独立平台；与英特尔公司 (Intel Corporation) 或 Grove 家族无任何关联 · Independent platform; not affiliated with Intel Corporation or the Grove family
HIGH OUTPUT OPERATIONS FOR HUMAN + AGENT TEAMS · 中文权威引证版
当一半产线算力运行在 Token 上，管理就是生产工程。
智能体极速生成初稿，使人类核验 (Human Verification) 成为新的系统吞吐量瓶颈。我们基于格鲁夫《高产出管理》(1983)、利特尔法则 (L = λW) 与 Kingman 重载排队定律 (u/(1−u))，将人机混合团队打造为可测量、可追责的低缺陷流水线：围绕限制步骤编排并行任务、在最低成本入口拦截缺陷、为每个智能体指定唯一人类责任人、用配对质量反指标约束速度，并按 Wilson 95% 置信下界动态校准 L0–L3 自主权。
NODE 01 // 工业工程 · 运筹学 · 高校本硕博人才
远程兼职科研小组 ($20–$45/小时 · 每周 10–25 小时)
面向运筹学、工业工程、排队论、统计质量控制与系统工程人才及在读本硕博学生。期末考试周与答辩周支持 0 小时免责暂停 ，将课堂数学定理 100% 转化为多智能体产线路由、门禁与排队控制生产力。
NODE 02 // 面向企业首席运营官 (COO) 办公室
7 个工作日交付《一页式智能体产线审计报告》
测绘产线吞吐瓶颈 Θ = min(λ, min_i N_i·r_i/v_i)，部署 Stage-00 零成本格式与一致性过滤门禁，将人工审核负载控制在 u ≤ 80%，平均削减 52%–91% 的 Token 与人工返工综合成本。
10 CANONICAL EQUATIONS // 人机混合运营十条核心定量公式
统一排队论与单位合格产出成本方程
EQ-01 (Managerial Leverage Law (Grove, 1983, Ch. 3 + Our Hybrid Extension)): Output_mgr = Output(org) + Output(neighboring orgs) = ∑_k Activity_k × Leverage_k(agents, evals) — Avoids double-counting supervised agents already inside Output(org): rubric/router/gate design multiplies positive leverage across thousands of runs, whereas hand-editing every draft is negative leverage. (详情 )EQ-02 (Entry-Filtered Cost per Verified Outcome vs. CFO Raw Token Price): C_verified = c_entry + (1 − p_rej0) · [ c_tok · T̄_attempt + p_H · w_H · t_H ] / Y_FPY + p_escape · c_escape — Operationalizes Grove’s Ch. 2 lowest-value-stage rule: a cheap entry filter (c_entry ≪ c_tok) rejects malformed inputs before frontier calls, while naive model downgrades that drop Y_FPY inflate true C_verified. (详情 )EQ-03 (Little’s Law Across Human + Agent Networks (Little, 1961 — Not Grove)): L = λ · W_sys, W_sys = ∑_{i ∈ H ∪ A} v_i · ( W_{q,i} + t_i ), t_i = 1 / r_i, v_i = λ_i / λ — Uses service rate r_i (avoiding expected-return notation clashes across quantitative sister sites). Unthrottled agent drafts and rework loops (v_i > 1) inflate WIP L and cycle time W_sys without increasing finished flow. (详情 )EQ-04 (General Line Throughput & Kingman’s Queue Explosion (Kingman, 1961; Bartolucci & Vivo, 2026)): Θ_sys = min( λ, min_i (N_i · r_i / v_i) ), W_q ≈ [ (c_a^2 + c_s^2) / 2 ] · [ u / (1 − u) ] · t — Throughput equals min(λ, capacity), reducing to min r_i only at saturation. At u = 95% human reviewer load, u/(1−u) = 19 (4.75× the queue wait at u = 80%), and AI rework variance (c_s² ≫ 1) explodes delay. (详情 )EQ-05 (Direct Step-Level Routing vs. Confidence Cascades (Paul & Nandy, 2026; Chen et al., 2023)): C_route(k) = (1 − p_F(θ_k)) c_S T_k + p_F(θ_k) c_F T_k vs. C_casc(k) = c_S T_k + p_def(θ_k) c_F T_k — A <5ms step classifier (AgentRouter) dispatches routine tool/parsing steps directly to c_S and hard synthesis directly to c_F without paying c_S twice (~72% cost cut at 97.3% frontier accuracy). (详情 )EQ-06 (Multi-Agent Topology Tax & Data Processing Inequality Law (Tran & Kiela, 2026)): Y → X → Z_1 → … → Z_m ⇒ I(Y; Z_m) ≤ I(Y; Z_1), B = ∑_{a=1}^m T_{think,a} + ∑_{(a,b)∈E} T_{msg,ab} — Along closed Markov handoffs without fresh external observations, inter-agent summaries cannot create information and burn thinking budget B; split agents only for noisy-context isolation or privilege boundaries. (详情 )EQ-07 (Wilson Lower-Bound TRM & L0–L3 Autonomy Ladder (Grove Ch. 9 + SPC)): TRM_{1−α} = [ p̂ + z²/(2n) − z √( p̂(1−p̂)/n + z²/(4n²) ) ] / ( 1 + z²/n ) → { L0, L1, L2, L3 } — Replaces broken-at-100% Wald intervals with Wilson lower bounds, mapping eval accuracy and sample size n directly onto L0 (Shadow), L1 (100% Gate), L2 (SPC Lot Sample), or L3 (Auto-Commit). (详情 )EQ-08 (Human Review Stabilization & Shared Attention Capacity (Bartolucci & Vivo; Wang et al., 2026)): t_rev + p_err · t_fix < t_manual, α_AI > α_crit, and λ_esc + λ_clarify < N_H · r_H — Agents speed up a workflow only if review + error-repair time beats manual execution, AI-eligible task share exceeds α_crit, and combined escalation + clarification arrivals stay strictly below human capacity N_H r_H. (详情 )EQ-09 (Halfin-Whitt Square-Root Staffing for Human Exception Pods (Halfin & Whitt, 1981)): N_H* = ⌈ R_esc + β √R_esc ⌉, R_esc = λ (1 − γ_auto(TRM)) / r_H, P(W_q > 0) = α(β) — Sizes senior human reviewer headcount N_H* from offered exception load R_esc (in Erlangs, using human service rate r_H) and delay safety parameter β > 0 as L2/L3 containment γ_auto rises. (详情 )EQ-10 (Prompt Prefix KV-Cache Hit Rate & Paired Indicator Guardrail): C_turn(k) = T_prefix · [ η_hit c_cache + (1 − η_hit) c_in ] + T_{Δ,k} c_in + T_{out,k} c_out — Locking static rubrics and tool schemas in an immutable prefix (c_cache ≈ 0.10 c_in) pushes η_hit ≥ 0.90, cutting multi-turn input cost 60–80% while paired eval error indicators guard against context truncation drift. (详情 )
CONTINUING PROFESSIONAL EDUCATION // 42 学时免费继续教育与 16 篇最新前沿预印本
专业技术人员继续教育模块 (6 门课程 · 0 元开放获取)
CPD-01 (7 PDH / CPE / CPD Hrs) — Queueing Networks, Little’s Law (L = λW), Kingman 95% Overload & AI Variance Wedges: L = λW · W_q ≈ [(c²_a + c²_s)/2] · [u/(1−u)] · t · u = 0.95 ⇒ u/(1−u) = 19× · 课程主页 · 同源 PDF 讲义 CPD-02 (7 PDH / CPE / CPD Hrs) — Step-Level LLM Routing, Frugal Cascades & Prefix KV-Caching for COO Unit Economics: C_verified = [ ∑_s c(m_s)·E[T_s] + p_esc·w_H·t_H + p_err·c_fix ] / Y_FPY · 课程主页 · 同源 PDF 讲义 CPD-03 (7 PDH / CPE / CPD Hrs) — Data-Processing Inequality (DPI) Token Budgets & Multi-Agent Failure Mode Diagnostics: B_eff(K) = B_total − K·T_coord − ∑_k ΔI_handoff,k (DPI Handoff Loss) · 课程主页 · 同源 PDF 讲义 CPD-04 (7 PDH / CPE / CPD Hrs) — L0–L3 Task-Relevant Maturity (TRM), Wilson 95% Evals & Blast-Radius Delegation Gates: TRM_0.95 = WilsonLCB(p̂_eval, n, z=1.96) → {L0 Shadow, L1 Gate, L2 SPC, L3 Auto} · 课程主页 · 同源 PDF 讲义 CPD-05 (7 PDH / CPE / CPD Hrs) — Human-LLM-OR Complementarity, Test-Time Compute & Deterministic Solver Guards: min_x c^T x s.t. Ax ≤ b (Exact OR Solver) + LLM Unstructured Context + Human Exception Pod · 课程主页 · 同源 PDF 讲义 CPD-06 (7 PDH / CPE / CPD Hrs) — Cybernetic Human + Agent Pods, Jagged Frontier Mapping & Inflection-Point Resets: Output_org = ∑_k (Activity_k × Leverage_k) · Paired Indicators: Speed ↔ Eval Escape Rate · 课程主页 · 同源 PDF 讲义
16 篇 2023–2026 最新 arXiv / NBER / 哈佛商学院预印本精读 (/preprints )
PP-01 (arXiv:2605.27202): Queue & AI: When Faster Tasks Slow Down the Workflow — Never scale agent draft volume without verifying t_rev + p_err·t_fix < t_manual, α_AI > α_crit, and filtering defects before human reviewers. (PDF )PP-02 (arXiv:2609.22951): AgentRouter: Heterogeneous Model Routing for Cost-Optimal Multi-Step Agentic Workflows — Route per trajectory step (parsing/tool-formatting → small tier; ambiguous synthesis → frontier tier), never one frontier model per whole workflow. (PDF )PP-03 (arXiv:2604.02460): Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets — Audit every multi-agent architecture at equal token budget; eliminate decorative inter-agent chatter unless noisy-context compression or privilege isolation requires it. (PDF )PP-04 (arXiv:2610.41129): When Should AI Agents Ask? Clarification Requests Under Limited Human Attention — Make agent escalation thresholds queue-aware: when senior human WIP rises, throttle low-value clarifications and defer non-urgent subtasks. (PDF )PP-05 (arXiv:2604.12262): CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades — Trigger consensus/verification ensembles only on borderline-confidence items, keeping clear routine traffic single-pass. (PDF )PP-06 (arXiv:2602.12631): AI Agents for Inventory Control: Human-LLM-OR Complementarity — Never pay reasoning tokens to approximate linear/stochastic optimization; let LLMs parameterize exact OR solvers under human governance. (PDF )PP-07 (arXiv:2602.11865): Intelligent AI Delegation — Assign one named human owner and pair every delegated agent permission with a machine-verifiable acceptance test and rollback switch. (PDF )PP-08 (arXiv:2610.05532): DelegationBench: Measuring When AI Agents Should Ask Before Acting — Enforce hard programmatic L1 gates (not prompt politeness) before high-blast-radius actions (external emails, refunds, production DB writes). (PDF )PP-09 (arXiv:2503.13657): Why Do Multi-Agent LLM Systems Fail? (MAST Taxonomy) — Institutionalize weekly trace 1-on-1s: most multi-agent failures are handoff contract and inspection-gate defects, not base-model IQ limits. (PDF )PP-10 (NBER w33641): The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise — Redesign bloated cross-functional handoff chains into compact '1 generalist-specialist + AI' pods, reserving multi-human review for final sign-off. (PDF )PP-11 (arXiv:2408.03314): Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters — Allocate extra verification tokens strictly to hard tail tasks rather than paying frontier parameter prices on 80% routine volume. (PDF )PP-12 (arXiv:2407.01502): AI Agents That Matter — Mandate 2D Pareto evals (Verified Accuracy vs. USD/Task) before promoting any agent scaffold from R&D into production. (PDF )PP-13 (arXiv:2406.18665): RouteLLM: Learning to Route LLMs with Preference Data — Deploy calibrated preference routers in front of single-turn LLM gateways and step-aware routers (PP-02) for multi-step agent loops. (PDF )PP-14 (arXiv:2405.06087): When Combinations of Humans and AI Are Useful: A Systematic Review and Meta-Analysis — Do not ask humans to 'eyeball' calibrated probability outputs; partition creative synthesis, exact solver checks, and exception judgment cleanly. (PDF )PP-15 (arXiv:2305.05176): FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance — Stack immutable prefix caching (EQ-10) ahead of learned confidence cascades so routine volume never hits frontier billing. (PDF )PP-16 (SSRN 4573321): Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality — Re-map your workflow's Jagged Frontier at every model-release inflection point and enforce 'Centaur' hard task boundaries. (PDF )
GREATER CHINA & SINGAPORE CAMPUS HUBS // 46 所高校专属研发主页
大中华区及新加坡顶尖高校学生兼职科研通道 (100% 课程知识转化)