines, with two formal results. First, we cast representativeness as a coverage-style objective over expert-elicited anchors and operationalize disciplinary representativeness through a proxy, yielding a (1-1/e) greedy approximation (Proposition 1); the guarantee applies to the proxy, not to population representativeness. Second, we prove a bonus-on-bar tournament weakly FOSD-dominates flat payment in released-review quality, with incentive-compatibility threshold B > Delta C / Delta p_min (Theorem 1). Evaluating 42 models from 13 labs, the top model, Gemini-3.1-Pro-Preview, reaches 53.17%, followed by Claude-Opus-4.6 at 49.92% and GPT-5.4 at 48.55%, leaving substantial headroom below saturation. The full leaderboard shows a tiered structure rather than a smooth total order: a small frontier tier lies above 48%, a dense strong-model tier spans roughly 38-45%, and low-performing models remain only modestly above the 10% chance baseline. Tool augmentation adds up to 5.17 points across the five tool-use evaluations, with gains varying substantially across models. We report bootstrap ranking-stability statistics to make bounded-budget variance explicit and to discourage over-interpretation of adjacent ranks.