id	title	description	availability	condition	price	link	image_link	brand	custom_label_0	custom_label_1	custom_label_2
CPD-01	CPD-01 (GRV-CPD-401): Queueing Networks, Little’s Law (L = λW), Kingman 95% Overload & AI Variance Wedges [7 PDH / CPE / CPD Hrs]	Quantify why speeding up Routine tasks without filtering bimodal AI service-time variance creates a Kingman queue explosion at human review stations, and compute congestion-dependent escalation thresholds. Built on fresh 2023-2026 preprints (https://andygrove.systems/preprints)	in stock	new	0.00 USD	https://andygrove.systems/course/cpd-01	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Continuing Education	Continuing Professional Development (PDH/CPE/CPD)	7 PDH / CPE / CPD Hrs	https://andygrove.systems/preprints
CPD-02	CPD-02 (GRV-CPD-402): Step-Level LLM Routing, Frugal Cascades & Prefix KV-Caching for COO Unit Economics [7 PDH / CPE / CPD Hrs]	Replace naive CFO flat token-price cuts with trajectory step-level routing, immutable prefix KV-caching, and confidence-calibrated cascades that cut inference bills 50–85% without collapsing First-Pass Yield (Y_FPY). Built on fresh 2023-2026 preprints (https://andygrove.systems/preprints)	in stock	new	0.00 USD	https://andygrove.systems/course/cpd-02	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Continuing Education	Continuing Professional Development (PDH/CPE/CPD)	7 PDH / CPE / CPD Hrs	https://andygrove.systems/preprints
CPD-03	CPD-03 (GRV-CPD-403): Data-Processing Inequality (DPI) Token Budgets & Multi-Agent Failure Mode Diagnostics [7 PDH / CPE / CPD Hrs]	Audit 14 MAST multi-agent failure modes across 1,600+ traces, eliminate redundant sequential agent chatter under equal token budgets, and enforce typed schema handoffs at Stage 00. Built on fresh 2023-2026 preprints (https://andygrove.systems/preprints)	in stock	new	0.00 USD	https://andygrove.systems/course/cpd-03	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Continuing Education	Continuing Professional Development (PDH/CPE/CPD)	7 PDH / CPE / CPD Hrs	https://andygrove.systems/preprints
CPD-04	CPD-04 (GRV-CPD-404): L0–L3 Task-Relevant Maturity (TRM), Wilson 95% Evals & Blast-Radius Delegation Gates [7 PDH / CPE / CPD Hrs]	Operationalize Grove’s Chapter 9 Task-Relevant Maturity via finite-sample Wilson 95% lower confidence bounds, SPC acceptance sampling, single human Owner accountability, and hard L1 irreversible action gates. Built on fresh 2023-2026 preprints (https://andygrove.systems/preprints)	in stock	new	0.00 USD	https://andygrove.systems/course/cpd-04	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Continuing Education	Continuing Professional Development (PDH/CPE/CPD)	7 PDH / CPE / CPD Hrs	https://andygrove.systems/preprints
CPD-05	CPD-05 (GRV-CPD-405): Human-LLM-OR Complementarity, Test-Time Compute & Deterministic Solver Guards [7 PDH / CPE / CPD Hrs]	Partition unstructured regime translation (LLMs), exact constrained optimization (MIP/LP solvers), and strategic exception sign-off (humans) while allocating test-time verifier compute strictly to tail difficulty. Built on fresh 2023-2026 preprints (https://andygrove.systems/preprints)	in stock	new	0.00 USD	https://andygrove.systems/course/cpd-05	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Continuing Education	Continuing Professional Development (PDH/CPE/CPD)	7 PDH / CPE / CPD Hrs	https://andygrove.systems/preprints
CPD-06	CPD-06 (GRV-CPD-406): Cybernetic Human + Agent Pods, Jagged Frontier Mapping & Inflection-Point Resets [7 PDH / CPE / CPD Hrs]	Re-architect functional silos into AI-equipped Cybernetic Pods, map Jagged Frontier failure cliffs at every model-release Strategic Inflection Point (Grove, 1996), and run weekly Trace 1-on-1s. Built on fresh 2023-2026 preprints (https://andygrove.systems/preprints)	in stock	new	0.00 USD	https://andygrove.systems/course/cpd-06	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Continuing Education	Continuing Professional Development (PDH/CPE/CPD)	7 PDH / CPE / CPD Hrs	https://andygrove.systems/preprints
PP-01	Fresh Preprint PP-01 (arXiv:2605.27202): Queue & AI: When Faster Tasks Slow Down the Workflow	Silvia Bartolucci, Pierpaolo Vivo (UCL & King's College London) (May 2026 · UCL / King's College London) - Proves how fast AI draft generation creates a 'spike-and-tail' service distribution ('variance wedge'): escaped errors return as heavy rework while congested human reviewers rationally lower scrutiny precisely when risk peaks. Upskilling Rule: 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.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-01	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2605.27202	https://andygrove.systems/preprints
PP-02	Fresh Preprint PP-02 (arXiv:2609.22951): AgentRouter: Heterogeneous Model Routing for Cost-Optimal Multi-Step Agentic Workflows	Rudrendu Kumar Paul, Sourav Nandy (Sep 2026 · ICML AgenticUQ) - Trains a <5ms 12M-parameter trajectory router across 50,000 steps (GAIA, SWE-bench, WebArena, ToolBench) and 4 model tiers, cutting inference cost by ~72% while retaining 97.3% of frontier-only performance (vs. 31% for single-turn RouteLLM). Upskilling Rule: Route per trajectory step (parsing/tool-formatting → small tier; ambiguous synthesis → frontier tier), never one frontier model per whole workflow.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-02	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2609.22951	https://andygrove.systems/preprints
PP-03	Fresh Preprint PP-03 (arXiv:2604.02460): Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets	Dat Tran, Douwe Kiela (Stanford University) (Apr 2026 · Stanford) - Uses the Data Processing Inequality (DPI) to prove that under equal thinking-token budgets, multi-agent message passing loses mutual information and trails single-agent reasoning unless context rot on noisy long inputs degrades single-agent focus. Upskilling Rule: Audit every multi-agent architecture at equal token budget; eliminate decorative inter-agent chatter unless noisy-context compression or privilege isolation requires it.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-03	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2604.02460	https://andygrove.systems/preprints
PP-04	Fresh Preprint PP-04 (arXiv:2610.41129): When Should AI Agents Ask? Clarification Requests Under Limited Human Attention	Hanzhao Wang, Shengjie Liu, Zhengchao Wang (Oct 2026) - Models parallel AI agents sharing one human supervisor with finite attention as a queueing dynamic program; introduces Pairwise Coordination Policy (PCP) so agents condition escalation on real-time human queue congestion. Upskilling Rule: Make agent escalation thresholds queue-aware: when senior human WIP rises, throttle low-value clarifications and defer non-urgent subtasks.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-04	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2610.41129	https://andygrove.systems/preprints
PP-05	Fresh Preprint PP-05 (arXiv:2604.12262): CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades	Raeyoung Chang, Dongwook Kwon, Jisoo Lee, Nikhil Verma (Apr 2026) - Injects lightweight multi-agent deliberation strictly at uncertain escalation boundaries of a cost-aware LLM cascade, resolving borderline ambiguity on small models before paying frontier-tier or human escalation costs. Upskilling Rule: Trigger consensus/verification ensembles only on borderline-confidence items, keeping clear routine traffic single-pass.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-05	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2604.12262	https://andygrove.systems/preprints
PP-06	Fresh Preprint PP-06 (arXiv:2602.12631): AI Agents for Inventory Control: Human-LLM-OR Complementarity	Jackie Baek, Yaopeng Fu, Will Ma, Tianyi Peng (Feb 2026 · Columbia) - Evaluates multi-period operational control on InventoryBench, proving that pairing LLMs (unstructured context translation) + exact Operations Research solvers (constrained stochastic math) + human oversight beats standalone LLMs or pure OR. Upskilling Rule: Never pay reasoning tokens to approximate linear/stochastic optimization; let LLMs parameterize exact OR solvers under human governance.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-06	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2602.12631	https://andygrove.systems/preprints
PP-07	Fresh Preprint PP-07 (arXiv:2602.11865): Intelligent AI Delegation	Nenad Tomašev, Matija Franklin, Simon Osindero (Google DeepMind) (Feb 2026 · Google DeepMind) - Establishes a formal operational framework for multi-hop human-to-agent and agent-to-agent delegation covering dynamic authority bounds, contract specification, verification checkpoints, and accountability chains. Upskilling Rule: Assign one named human owner and pair every delegated agent permission with a machine-verifiable acceptance test and rollback switch.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-07	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2602.11865	https://andygrove.systems/preprints
PP-08	Fresh Preprint PP-08 (arXiv:2610.05532): DelegationBench: Measuring When AI Agents Should Ask Before Acting	Shiva Pochampally (Oct 2026) - Tests 10 models across 156 matched-pair irreversible vs. reversible scenarios: surface prompt rephrasing swings act-now rates by up to 52.5 percentage points, and live tool-executing agents check in far less than static evaluators. Upskilling Rule: Enforce hard programmatic L1 gates (not prompt politeness) before high-blast-radius actions (external emails, refunds, production DB writes).	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-08	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2610.05532	https://andygrove.systems/preprints
PP-09	Fresh Preprint PP-09 (arXiv:2503.13657): Why Do Multi-Agent LLM Systems Fail? (MAST Taxonomy)	Mert Cemri, Melissa Z. Pan, Shuyi Yang, Lakshya A. Agrawal, Matei Zaharia, Ion Stoica et al. (Mar 2025 · UC Berkeley) - Analyzes 1,600+ execution traces across 7 multi-agent frameworks, classifying 14 failure modes across specification flaws, inter-agent misalignment, and absent task verification. Upskilling Rule: Institutionalize weekly trace 1-on-1s: most multi-agent failures are handoff contract and inspection-gate defects, not base-model IQ limits.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-09	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2503.13657	https://andygrove.systems/preprints
PP-10	Fresh Preprint PP-10 (NBER w33641): The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise	Fabrizio Dell’Acqua, Charles Ayoubi, Raffaella Sadun, Ethan Mollick, Karim R. Lakhani et al. (Mar 2025 · Harvard / Wharton) - Pre-registered field experiment with 776 P&G professionals: 1 human + AI matched the performance of 2-person cross-functional human teams while erasing R&D vs. commercial functional silos. Upskilling Rule: Redesign bloated cross-functional handoff chains into compact '1 generalist-specialist + AI' pods, reserving multi-human review for final sign-off.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-10	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	NBER w33641	https://andygrove.systems/preprints
PP-11	Fresh Preprint PP-11 (arXiv:2408.03314): Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters	Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar (Aug 2024 · UC Berkeley / DeepMind) - Shows that allocating inference compute adaptively by prompt difficulty via process verifiers lets smaller models beat 14× larger models at substantially lower total FLOPs. Upskilling Rule: Allocate extra verification tokens strictly to hard tail tasks rather than paying frontier parameter prices on 80% routine volume.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-11	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2408.03314	https://andygrove.systems/preprints
PP-12	Fresh Preprint PP-12 (arXiv:2407.01502): AI Agents That Matter	Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, Nitya Nadgir, Arvind Narayanan (Jul 2024 · Princeton) - Demonstrates that SOTA agent scaffolds often burn 10×–50× higher API cost for negligible accuracy gains and fail on holdout robustness when evaluated without joint cost-accuracy Pareto curves. Upskilling Rule: Mandate 2D Pareto evals (Verified Accuracy vs. USD/Task) before promoting any agent scaffold from R&D into production.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-12	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2407.01502	https://andygrove.systems/preprints
PP-13	Fresh Preprint PP-13 (arXiv:2406.18665): RouteLLM: Learning to Route LLMs with Preference Data	Isaac Ong, Amjad Almahairi, Wei-Lin Chiang, Joseph E. Gonzalez, Ion Stoica et al. (Jun 2024 · UC Berkeley LMSYS) - Trains lightweight query routers on human preference data to dynamically select between strong and weak LLMs, cutting single-turn frontier call costs by 50–85% without degrading response quality. Upskilling Rule: Deploy calibrated preference routers in front of single-turn LLM gateways and step-aware routers (PP-02) for multi-step agent loops.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-13	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2406.18665	https://andygrove.systems/preprints
PP-14	Fresh Preprint PP-14 (arXiv:2405.06087): When Combinations of Humans and AI Are Useful: A Systematic Review and Meta-Analysis	Michelle Vaccaro, Abdullah Almaatouq, Thomas W. Malone (MIT) (May 2024 · MIT CCI (Nature Hum. Behav.)) - Meta-analysis of 106 experimental studies (370 effect sizes): naive human+AI pairing underperforms the best solo actor on pure prediction/decision tasks, but achieves true synergy in modular creation + structured verification workflows. Upskilling Rule: Do not ask humans to 'eyeball' calibrated probability outputs; partition creative synthesis, exact solver checks, and exception judgment cleanly.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-14	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2405.06087	https://andygrove.systems/preprints
PP-15	Fresh Preprint PP-15 (arXiv:2305.05176): FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance	Lingjiao Chen, Matei Zaharia, James Zou (Stanford University) (May 2023 · Stanford (TMLR)) - Introduces the foundational three-lever inference cost stack — prompt compaction, semantic/prefix approximation caching, and learned LLM cascades — matching GPT-4 accuracy with up to 98% cost reduction. Upskilling Rule: Stack immutable prefix caching (EQ-10) ahead of learned confidence cascades so routine volume never hits frontier billing.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-15	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	arXiv:2305.05176	https://andygrove.systems/preprints
PP-16	Fresh Preprint PP-16 (SSRN 4573321): Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality	Fabrizio Dell’Acqua, Edward McFowland III, Ethan R. Mollick, Karim R. Lakhani et al. (758 BCG Consultants) (Sep 2023 · HBS / Wharton / MIT) - Inside the capability frontier, AI lifted output +12.2%, speed +25.1%, and quality +40%; on deceptive tasks just outside the frontier, AI users dropped −19 percentage points below unassisted humans due to unverified reliance. Upskilling Rule: Re-map your workflow's Jagged Frontier at every model-release inflection point and enforce 'Centaur' hard task boundaries.	in stock	new	0.00 USD	https://andygrove.systems/preprint/pp-16	https://andygrove.systems/og-card.png	ANDYGROVE.SYSTEMS Preprint Reading Room	Fresh 2023-2026 Preprint for Professional Upskilling	SSRN 4573321	https://andygrove.systems/preprints