500 道原创场景练习题，并非公司面试真题。参考资料解释方法；数值答案来自可复现的本地计算，不代表前沿模型实测基准。
每个场景 10 道引导题，题型在不同实验中有意复用。不是 500 道独立采集的面试真题。
技术场景说明、参数名称与参考答案目前为英文。
6 张面经学习卡：3 个 OpenAI 具体题目主题、3 个 Anthropic 考核形式。候选人自述未经公司确认。下列练习、测试案例与解题思路为 Token Lab 原创，不是面试原文或官方答案。
来源核对：2026-10-25。OpenAI 来源：Aced 托管的匿名面经，确切发布日期未提供（页面仅显示相对时间）。Anthropic 来源：Andrey Goncharov，2025-02-12，2025 年 9 月更新。个体经历不代表公司统一流程或统计频率。
一位后端候选人报告，编码轮涉及 GPU 额度管理。
阅读面经来源 ↗原创变体：实现 grant(id, amount, start, end)、consume(amount, time)、balance(time)。有效期为 [start,end)，优先消耗最早到期的额度；余额不足时整笔拒绝；事件按时间顺序输入。
验收案例： A=10，有效期 [0,10)；B=5，有效期 [3,6)。t=4 消耗 7：B=0、A=8。t=6 余额为 8；t=10 为 0。测试重复 ID 和被拒绝的扣款。
先用排序额度列表实现基准版本。先明确区间语义，再决定是否用堆。扣款失败不得修改任何额度。进阶：乱序事件需明确重放或版本语义。
分别按 0–2 分自评：假设清晰、推理正确、边界覆盖、权衡讨论。总分 /8 仅为学习量表，并非公司录用评分。
同一候选人描述，迭代器题逐步扩展到二维、三维和异步遍历。
阅读面经来源 ↗原创变体：惰性遍历 [[],[1,2],[],[3]]，增加可序列化检查点；输出 2 后保存并恢复。再扩展到支持取消的异步数据源。
验收案例： 预期输出 1、2、3；恢复后只输出 3。覆盖空输入、重复耗尽、过期检查点、等待读取时取消。
维护显式游标，跨过空行但不预先展开全部输入。检查点绑定输入版本。解释异步读取在成功前还是成功后推进游标。
分别按 0–2 分自评：假设清晰、推理正确、边界覆盖、权衡讨论。总分 /8 仅为学习量表，并非公司录用评分。
候选人报告，系统设计轮涉及任务调度器，并讨论扩展与故障。
阅读面经来源 ↗原创变体：为 10,000 个排队实验任务和 100 个 worker 设计调度器。加入租户配额、重试、取消和实验产物。先定义投递保证，再画组件。
验收案例： 注入故障：worker 写完输出但尚未确认时崩溃。说明重复尝试如何避免重复发布产物。度量排队等待 p95 和各租户饥饿情况。
使用持久化任务状态、租约与 fencing token、幂等产物提交和有限重试。区分公平调度与执行。恰好一次效果需要输出存储配合。
分别按 0–2 分自评：假设清晰、推理正确、边界覆盖、权衡讨论。总分 /8 仅为学习量表，并非公司录用评分。
一位 Research Fellowship 申请者描述了四阶段、测试通过后解锁的编码考核；未披露具体 API 题面。
阅读面经来源 ↗原创练习，并非未公开的原题：分四阶段实现键值存储：get/set、前缀扫描、TTL、快照恢复。每阶段保留前面阶段的正确行为。
验收案例： 定义 t >= expiresAt 时过期。测试缺失键、覆盖、排序扫描和延迟恢复快照。明确恢复保留绝对过期时刻还是剩余 TTL。
将时钟与存储分离，用可控时钟做确定性测试。明确恢复语义；改变内部表示时保留回归测试。
分别按 0–2 分自评：假设清晰、推理正确、边界覆盖、权衡讨论。总分 /8 仅为学习量表，并非公司录用评分。
申请者报告了简短研究讨论，包含两个关于黑箱 LLM 行为的开放问题；未公开具体问题。
阅读面经来源 ↗原创练习：加入无关上下文后，模型答案发生变化。提出三个竞争性解释，用 200 次查询设计可区分这些解释的实验。
验收案例： 为每个假设写可证伪预测、随机顺序、重复样本、留出集和停止规则。说明什么结果会让你改变判断。
区分位置效应、语义干扰和采样噪声。改变一个因素时控制其他因素。报告效应量和不确定性；阴性结果需结合检验功效解释。
分别按 0–2 分自评：假设清晰、推理正确、边界覆盖、权衡讨论。总分 /8 仅为学习量表，并非公司录用评分。
申请者称，在作业轮被取消后仍收到用于 API 黑箱研究的 notebook；本人并未完成该轮。
阅读面经来源 ↗原创练习：检验评审模型是否偏爱冗长答案。构造 30 对事实内容相同的答案，随机呈现顺序并预注册分析。先用合成标签验证流程。
验收案例： 分离正确性与冗长度。加入交换顺序试验、分歧复核、置信区间与费用上限。保留提示词、模型版本、原始输出与排除记录。
估计配对偏好差异，按题目聚类计算不确定性。区分模型偏好与人类质量判断。合成试运行只验证流程，不能作为真实模型证据。
分别按 0–2 分自评：假设清晰、推理正确、边界覆盖、权衡讨论。总分 /8 仅为学习量表，并非公司录用评分。
以下官方页面提供流程与工具使用要求，并非公开真题库。具体考核规则以招聘方说明为准。
Future positions must receive zero probability.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →softmax(scale × QKᵀ / √2 + mask)V
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →{"scale":1,"causal":1}
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →{"Mean entropy (bits)":1.0510152779349418,"Last output":2.980121288333647}
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Keep the supplied fixture and other settings fixed; change scale. Inspect per-row normalization, entropy and the weighted value output.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →A high attention weight does not establish causal feature importance.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →No. One synthetic head, four positions,
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Inspect per-row normalization, entropy and the weighted value output. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Open attention-1, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →A high attention weight does not establish causal feature importance. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Removing the mask exposes future keys.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →softmax(scale × QKᵀ / √2 + mask)V
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →{"scale":1,"causal":0}
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →{"Mean entropy (bits)":1.8471329825256673,"Last output":2.980121288333647}
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Keep the supplied fixture and other settings fixed; change scale. Inspect per-row normalization, entropy and the weighted value output.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →A high attention weight does not establish causal feature importance.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →No. One synthetic head, four positions,
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Inspect per-row normalization, entropy and the weighted value output. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Open attention-2, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →A high attention weight does not establish causal feature importance. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Larger logits concentrate probability on the largest visible score.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →softmax(scale × QKᵀ / √2 + mask)V
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →{"scale":5,"causal":1}
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →{"Mean entropy (bits)":0.19672661245634565,"Last output":1.1271721043873806}
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Keep the supplied fixture and other settings fixed; change scale. Inspect per-row normalization, entropy and the weighted value output.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →A high attention weight does not establish causal feature importance.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →No. One synthetic head, four positions,
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Inspect per-row normalization, entropy and the weighted value output. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Open attention-3, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →A high attention weight does not establish causal feature importance. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Equal visible logits yield uniform weights.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →softmax(scale × QKᵀ / √2 + mask)V
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →{"scale":0,"causal":1}
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →{"Mean entropy (bits)":1.146240625180289,"Last output":4}
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Keep the supplied fixture and other settings fixed; change scale. Inspect per-row normalization, entropy and the weighted value output.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →A high attention weight does not establish causal feature importance.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →No. One synthetic head, four positions,
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Inspect per-row normalization, entropy and the weighted value output. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Open attention-4, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →A high attention weight does not establish causal feature importance. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →A smaller scale moves weights toward uniform without changing visibility.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →softmax(scale × QKᵀ / √2 + mask)V
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →{"scale":0.3,"causal":1}
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →{"Mean entropy (bits)":1.1377371118107673,"Last output":3.700414589154465}
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Keep the supplied fixture and other settings fixed; change scale. Inspect per-row normalization, entropy and the weighted value output.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →A high attention weight does not establish causal feature importance.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →No. One synthetic head, four positions,
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Inspect per-row normalization, entropy and the weighted value output. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →Open attention-5, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →A high attention weight does not establish causal feature importance. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Attention Is All You Need · §3.2 ↗
打开实验 →A large document norm should not dominate cosine ranking.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →{"x":1,"y":0.2,"metric":0}
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →{"Top score":0.9999999999999998,"Query norm":1.019803902718557}
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Keep the supplied fixture and other settings fixed; change x. Compare ranking order, vector norms and scores on identical documents.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Geometry alone cannot establish semantic relevance; these vectors are hand-authored.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →No. Five manually chosen 2D vectors, not embeddings generated by a language model. Zero-vector cosine is defined as 0 in this lab. Ties preserve fixture order.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Compare ranking order, vector norms and scores on identical documents. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Open embedding-1, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Geometry alone cannot establish semantic relevance; these vectors are hand-authored. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →The long vector may outrank a better-aligned short vector.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →{"x":1,"y":0.2,"metric":1}
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →{"Top score":5.2,"Query norm":1.019803902718557}
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Keep the supplied fixture and other settings fixed; change x. Compare ranking order, vector norms and scores on identical documents.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Geometry alone cannot establish semantic relevance; these vectors are hand-authored.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →No. Five manually chosen 2D vectors, not embeddings generated by a language model. Zero-vector cosine is defined as 0 in this lab. Ties preserve fixture order.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Compare ranking order, vector norms and scores on identical documents. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Open embedding-2, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Geometry alone cannot establish semantic relevance; these vectors are hand-authored. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Rotating the query changes which document aligns best.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →{"x":0,"y":1,"metric":0}
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →{"Top score":1,"Query norm":1}
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Keep the supplied fixture and other settings fixed; change x. Compare ranking order, vector norms and scores on identical documents.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Geometry alone cannot establish semantic relevance; these vectors are hand-authored.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →No. Five manually chosen 2D vectors, not embeddings generated by a language model. Zero-vector cosine is defined as 0 in this lab. Ties preserve fixture order.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Compare ranking order, vector norms and scores on identical documents. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Open embedding-3, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Geometry alone cannot establish semantic relevance; these vectors are hand-authored. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Opposite vectors receive negative cosine scores.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →{"x":-1,"y":0,"metric":0}
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →{"Top score":1,"Query norm":1}
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Keep the supplied fixture and other settings fixed; change x. Compare ranking order, vector norms and scores on identical documents.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Geometry alone cannot establish semantic relevance; these vectors are hand-authored.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →No. Five manually chosen 2D vectors, not embeddings generated by a language model. Zero-vector cosine is defined as 0 in this lab. Ties preserve fixture order.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Compare ranking order, vector norms and scores on identical documents. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Open embedding-4, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Geometry alone cannot establish semantic relevance; these vectors are hand-authored. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →The lab returns zero scores for an undefined cosine denominator.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →{"x":0,"y":0,"metric":0}
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →{"Top score":0,"Query norm":0}
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Keep the supplied fixture and other settings fixed; change x. Compare ranking order, vector norms and scores on identical documents.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Geometry alone cannot establish semantic relevance; these vectors are hand-authored.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →No. Five manually chosen 2D vectors, not embeddings generated by a language model. Zero-vector cosine is defined as 0 in this lab. Ties preserve fixture order.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Compare ranking order, vector norms and scores on identical documents. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Open embedding-5, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Geometry alone cannot establish semantic relevance; these vectors are hand-authored. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Introduction to Information Retrieval · vector space scoring ↗
打开实验 →Eight KV heads use one quarter the cache of 32 heads.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →KV
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →{"sequence":4096,"batch":1,"heads":8,"bytes":2,"block":16}
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →{"KV cache (GiB)":0.5,"Allocated KV (GiB)":0.5,"Padding tokens":0}
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Keep the supplied fixture and other settings fixed; change sequence. Compare logical KV GiB, allocated GiB and padding tokens.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →An estimated cache footprint is not a measured GPU throughput or total memory requirement.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →No. 32 layers, head dimension 128. Dense cache only; excludes model weights, activations, block tables and sharing. Per-sequence allocation rounds up to the next page.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Compare logical KV GiB, allocated GiB and padding tokens. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Open kv-1, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →An estimated cache footprint is not a measured GPU throughput or total memory requirement. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →More KV heads scale cache bytes linearly.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →KV
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →{"sequence":4096,"batch":1,"heads":32,"bytes":2,"block":16}
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →{"KV cache (GiB)":2,"Allocated KV (GiB)":2,"Padding tokens":0}
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Keep the supplied fixture and other settings fixed; change sequence. Compare logical KV GiB, allocated GiB and padding tokens.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →An estimated cache footprint is not a measured GPU throughput or total memory requirement.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →No. 32 layers, head dimension 128. Dense cache only; excludes model weights, activations, block tables and sharing. Per-sequence allocation rounds up to the next page.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Compare logical KV GiB, allocated GiB and padding tokens. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Open kv-2, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →An estimated cache footprint is not a measured GPU throughput or total memory requirement. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →One KV head sharply reduces logical cache size.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →KV
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →{"sequence":4096,"batch":1,"heads":1,"bytes":2,"block":16}
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →{"KV cache (GiB)":0.0625,"Allocated KV (GiB)":0.0625,"Padding tokens":0}
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Keep the supplied fixture and other settings fixed; change sequence. Compare logical KV GiB, allocated GiB and padding tokens.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →An estimated cache footprint is not a measured GPU throughput or total memory requirement.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →No. 32 layers, head dimension 128. Dense cache only; excludes model weights, activations, block tables and sharing. Per-sequence allocation rounds up to the next page.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Compare logical KV GiB, allocated GiB and padding tokens. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Open kv-3, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →An estimated cache footprint is not a measured GPU throughput or total memory requirement. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Sequence and batch size multiply memory demand.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →KV
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →{"sequence":32768,"batch":8,"heads":8,"bytes":2,"block":16}
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →{"KV cache (GiB)":32,"Allocated KV (GiB)":32,"Padding tokens":0}
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Keep the supplied fixture and other settings fixed; change sequence. Compare logical KV GiB, allocated GiB and padding tokens.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →An estimated cache footprint is not a measured GPU throughput or total memory requirement.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →No. 32 layers, head dimension 128. Dense cache only; excludes model weights, activations, block tables and sharing. Per-sequence allocation rounds up to the next page.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Compare logical KV GiB, allocated GiB and padding tokens. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Open kv-4, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →An estimated cache footprint is not a measured GPU throughput or total memory requirement. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →One extra token allocates an additional page per sequence.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →KV
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →{"sequence":4097,"batch":1,"heads":8,"bytes":2,"block":256}
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →{"KV cache (GiB)":0.5001220703125,"Allocated KV (GiB)":0.53125,"Padding tokens":255}
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Keep the supplied fixture and other settings fixed; change sequence. Compare logical KV GiB, allocated GiB and padding tokens.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →An estimated cache footprint is not a measured GPU throughput or total memory requirement.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →No. 32 layers, head dimension 128. Dense cache only; excludes model weights, activations, block tables and sharing. Per-sequence allocation rounds up to the next page.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Compare logical KV GiB, allocated GiB and padding tokens. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →Open kv-5, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →An estimated cache footprint is not a measured GPU throughput or total memory requirement. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: GQA (2023); PagedAttention (2023) ↗
打开实验 →A nonlinear hidden layer can fit this non-linearly separable task.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →{"rate":0.3,"hidden":8,"epochs":1000,"task":0,"seed":42}
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Run the seeded training lab and inspect the actual loss and accuracy; convergence is not assumed.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Keep the supplied fixture and other settings fixed; change rate. Watch train loss, held-out accuracy and decision boundary over epochs.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Training convergence on tiny synthetic data does not imply an LLM can train in the browser.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →No. Actual full-batch gradient descent in JavaScript. 2 inputs → tanh hidden layer → sigmoid. Binary cross-entropy; 100 synthetic training and 100 held-out samples from the same generator. Held-out accuracy is not real-world generalization.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Watch train loss, held-out accuracy and decision boundary over epochs. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Open neural-1, reset the preset, retain the exact inputs and run. Retain the seed, training epochs and all weights.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Training convergence on tiny synthetic data does not imply an LLM can train in the browser. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →One hidden unit limits the available decision boundaries.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →{"rate":0.3,"hidden":1,"epochs":1000,"task":0,"seed":42}
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Run the seeded training lab and inspect the actual loss and accuracy; convergence is not assumed.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Keep the supplied fixture and other settings fixed; change rate. Watch train loss, held-out accuracy and decision boundary over epochs.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Training convergence on tiny synthetic data does not imply an LLM can train in the browser.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →No. Actual full-batch gradient descent in JavaScript. 2 inputs → tanh hidden layer → sigmoid. Binary cross-entropy; 100 synthetic training and 100 held-out samples from the same generator. Held-out accuracy is not real-world generalization.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Watch train loss, held-out accuracy and decision boundary over epochs. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Open neural-2, reset the preset, retain the exact inputs and run. Retain the seed, training epochs and all weights.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Training convergence on tiny synthetic data does not imply an LLM can train in the browser. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →A lower learning rate may need more epochs.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →{"rate":0.01,"hidden":8,"epochs":1000,"task":0,"seed":42}
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Run the seeded training lab and inspect the actual loss and accuracy; convergence is not assumed.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Keep the supplied fixture and other settings fixed; change rate. Watch train loss, held-out accuracy and decision boundary over epochs.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Training convergence on tiny synthetic data does not imply an LLM can train in the browser.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →No. Actual full-batch gradient descent in JavaScript. 2 inputs → tanh hidden layer → sigmoid. Binary cross-entropy; 100 synthetic training and 100 held-out samples from the same generator. Held-out accuracy is not real-world generalization.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Watch train loss, held-out accuracy and decision boundary over epochs. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Open neural-3, reset the preset, retain the exact inputs and run. Retain the seed, training epochs and all weights.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Training convergence on tiny synthetic data does not imply an LLM can train in the browser. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →The hidden layer can approximate a curved boundary.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →{"rate":0.3,"hidden":16,"epochs":1000,"task":1,"seed":42}
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Run the seeded training lab and inspect the actual loss and accuracy; convergence is not assumed.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Keep the supplied fixture and other settings fixed; change rate. Watch train loss, held-out accuracy and decision boundary over epochs.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Training convergence on tiny synthetic data does not imply an LLM can train in the browser.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →No. Actual full-batch gradient descent in JavaScript. 2 inputs → tanh hidden layer → sigmoid. Binary cross-entropy; 100 synthetic training and 100 held-out samples from the same generator. Held-out accuracy is not real-world generalization.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Watch train loss, held-out accuracy and decision boundary over epochs. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Open neural-4, reset the preset, retain the exact inputs and run. Retain the seed, training epochs and all weights.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Training convergence on tiny synthetic data does not imply an LLM can train in the browser. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Initialization and sampled data affect the learning trajectory.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →{"rate":0.3,"hidden":8,"epochs":1000,"task":0,"seed":7}
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Run the seeded training lab and inspect the actual loss and accuracy; convergence is not assumed.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Keep the supplied fixture and other settings fixed; change rate. Watch train loss, held-out accuracy and decision boundary over epochs.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Training convergence on tiny synthetic data does not imply an LLM can train in the browser.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →No. Actual full-batch gradient descent in JavaScript. 2 inputs → tanh hidden layer → sigmoid. Binary cross-entropy; 100 synthetic training and 100 held-out samples from the same generator. Held-out accuracy is not real-world generalization.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Watch train loss, held-out accuracy and decision boundary over epochs. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Open neural-5, reset the preset, retain the exact inputs and run. Retain the seed, training epochs and all weights.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →Training convergence on tiny synthetic data does not imply an LLM can train in the browser. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Dive into Deep Learning · multilayer perceptrons ↗
打开实验 →A write pauses without the mock approval toggle.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Plan → allowlist → approval → budget → mock tool → FIFO memory → next step
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →{"plan":"search,calculator,recall,write","allowed":"search,calculator,recall,write","steps":6,"budget":0.1,"memory":3,"approval":0}
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →{"Executed / observed steps":4,"Simulated cost (USD)":0.03,"Memory entries":3}
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Keep the supplied fixture and other settings fixed; change steps. Inspect the trace, stopping reason, simulated spend and retained memory.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →No. A deterministic state machine executes your plan against mock tools, not an autonomous language model. Each executed tool costs a synthetic $0.01. The write action never changes external data. Each mock result is truncated to 1,000 characters to bound memory. Guards are illustrative policy rules, not a complete security system.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Inspect the trace, stopping reason, simulated spend and retained memory. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Open agent-1, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Approval permits only the local mock write.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Plan → allowlist → approval → budget → mock tool → FIFO memory → next step
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →{"plan":"search,calculator,recall,write","allowed":"search,calculator,recall,write","steps":6,"budget":0.1,"memory":3,"approval":1}
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →{"Executed / observed steps":4,"Simulated cost (USD)":0.04,"Memory entries":3}
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Keep the supplied fixture and other settings fixed; change steps. Inspect the trace, stopping reason, simulated spend and retained memory.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →No. A deterministic state machine executes your plan against mock tools, not an autonomous language model. Each executed tool costs a synthetic $0.01. The write action never changes external data. Each mock result is truncated to 1,000 characters to bound memory. Guards are illustrative policy rules, not a complete security system.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Inspect the trace, stopping reason, simulated spend and retained memory. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Open agent-2, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →A disallowed calculator call stops the workflow.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Plan → allowlist → approval → budget → mock tool → FIFO memory → next step
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →{"plan":"search,calculator,recall,write","allowed":"search,recall","steps":6,"budget":0.1,"memory":3,"approval":0}
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →{"Executed / observed steps":2,"Simulated cost (USD)":0.01,"Memory entries":1}
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Keep the supplied fixture and other settings fixed; change steps. Inspect the trace, stopping reason, simulated spend and retained memory.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →No. A deterministic state machine executes your plan against mock tools, not an autonomous language model. Each executed tool costs a synthetic $0.01. The write action never changes external data. Each mock result is truncated to 1,000 characters to bound memory. Guards are illustrative policy rules, not a complete security system.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Inspect the trace, stopping reason, simulated spend and retained memory. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Open agent-3, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →The second paid action is blocked before exceeding budget.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Plan → allowlist → approval → budget → mock tool → FIFO memory → next step
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →{"plan":"search,calculator,recall,write","allowed":"search,calculator,recall,write","steps":6,"budget":0.01,"memory":3,"approval":0}
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →{"Executed / observed steps":1,"Simulated cost (USD)":0.01,"Memory entries":1}
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Keep the supplied fixture and other settings fixed; change steps. Inspect the trace, stopping reason, simulated spend and retained memory.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →No. A deterministic state machine executes your plan against mock tools, not an autonomous language model. Each executed tool costs a synthetic $0.01. The write action never changes external data. Each mock result is truncated to 1,000 characters to bound memory. Guards are illustrative policy rules, not a complete security system.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Inspect the trace, stopping reason, simulated spend and retained memory. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Open agent-4, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Only the latest result survives FIFO truncation.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Plan → allowlist → approval → budget → mock tool → FIFO memory → next step
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →{"plan":"search,calculator,recall","allowed":"search,calculator,recall,write","steps":6,"budget":0.1,"memory":1,"approval":0}
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →{"Executed / observed steps":3,"Simulated cost (USD)":0.03,"Memory entries":1}
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Keep the supplied fixture and other settings fixed; change steps. Inspect the trace, stopping reason, simulated spend and retained memory.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →No. A deterministic state machine executes your plan against mock tools, not an autonomous language model. Each executed tool costs a synthetic $0.01. The write action never changes external data. Each mock result is truncated to 1,000 characters to bound memory. Guards are illustrative policy rules, not a complete security system.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Inspect the trace, stopping reason, simulated spend and retained memory. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Open agent-5, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗
打开实验 →Combining independent ranks can promote shared candidates.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →{"query":"KV cache memory","documents":"[\n {\n \"id\": \"D1\",\n \"text\": \"KV cache stores keys and values for generation\",\n \"vector\": [\n 1,\n 0.1\n ],\n \"relevance\": 3\n },\n {\n \"id\": \"D2\",\n \"text\": \"Cache restaurant menu and opening hours\",\n \"vector\": [\n 0.1,\n 1\n ],\n \"relevance\": 0\n },\n {\n \"id\": \"D3\",\n \"text\": \"Grouped query attention reduces memory\",\n \"vector\": [\n 0.95,\n 0.2\n ],\n \"relevance\": 2\n },\n {\n \"id\": \"D4\",\n \"text\": \"KV cache memory footprint depends on sequence length\",\n \"vector\": [\n 0.8,\n 0.3\n ],\n \"relevance\": 3\n },\n {\n \"id\": \"D5\",\n \"text\": \"Tokenization splits input text\",\n \"vector\": [\n 0.4,\n 0.8\n ],\n \"relevance\": 0\n }\n]","x":1,"y":0.1,"weight":0.5,"k":60,"retrieve":5,"top":3,"rerank":0}
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →{"Recall@k":1,"nDCG@k":1}
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Keep the supplied fixture and other settings fixed; change weight. Compare Recall@k and nDCG@k while varying candidate pool and fusion weight.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →An oracle reranker leaks labels intentionally; never report it as learned model performance.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →No. Lexical score counts matching query terms (not BM25). Dense score is cosine on supplied 2D vectors, not a live embedding model. RRF combines ranks. Optional reranking uses known relevance labels as an oracle upper-bound, not a trained reranker. Empty query ties preserve document order.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Compare Recall@k and nDCG@k while varying candidate pool and fusion weight. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Open retrieval-1, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →An oracle reranker leaks labels intentionally; never report it as learned model performance. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Exact query words alone set the ranking.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →{"query":"KV cache memory","documents":"[\n {\n \"id\": \"D1\",\n \"text\": \"KV cache stores keys and values for generation\",\n \"vector\": [\n 1,\n 0.1\n ],\n \"relevance\": 3\n },\n {\n \"id\": \"D2\",\n \"text\": \"Cache restaurant menu and opening hours\",\n \"vector\": [\n 0.1,\n 1\n ],\n \"relevance\": 0\n },\n {\n \"id\": \"D3\",\n \"text\": \"Grouped query attention reduces memory\",\n \"vector\": [\n 0.95,\n 0.2\n ],\n \"relevance\": 2\n },\n {\n \"id\": \"D4\",\n \"text\": \"KV cache memory footprint depends on sequence length\",\n \"vector\": [\n 0.8,\n 0.3\n ],\n \"relevance\": 3\n },\n {\n \"id\": \"D5\",\n \"text\": \"Tokenization splits input text\",\n \"vector\": [\n 0.4,\n 0.8\n ],\n \"relevance\": 0\n }\n]","x":1,"y":0.1,"weight":1,"k":60,"retrieve":5,"top":3,"rerank":0}
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →{"Recall@k":0.6666666666666666,"nDCG@k":0.8838695436828513}
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Keep the supplied fixture and other settings fixed; change weight. Compare Recall@k and nDCG@k while varying candidate pool and fusion weight.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →An oracle reranker leaks labels intentionally; never report it as learned model performance.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →No. Lexical score counts matching query terms (not BM25). Dense score is cosine on supplied 2D vectors, not a live embedding model. RRF combines ranks. Optional reranking uses known relevance labels as an oracle upper-bound, not a trained reranker. Empty query ties preserve document order.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Compare Recall@k and nDCG@k while varying candidate pool and fusion weight. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Open retrieval-2, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →An oracle reranker leaks labels intentionally; never report it as learned model performance. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Only vector direction determines the ranking.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →{"query":"KV cache memory","documents":"[\n {\n \"id\": \"D1\",\n \"text\": \"KV cache stores keys and values for generation\",\n \"vector\": [\n 1,\n 0.1\n ],\n \"relevance\": 3\n },\n {\n \"id\": \"D2\",\n \"text\": \"Cache restaurant menu and opening hours\",\n \"vector\": [\n 0.1,\n 1\n ],\n \"relevance\": 0\n },\n {\n \"id\": \"D3\",\n \"text\": \"Grouped query attention reduces memory\",\n \"vector\": [\n 0.95,\n 0.2\n ],\n \"relevance\": 2\n },\n {\n \"id\": \"D4\",\n \"text\": \"KV cache memory footprint depends on sequence length\",\n \"vector\": [\n 0.8,\n 0.3\n ],\n \"relevance\": 3\n },\n {\n \"id\": \"D5\",\n \"text\": \"Tokenization splits input text\",\n \"vector\": [\n 0.4,\n 0.8\n ],\n \"relevance\": 0\n }\n]","x":1,"y":0.1,"weight":0,"k":60,"retrieve":5,"top":3,"rerank":0}
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →{"Recall@k":1,"nDCG@k":0.9594535145926796}
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Keep the supplied fixture and other settings fixed; change weight. Compare Recall@k and nDCG@k while varying candidate pool and fusion weight.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →An oracle reranker leaks labels intentionally; never report it as learned model performance.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →No. Lexical score counts matching query terms (not BM25). Dense score is cosine on supplied 2D vectors, not a live embedding model. RRF combines ranks. Optional reranking uses known relevance labels as an oracle upper-bound, not a trained reranker. Empty query ties preserve document order.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Compare Recall@k and nDCG@k while varying candidate pool and fusion weight. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Open retrieval-3, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →An oracle reranker leaks labels intentionally; never report it as learned model performance. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Perfect labels can improve ordering only inside the candidate pool.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →{"query":"KV cache memory","documents":"[\n {\n \"id\": \"D1\",\n \"text\": \"KV cache stores keys and values for generation\",\n \"vector\": [\n 1,\n 0.1\n ],\n \"relevance\": 3\n },\n {\n \"id\": \"D2\",\n \"text\": \"Cache restaurant menu and opening hours\",\n \"vector\": [\n 0.1,\n 1\n ],\n \"relevance\": 0\n },\n {\n \"id\": \"D3\",\n \"text\": \"Grouped query attention reduces memory\",\n \"vector\": [\n 0.95,\n 0.2\n ],\n \"relevance\": 2\n },\n {\n \"id\": \"D4\",\n \"text\": \"KV cache memory footprint depends on sequence length\",\n \"vector\": [\n 0.8,\n 0.3\n ],\n \"relevance\": 3\n },\n {\n \"id\": \"D5\",\n \"text\": \"Tokenization splits input text\",\n \"vector\": [\n 0.4,\n 0.8\n ],\n \"relevance\": 0\n }\n]","x":1,"y":0.1,"weight":0.5,"k":60,"retrieve":5,"top":3,"rerank":1}
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →{"Recall@k":1,"nDCG@k":1}
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Keep the supplied fixture and other settings fixed; change weight. Compare Recall@k and nDCG@k while varying candidate pool and fusion weight.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →An oracle reranker leaks labels intentionally; never report it as learned model performance.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →No. Lexical score counts matching query terms (not BM25). Dense score is cosine on supplied 2D vectors, not a live embedding model. RRF combines ranks. Optional reranking uses known relevance labels as an oracle upper-bound, not a trained reranker. Empty query ties preserve document order.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Compare Recall@k and nDCG@k while varying candidate pool and fusion weight. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Open retrieval-4, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →An oracle reranker leaks labels intentionally; never report it as learned model performance. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Even an oracle cannot recover documents excluded before reranking.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →{"query":"KV cache memory","documents":"[\n {\n \"id\": \"D1\",\n \"text\": \"KV cache stores keys and values for generation\",\n \"vector\": [\n 1,\n 0.1\n ],\n \"relevance\": 3\n },\n {\n \"id\": \"D2\",\n \"text\": \"Cache restaurant menu and opening hours\",\n \"vector\": [\n 0.1,\n 1\n ],\n \"relevance\": 0\n },\n {\n \"id\": \"D3\",\n \"text\": \"Grouped query attention reduces memory\",\n \"vector\": [\n 0.95,\n 0.2\n ],\n \"relevance\": 2\n },\n {\n \"id\": \"D4\",\n \"text\": \"KV cache memory footprint depends on sequence length\",\n \"vector\": [\n 0.8,\n 0.3\n ],\n \"relevance\": 3\n },\n {\n \"id\": \"D5\",\n \"text\": \"Tokenization splits input text\",\n \"vector\": [\n 0.4,\n 0.8\n ],\n \"relevance\": 0\n }\n]","x":1,"y":0.1,"weight":0.5,"k":60,"retrieve":1,"top":3,"rerank":1}
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →{"Recall@k":0.3333333333333333,"nDCG@k":0.541942129480027}
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Keep the supplied fixture and other settings fixed; change weight. Compare Recall@k and nDCG@k while varying candidate pool and fusion weight.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →An oracle reranker leaks labels intentionally; never report it as learned model performance.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →No. Lexical score counts matching query terms (not BM25). Dense score is cosine on supplied 2D vectors, not a live embedding model. RRF combines ranks. Optional reranking uses known relevance labels as an oracle upper-bound, not a trained reranker. Empty query ties preserve document order.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Compare Recall@k and nDCG@k while varying candidate pool and fusion weight. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Open retrieval-5, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →An oracle reranker leaks labels intentionally; never report it as learned model performance. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Reciprocal Rank Fusion · SIGIR 2009 ↗
打开实验 →Agreement and order consistency measure different properties.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →{"task":"Compare two answers for factual correctness using supplied evidence.","ratings":"[\n {\n \"human\": \"A\",\n \"judge\": \"A\",\n \"swapped\": \"B\"\n },\n {\n \"human\": \"B\",\n \"judge\": \"A\",\n \"swapped\": \"A\"\n },\n {\n \"human\": \"tie\",\n \"judge\": \"tie\",\n \"swapped\": \"tie\"\n },\n {\n \"human\": \"B\",\n \"judge\": \"B\",\n \"swapped\": \"A\"\n }\n]"}
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →{"Human agreement":0.75,"Order consistency":0.75,"A wins (original)":0.5}
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Keep the supplied fixture and other settings fixed; change ratings. Compare human agreement and order consistency; retain evidence spans in real evaluations.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →High agreement on a small biased sample does not validate a judge for a new domain.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →No. Build an exportable rubric and analyze supplied judge labels. No LLM is called. In swapped trials A means original B and vice versa; normalize before computing consistency. Fixture labels are synthetic. Rubric wording is an original starting point, not a validated scale.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Compare human agreement and order consistency; retain evidence spans in real evaluations. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Open judge-1, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →High agreement on a small biased sample does not validate a judge for a new domain. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Perfect labels yield agreement and consistency of one.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →{"task":"Compare two answers for factual correctness using supplied evidence.","ratings":"[{\"human\":\"A\",\"judge\":\"A\",\"swapped\":\"B\"},{\"human\":\"B\",\"judge\":\"B\",\"swapped\":\"A\"}]"}
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →{"Human agreement":1,"Order consistency":1,"A wins (original)":0.5}
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Keep the supplied fixture and other settings fixed; change ratings. Compare human agreement and order consistency; retain evidence spans in real evaluations.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →High agreement on a small biased sample does not validate a judge for a new domain.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →No. Build an exportable rubric and analyze supplied judge labels. No LLM is called. In swapped trials A means original B and vice versa; normalize before computing consistency. Fixture labels are synthetic. Rubric wording is an original starting point, not a validated scale.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Compare human agreement and order consistency; retain evidence spans in real evaluations. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Open judge-2, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →High agreement on a small biased sample does not validate a judge for a new domain. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Selecting the first position fails swap consistency.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →{"task":"Compare two answers for factual correctness using supplied evidence.","ratings":"[{\"human\":\"A\",\"judge\":\"A\",\"swapped\":\"A\"},{\"human\":\"B\",\"judge\":\"A\",\"swapped\":\"A\"}]"}
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →{"Human agreement":0.5,"Order consistency":0,"A wins (original)":1}
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Keep the supplied fixture and other settings fixed; change ratings. Compare human agreement and order consistency; retain evidence spans in real evaluations.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →High agreement on a small biased sample does not validate a judge for a new domain.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →No. Build an exportable rubric and analyze supplied judge labels. No LLM is called. In swapped trials A means original B and vice versa; normalize before computing consistency. Fixture labels are synthetic. Rubric wording is an original starting point, not a validated scale.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Compare human agreement and order consistency; retain evidence spans in real evaluations. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Open judge-3, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →High agreement on a small biased sample does not validate a judge for a new domain. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →A large tie class can make aggregate agreement misleading.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →{"task":"Compare two answers for factual correctness using supplied evidence.","ratings":"[{\"human\":\"tie\",\"judge\":\"tie\",\"swapped\":\"tie\"},{\"human\":\"tie\",\"judge\":\"A\",\"swapped\":\"B\"},{\"human\":\"tie\",\"judge\":\"tie\",\"swapped\":\"tie\"}]"}
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →{"Human agreement":0.6666666666666666,"Order consistency":1,"A wins (original)":0.3333333333333333}
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Keep the supplied fixture and other settings fixed; change ratings. Compare human agreement and order consistency; retain evidence spans in real evaluations.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →High agreement on a small biased sample does not validate a judge for a new domain.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →No. Build an exportable rubric and analyze supplied judge labels. No LLM is called. In swapped trials A means original B and vice versa; normalize before computing consistency. Fixture labels are synthetic. Rubric wording is an original starting point, not a validated scale.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Compare human agreement and order consistency; retain evidence spans in real evaluations. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Open judge-4, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →High agreement on a small biased sample does not validate a judge for a new domain. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →A judge can be order-consistent and disagree with every human label.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →{"task":"Compare two answers for factual correctness using supplied evidence.","ratings":"[{\"human\":\"A\",\"judge\":\"B\",\"swapped\":\"A\"},{\"human\":\"B\",\"judge\":\"A\",\"swapped\":\"B\"}]"}
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →{"Human agreement":0,"Order consistency":1,"A wins (original)":0.5}
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Keep the supplied fixture and other settings fixed; change ratings. Compare human agreement and order consistency; retain evidence spans in real evaluations.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →High agreement on a small biased sample does not validate a judge for a new domain.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →No. Build an exportable rubric and analyze supplied judge labels. No LLM is called. In swapped trials A means original B and vice versa; normalize before computing consistency. Fixture labels are synthetic. Rubric wording is an original starting point, not a validated scale.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Compare human agreement and order consistency; retain evidence spans in real evaluations. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Open judge-5, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →High agreement on a small biased sample does not validate a judge for a new domain. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Judging LLM-as-a-Judge · MT-Bench ↗
打开实验 →Most traffic uses the cheap path; failures add strong-model cost.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →E[C]=s(Ccheap+f×Cstrong)+(1−s)Cstrong; E[T]=s(Tcheap+f×Tstrong)+(1−s)Tstrong
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →{"requests":1000,"cheap":0.8,"failure":0.1,"cheapCost":0.001,"strongCost":0.02,"cheapMs":200,"strongMs":1000}
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →{"Expected USD/request":0.0063999999999999994,"Expected total USD":6.3999999999999995,"Expected latency (ms)":439.99999999999994,"Fallback calls":80}
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Keep the supplied fixture and other settings fixed; change failure. Compare expected cost, mean latency and fallback volume with observed production traces.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →A cheaper route may violate task-quality requirements; this estimator does not measure quality.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →No. Analytical expectation with user-supplied synthetic per-request prices and latencies, not current provider rates. Sequential one-time fallback; failed primary attempts are charged; fallback always succeeds. No quality prediction, queueing, correlated failures or tail-latency model.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Compare expected cost, mean latency and fallback volume with observed production traces. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Open routing-1, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →A cheaper route may violate task-quality requirements; this estimator does not measure quality. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Strong-only routing provides a simple cost baseline.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →E[C]=s(Ccheap+f×Cstrong)+(1−s)Cstrong; E[T]=s(Tcheap+f×Tstrong)+(1−s)Tstrong
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →{"requests":1000,"cheap":0,"failure":0.1,"cheapCost":0.001,"strongCost":0.02,"cheapMs":200,"strongMs":1000}
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →{"Expected USD/request":0.02,"Expected total USD":20,"Expected latency (ms)":1000,"Fallback calls":0}
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Keep the supplied fixture and other settings fixed; change failure. Compare expected cost, mean latency and fallback volume with observed production traces.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →A cheaper route may violate task-quality requirements; this estimator does not measure quality.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →No. Analytical expectation with user-supplied synthetic per-request prices and latencies, not current provider rates. Sequential one-time fallback; failed primary attempts are charged; fallback always succeeds. No quality prediction, queueing, correlated failures or tail-latency model.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Compare expected cost, mean latency and fallback volume with observed production traces. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Open routing-2, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →A cheaper route may violate task-quality requirements; this estimator does not measure quality. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →High fallback frequency can erase routing savings.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →E[C]=s(Ccheap+f×Cstrong)+(1−s)Cstrong; E[T]=s(Tcheap+f×Tstrong)+(1−s)Tstrong
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →{"requests":1000,"cheap":0.8,"failure":0.8,"cheapCost":0.001,"strongCost":0.02,"cheapMs":200,"strongMs":1000}
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →{"Expected USD/request":0.0176,"Expected total USD":17.6,"Expected latency (ms)":1000,"Fallback calls":640}
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Keep the supplied fixture and other settings fixed; change failure. Compare expected cost, mean latency and fallback volume with observed production traces.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →A cheaper route may violate task-quality requirements; this estimator does not measure quality.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →No. Analytical expectation with user-supplied synthetic per-request prices and latencies, not current provider rates. Sequential one-time fallback; failed primary attempts are charged; fallback always succeeds. No quality prediction, queueing, correlated failures or tail-latency model.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Compare expected cost, mean latency and fallback volume with observed production traces. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Open routing-3, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →A cheaper route may violate task-quality requirements; this estimator does not measure quality. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Every request pays both cost and latency before successful fallback.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →E[C]=s(Ccheap+f×Cstrong)+(1−s)Cstrong; E[T]=s(Tcheap+f×Tstrong)+(1−s)Tstrong
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →{"requests":1000,"cheap":1,"failure":1,"cheapCost":0.001,"strongCost":0.02,"cheapMs":200,"strongMs":1000}
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →{"Expected USD/request":0.021,"Expected total USD":21,"Expected latency (ms)":1200,"Fallback calls":1000}
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Keep the supplied fixture and other settings fixed; change failure. Compare expected cost, mean latency and fallback volume with observed production traces.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →A cheaper route may violate task-quality requirements; this estimator does not measure quality.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →No. Analytical expectation with user-supplied synthetic per-request prices and latencies, not current provider rates. Sequential one-time fallback; failed primary attempts are charged; fallback always succeeds. No quality prediction, queueing, correlated failures or tail-latency model.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Compare expected cost, mean latency and fallback volume with observed production traces. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Open routing-4, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →A cheaper route may violate task-quality requirements; this estimator does not measure quality. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Expected cost is a weighted average without retries.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →E[C]=s(Ccheap+f×Cstrong)+(1−s)Cstrong; E[T]=s(Tcheap+f×Tstrong)+(1−s)Tstrong
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →{"requests":1000,"cheap":0.8,"failure":0,"cheapCost":0.001,"strongCost":0.02,"cheapMs":200,"strongMs":1000}
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →{"Expected USD/request":0.0048,"Expected total USD":4.8,"Expected latency (ms)":359.99999999999994,"Fallback calls":0}
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Keep the supplied fixture and other settings fixed; change failure. Compare expected cost, mean latency and fallback volume with observed production traces.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →A cheaper route may violate task-quality requirements; this estimator does not measure quality.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →No. Analytical expectation with user-supplied synthetic per-request prices and latencies, not current provider rates. Sequential one-time fallback; failed primary attempts are charged; fallback always succeeds. No quality prediction, queueing, correlated failures or tail-latency model.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Compare expected cost, mean latency and fallback volume with observed production traces. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Open routing-5, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →A cheaper route may violate task-quality requirements; this estimator does not measure quality. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: AWS Builders Library · timeouts, retries and backoff ↗
打开实验 →Fewer representable values introduce rounding error.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →{"values":"-1,-0.7,-0.2,0,0.15,0.4,0.8,1","bits":4}
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →{"MSE":0.0009502551020408147,"Max absolute error":0.05714285714285716,"Ideal compression vs FP32":8,"Scale":0.14285714285714285}
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Keep the supplied fixture and other settings fixed; change bits. Compare reconstruction error, MSE, scale and ideal compression.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Low scalar MSE does not guarantee stable downstream model quality.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →No. Symmetric per-tensor scalar quantizer with range ±(2^(bits−1)−1). This is not LLM.int8(), GPTQ or AWQ. Ideal compression ignores scale metadata and runtime packing. Scalar error does not measure language-model accuracy.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Compare reconstruction error, MSE, scale and ideal compression. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Open quantization-1, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Low scalar MSE does not guarantee stable downstream model quality. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →More quantization levels usually reduce reconstruction error.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →{"values":"-1,-0.7,-0.2,0,0.15,0.4,0.8,1","bits":8}
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →{"MSE":0.0000028868807737615443,"Max absolute error":0.003149606299212626,"Ideal compression vs FP32":4,"Scale":0.007874015748031496}
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Keep the supplied fixture and other settings fixed; change bits. Compare reconstruction error, MSE, scale and ideal compression.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Low scalar MSE does not guarantee stable downstream model quality.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →No. Symmetric per-tensor scalar quantizer with range ±(2^(bits−1)−1). This is not LLM.int8(), GPTQ or AWQ. Ideal compression ignores scale metadata and runtime packing. Scalar error does not measure language-model accuracy.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Compare reconstruction error, MSE, scale and ideal compression. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Open quantization-2, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Low scalar MSE does not guarantee stable downstream model quality. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Only three signed levels preserve little detail.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →{"values":"-1,-0.7,-0.2,0,0.15,0.4,0.8,1","bits":2}
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →{"MSE":0.044062500000000004,"Max absolute error":0.4,"Ideal compression vs FP32":16,"Scale":1}
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Keep the supplied fixture and other settings fixed; change bits. Compare reconstruction error, MSE, scale and ideal compression.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Low scalar MSE does not guarantee stable downstream model quality.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →No. Symmetric per-tensor scalar quantizer with range ±(2^(bits−1)−1). This is not LLM.int8(), GPTQ or AWQ. Ideal compression ignores scale metadata and runtime packing. Scalar error does not measure language-model accuracy.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Compare reconstruction error, MSE, scale and ideal compression. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Open quantization-3, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Low scalar MSE does not guarantee stable downstream model quality. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →An outlier enlarges the global step and collapses small values.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →{"values":"-1,-0.7,-0.2,0,0.15,0.4,0.8,30","bits":4}
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →{"MSE":0.2940625,"Max absolute error":1,"Ideal compression vs FP32":8,"Scale":4.285714285714286}
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Keep the supplied fixture and other settings fixed; change bits. Compare reconstruction error, MSE, scale and ideal compression.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Low scalar MSE does not guarantee stable downstream model quality.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →No. Symmetric per-tensor scalar quantizer with range ±(2^(bits−1)−1). This is not LLM.int8(), GPTQ or AWQ. Ideal compression ignores scale metadata and runtime packing. Scalar error does not measure language-model accuracy.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Compare reconstruction error, MSE, scale and ideal compression. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Open quantization-4, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Low scalar MSE does not guarantee stable downstream model quality. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →The zero-range safeguard reconstructs all zeros exactly.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →{"values":"0,0,0,0","bits":4}
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →{"MSE":0,"Max absolute error":0,"Ideal compression vs FP32":8,"Scale":1}
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Keep the supplied fixture and other settings fixed; change bits. Compare reconstruction error, MSE, scale and ideal compression.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Low scalar MSE does not guarantee stable downstream model quality.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →No. Symmetric per-tensor scalar quantizer with range ±(2^(bits−1)−1). This is not LLM.int8(), GPTQ or AWQ. Ideal compression ignores scale metadata and runtime packing. Scalar error does not measure language-model accuracy.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Compare reconstruction error, MSE, scale and ideal compression. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Open quantization-5, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Low scalar MSE does not guarantee stable downstream model quality. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: LLM.int8() · outliers and mixed precision ↗
打开实验 →Repeated keys can avoid repeated model work.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →hit
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →{"keys":"A,B,A,C,A,B,D,A","capacity":3,"ttl":60,"interval":1,"cost":0.01,"missMs":1000,"hitMs":10}
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →{"Hit rate":0.5,"Misses":4,"Cost USD":0.04,"Mean latency (ms)":505}
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Keep the supplied fixture and other settings fixed; change capacity. Inspect hit/miss trace, eviction order, cost and mean latency.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Cross-tenant key reuse can expose private data; stale cached answers may be incorrect.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →No. Exact-key LRU simulation. TTL starts on insertion and does not refresh on hits. Expired entries are removed before each request. Assumes stable outputs within TTL. A real cache key should include tenant, model/version, prompt and generation settings; this lab uses letters.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Inspect hit/miss trace, eviction order, cost and mean latency. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Open cache-1, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Cross-tenant key reuse can expose private data; stale cached answers may be incorrect. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →A tiny cache evicts useful entries before reuse.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →hit
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →{"keys":"A,B,A,C,A,B,D,A","capacity":1,"ttl":60,"interval":1,"cost":0.01,"missMs":1000,"hitMs":10}
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →{"Hit rate":0,"Misses":8,"Cost USD":0.08,"Mean latency (ms)":1000}
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Keep the supplied fixture and other settings fixed; change capacity. Inspect hit/miss trace, eviction order, cost and mean latency.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Cross-tenant key reuse can expose private data; stale cached answers may be incorrect.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →No. Exact-key LRU simulation. TTL starts on insertion and does not refresh on hits. Expired entries are removed before each request. Assumes stable outputs within TTL. A real cache key should include tenant, model/version, prompt and generation settings; this lab uses letters.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Inspect hit/miss trace, eviction order, cost and mean latency. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Open cache-2, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Cross-tenant key reuse can expose private data; stale cached answers may be incorrect. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Entries expire at the next request with this interval.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →hit
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →{"keys":"A,B,A,C,A,B,D,A","capacity":3,"ttl":1,"interval":1,"cost":0.01,"missMs":1000,"hitMs":10}
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →{"Hit rate":0,"Misses":8,"Cost USD":0.08,"Mean latency (ms)":1000}
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Keep the supplied fixture and other settings fixed; change capacity. Inspect hit/miss trace, eviction order, cost and mean latency.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Cross-tenant key reuse can expose private data; stale cached answers may be incorrect.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →No. Exact-key LRU simulation. TTL starts on insertion and does not refresh on hits. Expired entries are removed before each request. Assumes stable outputs within TTL. A real cache key should include tenant, model/version, prompt and generation settings; this lab uses letters.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Inspect hit/miss trace, eviction order, cost and mean latency. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Open cache-3, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Cross-tenant key reuse can expose private data; stale cached answers may be incorrect. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Temporal locality improves the hit rate.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →hit
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →{"keys":"A,A,A,A,B,B,A,A","capacity":3,"ttl":60,"interval":1,"cost":0.01,"missMs":1000,"hitMs":10}
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →{"Hit rate":0.75,"Misses":2,"Cost USD":0.02,"Mean latency (ms)":257.5}
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Keep the supplied fixture and other settings fixed; change capacity. Inspect hit/miss trace, eviction order, cost and mean latency.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Cross-tenant key reuse can expose private data; stale cached answers may be incorrect.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →No. Exact-key LRU simulation. TTL starts on insertion and does not refresh on hits. Expired entries are removed before each request. Assumes stable outputs within TTL. A real cache key should include tenant, model/version, prompt and generation settings; this lab uses letters.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Inspect hit/miss trace, eviction order, cost and mean latency. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Open cache-4, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Cross-tenant key reuse can expose private data; stale cached answers may be incorrect. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Unique keys provide no exact-match cache savings.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →hit
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →{"keys":"A,B,C,D,E,F,G,H","capacity":3,"ttl":60,"interval":1,"cost":0.01,"missMs":1000,"hitMs":10}
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →{"Hit rate":0,"Misses":8,"Cost USD":0.08,"Mean latency (ms)":1000}
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Keep the supplied fixture and other settings fixed; change capacity. Inspect hit/miss trace, eviction order, cost and mean latency.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Cross-tenant key reuse can expose private data; stale cached answers may be incorrect.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →No. Exact-key LRU simulation. TTL starts on insertion and does not refresh on hits. Expired entries are removed before each request. Assumes stable outputs within TTL. A real cache key should include tenant, model/version, prompt and generation settings; this lab uses letters.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Inspect hit/miss trace, eviction order, cost and mean latency. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Open cache-5, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →Cross-tenant key reuse can expose private data; stale cached answers may be incorrect. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.
Original teaching answer / local derivation. Method reference: Redis · eviction and TTL policies ↗
打开实验 →