> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sleuthintel.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Sleuth - The Simulation Receipts

> Original Sleuth document.

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var(--border);overflow-x:auto;white-space:nowrap;}\n.navbar-inner{max-width:1060px;margin:0 auto;padding:0 24px;display:flex;gap:2px;}\n.navlink{color:var(--green-dim);text-decoration:none;font-size:0.72rem;letter-spacing:1.2px;\n  padding:15px 12px;border-bottom:2px solid transparent;transition:color .15s;}\n.navlink:hover{color:var(--green);}\n@media (prefers-reduced-motion: reduce){html{scroll-behavior:auto;}}\n</style>\n</head>\n<body>\n\n<canvas id=\"rain\"></canvas>\n\n<div class=\"navbar\"><div class=\"navbar-inner\">\n  <a class=\"navlink\" href=\"#readme\">00 \u00b7 HOW TO READ</a>\n  <a class=\"navlink\" href=\"#sim01\">01 \u00b7 MECHANICS</a>\n  <a class=\"navlink\" href=\"#sim02\">02 \u00b7 PRICE PATH</a>\n  <a class=\"navlink\" href=\"#sim03\">03 \u00b7 60-MO ECONOMY</a>\n  <a class=\"navlink\" href=\"#sim04\">04 \u00b7 COST CURVE</a>\n  <a class=\"navlink\" href=\"#sim05\">05 \u00b7 AGENT DEMAND</a>\n  <a class=\"navlink\" href=\"#sim06\">06 \u00b7 DETECTION RACE</a>\n  <a class=\"navlink\" href=\"#ledger\">CLAIMS LEDGER</a>\n</div></div>\n\n\n\n<div class=\"wrap\">\n\n<!-- ================= HERO ================= -->\n<div class=\"hero\">\n  <div class=\"vtag\">SLEUTH \u00b7 THE SIMULATION RECEIPTS \u00b7 JULY 2026</div>\n  <h1>SIX SIMULATIONS. ONE ECONOMY, ATTACKED FROM EVERY ANGLE.</h1>\n  <p class=\"lead\">The whitepaper makes claims. This document shows the numbers behind them. Six independent models, one shared parameter set matching the current tokenomics, each asking a question a sharp reader would ask if they had the time to build the model themselves: can the wage economy be cheated, what does the token do under real demand, what does this intelligence cost to produce, and how fast does a swarm catch something bad happening in the wild. None of them is a forecast. All six are arguments, run in the open, with the assumptions stated where you can see them.</p>\n</div>\n\n<!-- ================= 00 HOW TO READ ================= -->\n<section class=\"section\" id=\"readme\">\n  <div class=\"eyebrow\">// 00 \u00b7 HOW TO READ THIS DOCUMENT</div>\n  <h2>What a Simulation Is, and Is Not</h2>\n\n  <p>Every result in this document comes from one of two kinds of model, and knowing how they work is the difference between reading numbers and understanding them.</p>\n\n  <h3>Agent-Based Modeling: Let the Cheaters Play</h3>\n  <p>Instead of writing equations about averages, an agent-based model creates hundreds of individual accounts inside a program, gives each one a strategy, and lets them play the actual platform rules month after month. A grinder submits twenty cheap tags. A colluding trio inflates each other's grades whenever the random draw seats two of them on the same panel. A plant-and-flag pair runs its scam exactly as a real attacker would. Nothing is assumed to fail. The cheats are handed every advantage the rules allow, and the model simply counts who ends up with the money. When honest work wins in a model like this, it wins because the plumbing beat the exploit, not because the modeler decided it should.</p>\n\n  <h3>Monte Carlo: One Run Is a Story, a Thousand Runs Is a Shape</h3>\n  <p>The models contain randomness on purpose: which verifiers get drawn, what grades land, how demand wobbles month to month. Run the model once and you get one possible history, which might be lucky or unlucky. A Monte Carlo simulation runs the same model many times, 25 runs per configuration here, each from a different random seed, then reports the average across all of them. The name comes from the casino, and the idea is the same one a casino relies on: any single night is noise, a thousand nights is the truth of the odds. What you read below is never one run. It is the shape that survives when the luck is averaged out.</p>\n\n  <h3>Sensitivity Sweeps: Turn the Dial on the Thing You Doubt</h3>\n  <p>Every model rests on assumptions, and the honest response is to attack your own. A sensitivity sweep takes the parameter the result depends on most, the detection rate, the insider sell-through, the size of a verifier pool, and turns that dial across its entire plausible range, rerunning the full Monte Carlo at every setting. If the conclusion flips somewhere along the dial, then the conclusion depends on a guess, and this document says so where it happens. If the conclusion holds at every setting including the worst one, the result is structural: it comes from the rules, not from the parameters. The strongest claims below, honest work winning at a detection rate of zero, are the ones that survived their sweeps.</p>\n\n  <h3>What None of This Can Do</h3>\n  <p>No model here contains market microstructure, macro regimes, or attacker creativity beyond what an adversarial review surfaced. Price paths are flow-versus-depth approximations, not order books. Serving costs and adoption curves are stated assumptions until beta data replaces them, and every model reruns as real numbers arrive. Read every dollar figure as the output of its stated assumptions, never as a promise. A simulation is an argument. The point of publishing the argument is that you can check it.</p>\n\n  <div class=\"statrow\">\n    <div class=\"stat\"><div class=\"n\">6</div><div class=\"l\">INDEPENDENT MODELS</div></div>\n    <div class=\"stat\"><div class=\"n\">25</div><div class=\"l\">MONTE CARLO RUNS PER CELL</div></div>\n    <div class=\"stat\"><div class=\"n\">2,700+</div><div class=\"l\">SIMULATED MONTHS</div></div>\n    <div class=\"stat\"><div class=\"n\">$0.05</div><div class=\"l\">TGE PRICE \u00b7 ALL MODELS</div></div>\n  </div>\n\n  <div class=\"card gold\">\n    <h4>THE SHARED PARAMETER SET</h4>\n    <p style=\"margin:0\">All six models run the current tokenomics: 100M fixed supply, $0.05 listing at a $5M FDV, 23.25% of supply deployed as liquidity against the $250K community raise, an 8.5% true day-one holder float, the community bonus at month 1, Team, Dev Fund, and Marketing vesting linearly months 2 to 25, and an epoch ceiling of 500K tokens a month whose dollar value rides the token. Wage mechanics come straight from the whitepaper: the $10 to $250 grade curve, the $5 verifier fee under the 15% spend cap, the $50 bad-pass and wrong-reject slashes, the 10/25/100 slash grades with bounties sourced from the offender's own unvested wages, one month cliff and three tranches on every wage, 0.5% daily decay behind the 30-day grace, and the $1,000 licence shared by hunting and verifying.</p>\n  </div>\n<div class=\"summary\"><div class=\"st\">SECTION SUMMARY</div><p style=\"margin:0\">Two model types power everything below. Agent-based models create real cheaters inside a program and let them play the actual rules; Monte Carlo runs every model 25 times and averages out the luck; sensitivity sweeps attack the assumptions the results depend on most. All six models share one parameter set matching the current tokenomics, and every dollar figure is the output of stated assumptions, never a forecast.</p><p class=\"tldr\"><b>TL;DR</b>Simulations here are arguments you can check, not predictions. The strongest results are the ones that survived having their own assumptions attacked.</p></div>\n</section>\n\n<hr class=\"divider\">\n\n<!-- ================= SIM 01 ================= -->\n<section class=\"section\" id=\"sim01\">\n  <div class=\"eyebrow\">// SIM 01 \u00b7 ADVERSARIAL MECHANICS \u00b7 36 MONTHS</div>\n  <h2>Can the Wage Economy Be Cheated?</h2>\n  <p class=\"lead\">Eight competing strategies, honest and hostile, play the written rules for 36 monthly epochs. The question: does honest work out-earn every cheat, and does that depend on the police?</p>\n\n  <p>Before this sim ran, an adversarial review tried to break the mechanics on paper and found six holes. Every one was fixed in the ruleset first: the decay bar that ignores junk tags, the $50 outvoted wrong-reject slash, the 24-hour tier grace, the senior seed in thin pools. The sim runs the fixed rules. Detection is a parameter, not an assumption: each month, each active cheater is caught with some probability and Grade 1'd, full burn, unvested forfeiture, ban including linked accounts. Sweeping that dial from 0% to 40% measures how much honesty depends on enforcement versus how much is structural.</p>\n\n  <h3>The Populations</h3>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Strategy</th><th>Plays</th></tr>\n    <tr><td>Grinder</td><td>Honest volume: 20 low-grade tags a month</td></tr>\n    <tr><td>Quality hunter</td><td>Honest quality: 3 high-grade finds a month</td></tr>\n    <tr><td>Honest verifier</td><td>Contributes, reviews 100 items a month, grades straight</td></tr>\n    <tr><td>Lazy rejector</td><td>Same review load, rejects everything borderline to dodge bad-pass risk</td></tr>\n    <tr><td>Decay farmer</td><td>Builds a $50K balance early, then one $10 junk tag a month to sit immortal</td></tr>\n    <tr><td>Plant-and-flag pair</td><td>One account plants mediocre intel, a linked account flags it and farms the bounty</td></tr>\n    <tr><td>Colluding trio</td><td>Three verifiers in a thin niche inflate each other's grades when drawn together</td></tr>\n    <tr><td>Clean whale</td><td>Builds the same $50K early, stays genuinely active. The control.</td></tr>\n  </table></div>\n\n  <h3>The Payoff Table \u00b7 Vested Dollars Per Head Over 36 Months</h3>\n  <div class=\"svgfig\">\n    <div class=\"figtitle\">VESTED $ PER HEAD \u00b7 36 MONTHS \u00b7 BASE DEMAND PATH \u00b7 25 MC RUNS</div>\n    <svg id=\"payoffchart\" viewBox=\"0 0 900 360\" width=\"100%\" xmlns=\"http://www.w3.org/2000/svg\" role=\"img\" aria-label=\"Bar chart of vested dollars per head for eight strategies over 36 months.\"></svg>\n    <div class=\"cap\">Green bars survive. Dim bars are the cheats. The two shortest bars belong to the two strategies that ended the run banned.</div>\n  </div>\n\n  <div class=\"tablewrap\"><table>\n    <tr><th>Strategy</th><th>Vested $</th><th>aSLEUTH at End</th><th>Banned</th><th>Slashed $</th></tr>\n    <tr class=\"win\"><td>Honest verifier</td><td>$21,302</td><td>$7,657</td><td>0%</td><td>$639</td></tr>\n    <tr class=\"win\"><td>Quality hunter</td><td>$16,095</td><td>$19,022</td><td>0%</td><td>$0</td></tr>\n    <tr class=\"win\"><td>Grinder</td><td>$10,157</td><td>$12,001</td><td>0%</td><td>$0</td></tr>\n    <tr class=\"win\"><td>Clean whale</td><td>$9,028</td><td>$52,934</td><td>0%</td><td>$0</td></tr>\n    <tr class=\"lose\"><td>Lazy rejector</td><td>$8,889</td><td>$47</td><td>0%</td><td>$8,242</td></tr>\n    <tr class=\"win\"><td>Decay farmer</td><td>$6,580</td><td>$468</td><td>0%</td><td>$0</td></tr>\n    <tr class=\"lose\"><td>Colluding trio</td><td>$303</td><td>$0</td><td>100%</td><td>$132</td></tr>\n    <tr class=\"lose\"><td>Plant-and-flag</td><td>$21</td><td>$0</td><td>100%</td><td>$21</td></tr>\n  </table></div>\n  <p class=\"src\">Dollar figures are outputs of the modeled workloads above under the base demand path. They compare strategies at fixed effort; they are not earnings caps or forecasts.</p>\n\n  <h3>Sensitivity \u00b7 Catch Rate vs Plant-and-Flag</h3>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Monthly Catch Rate</th><th>Plant-and-Flag $</th><th>Honest Grinder $</th><th>Verdict</th></tr>\n    <tr><td>0%</td><td>$4,932</td><td>$10,156</td><td>Cheat loses</td></tr>\n    <tr><td>5%</td><td>$597</td><td>$10,151</td><td>Cheat loses</td></tr>\n    <tr><td>10%</td><td>$64</td><td>$10,154</td><td>Cheat loses</td></tr>\n    <tr><td>25%</td><td>$5</td><td>$10,156</td><td>Cheat loses</td></tr>\n    <tr><td>40%</td><td>$1</td><td>$10,159</td><td>Cheat loses</td></tr>\n  </table></div>\n  <p>The row that matters: <strong>at a 0% catch rate, with no enforcement of any kind, the exploit still earns half of honest grinding.</strong> The bounty is sourced from the planter's own unvested wages, so the pair shuffles its own money while producing worse work than an honest account.</p>\n\n  <h3>Sensitivity \u00b7 Verifier Pool Size vs Collusion</h3>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Niche Pool Size</th><th>Colluder $</th><th>Honest Verifier $</th><th>Verdict</th></tr>\n    <tr><td>9 verifiers</td><td>$303</td><td>$21,302</td><td>Collusion loses</td></tr>\n    <tr><td>15 verifiers</td><td>$237</td><td>$21,301</td><td>Collusion loses</td></tr>\n    <tr><td>30 verifiers</td><td>$218</td><td>$21,299</td><td>Collusion loses</td></tr>\n  </table></div>\n\n  <p>Two results worth reading closely. The lazy rejector runs the same review volume as the honest verifier and earns less than half, $8,889 against $21,302, because the outvoted wrong-reject slash eats $8,242 of the difference: refusing to judge is not a safe harbor. And the decay farmer keeps every vested dollar, $6,580, while the $50K record rots to $468, taking every licence, tier, and swarm right with it. You keep what you earned. You lose what you stopped earning.</p>\n\n  <div class=\"callout\">\n    <div class=\"ct\">THE HEADLINE</div>\n    <p style=\"margin:0\">Honest work beats every cheating strategy at every detection rate tested, including zero. The economics are not honest because the police are good. They are honest because the plumbing gives cheaters nothing to steal: bounties come out of the cheater's own pocket, wages sit unvested and forfeitable for months, and the record that gates every right decays the moment real work stops.</p>\n  </div>\n<div class=\"summary\"><div class=\"st\">SECTION SUMMARY</div><p style=\"margin:0\">Eight strategies played the written rules for 36 months. Every honest strategy out-earned every cheat, the two malicious strategies ended 100% banned with near-zero take, and the result held with detection switched off entirely: plant-and-flag earned half of honest grinding at a 0% catch rate because the bounty comes from the cheater's own unvested wages. Lazy verifying earned less than half of honest verifying, collusion lost at every pool size, and decay took the farmer's standing while leaving every vested dollar untouched.</p><p class=\"tldr\"><b>TL;DR</b>Cheating loses money on this platform even if nobody is watching. That is plumbing, not policing.</p></div>\n</section>\n\n<hr class=\"divider\">\n\n<!-- ================= SIM 02 ================= -->\n<section class=\"section\" id=\"sim02\">\n  <div class=\"eyebrow\">// SIM 02 \u00b7 PRICE PATH \u00b7 TGE TO MONTH 60</div>\n  <h2>What Does the Token Do Under Real Demand?</h2>\n  <p class=\"lead\">Full demand-side model: subscriber tier buying, agent-wave API revenue driving buybacks, speculation on the day-one float, wage sells, and the entire unlock calendar. Three demand scenarios, 25 runs each.</p>\n\n<div class=\"svgfig\">\n  <div class=\"figtitle\">SIMULATED SLEUTH PRICE \u00b7 $ \u00b7 MONTHS 0\u201360 \u00b7 UNLOCK WINDOW SHADED (MO 1\u201325)</div>\n  <div class=\"legend\">\n    <span class=\"l-bull\"><span class=\"sw\"></span>BULL \u00b7 peak $2.72 \u00b7 ends $2.62 (52x)</span>\n    <span class=\"l-base\"><span class=\"sw\"></span>BASE \u00b7 peak $1.44 \u00b7 ends $1.32 (26x)</span>\n    <span class=\"l-bear\"><span class=\"sw\"></span>BEAR \u00b7 peak $0.52 \u00b7 ends $0.49 (9.8x)</span>\n  </div>\n  <svg id=\"pachart\" viewBox=\"0 0 900 420\" width=\"100%\" xmlns=\"http://www.w3.org/2000/svg\" role=\"img\" aria-label=\"Line chart of simulated SLEUTH price over 60 months under three demand scenarios.\"></svg>\n  <div class=\"cap\">There is no cliff and no single unlock event on this calendar. Releases drip from month one, the community bonus at month 1 and the linear tranches through month 25, and demand absorbs them as they land: the worst peak-to-trough drawdown anywhere in the unlock era is 20 to 22%. The climb steepens once the last calendar tranche clears at month 25, and the bear path grinds along listing, briefly under it, before recovering.</div>\n</div>\n\n<h3>The Numbers Behind the Lines</h3>\n<div class=\"tablewrap\"><table>\n  <tr><th>Scenario</th><th>Price Mo 12</th><th>Worst Unlock-Era Drawdown</th><th>Price Mo 60</th><th>Multiple</th><th>Implied FDV Mo 60</th></tr>\n  <tr><td>Bull</td><td>$0.81 \u00b7 16.3x</td><td>22%</td><td>$2.62</td><td>52x</td><td>$262M</td></tr>\n  <tr><td>Base</td><td>$0.40 \u00b7 8.1x</td><td>22%</td><td>$1.32</td><td>26x</td><td>$132M</td></tr>\n  <tr><td>Bear</td><td>$0.08 \u00b7 1.6x</td><td>20%</td><td>$0.49</td><td>9.8x</td><td>$49M</td></tr>\n</table></div>\n<p>Read the multiples with the FDV column open: they are large because the entry is small, a $5M fully diluted valuation at listing, while the end-state platform values, $49M to $262M, are ordinary outcomes for a working intelligence platform. The model is arguing that the platform can be worth that much if the demand shows up, not that a chart owes anyone 26x.</p>\n\n<div class=\"callout\">\n  <div class=\"ct\">HOW TO READ THIS</div>\n  <p style=\"margin:0\">There is no cliff to build a run-up and no unlock event to dig a dip: releases drip from month one and demand absorbs them as they land. What insider sell-through decides is the ceiling of the climb, not the depth of a hole: at 20% sell-through month 60 lands at $1.67, at 80% it is $0.95, still 19x listing. The bear path dips briefly below the $0.05 listing in the first year and ends near 10x above it.</p>\n</div>\n<div class=\"summary\"><div class=\"st\">SECTION SUMMARY</div><p style=\"margin:0\">Under full demand-side modeling, all three scenarios end well above the $0.05 listing: bear near 10x, base 26x, bull 52x, with implied end-state FDVs of $49M to $262M, ordinary valuations for a working platform reached from a small entry. Because the unlocks drip from month one instead of cliffing, the worst drawdown anywhere in the unlock era is 20 to 22%, and insider sell-through decides how high the climb goes rather than how deep a hole gets dug.</p><p class=\"tldr\"><b>TL;DR</b>No cliff, no unlock crater. Every path tested ends multiples above listing, and the multiples are big because the entry is $0.05, not because the model is generous.</p></div>\n</section>\n\n<hr class=\"divider\">\n\n<!-- ================= SIM 03 ================= -->\n<section class=\"section\" id=\"sim03\">\n  <div class=\"eyebrow\">// SIM 03 \u00b7 TOKEN ECONOMY \u00b7 60 MONTHS</div>\n  <h2>Does the Whole Economy Hold Together for Five Years?</h2>\n  <p class=\"lead\">The swarm sim and the price model, wired together and run as one economy: wages priced at each month's simulated token price, the epoch ceiling riding the token, agent contributors joining with the wave. Six scenario cells, 25 runs each.</p>\n\n  <div class=\"statrow\">\n    <div class=\"stat\"><div class=\"n\">60</div><div class=\"l\">MONTHLY EPOCHS</div></div>\n    <div class=\"stat\"><div class=\"n\">~600</div><div class=\"l\">AGENTS \u00b7 10 STRATEGIES</div></div>\n    <div class=\"stat\"><div class=\"n\">6</div><div class=\"l\">SCENARIO CELLS</div></div>\n    <div class=\"stat\"><div class=\"n\">25</div><div class=\"l\">MC RUNS PER CELL</div></div>\n  </div>\n\n  <h3>The Scenario Grid</h3>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Demand Path</th><th>Humans at Plateau</th><th>Agent Wave</th><th>Speculative Interest</th></tr>\n    <tr><td>Bull</td><td>~7,000 (World B)</td><td>Month 9, ceiling ~12,000</td><td>High</td></tr>\n    <tr><td>Base</td><td>~4,000</td><td>Month 10, ceiling ~6,000</td><td>Average-high</td></tr>\n    <tr><td>Bear</td><td>~1,800 (near World A)</td><td>Late, month 18, ceiling ~2,500</td><td>Muted</td></tr>\n  </table></div>\n\n  <h3>Results \u00b7 The Price Path, All Six Cells</h3>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Scenario</th><th>Hold Share</th><th>Price Mo 12</th><th>Unlock Drawdown</th><th>Price Mo 36</th><th>Price Mo 60</th><th>Revenue / Mo at 60</th><th>Reserve $ at 60</th></tr>\n    <tr class=\"win\"><td>Bull</td><td>70/30</td><td>$0.81 \u00b7 16.3x</td><td>22%</td><td>$1.99</td><td>$2.62 \u00b7 52x</td><td>$674K</td><td>$10.3M</td></tr>\n    <tr class=\"win\"><td>Bull</td><td>45/55</td><td>$0.80 \u00b7 16.0x</td><td>21%</td><td>$2.09</td><td>$2.69 \u00b7 54x</td><td>$806K</td><td>$12.6M</td></tr>\n    <tr class=\"win\"><td>Base</td><td>70/30</td><td>$0.40 \u00b7 8.1x</td><td>22%</td><td>$1.07</td><td>$1.32 \u00b7 26x</td><td>$281K</td><td>$4.1M</td></tr>\n    <tr class=\"win\"><td>Base</td><td>45/55</td><td>$0.35 \u00b7 7.0x</td><td>22%</td><td>$0.94</td><td>$1.17 \u00b7 23x</td><td>$358K</td><td>$5.4M</td></tr>\n    <tr><td>Bear</td><td>70/30</td><td>$0.08 \u00b7 1.6x</td><td>20%</td><td>$0.40</td><td>$0.49 \u00b7 9.8x</td><td>$95K</td><td>$1.2M</td></tr>\n    <tr><td>Bear</td><td>45/55</td><td>$0.07 \u00b7 1.3x</td><td>7%</td><td>$0.32</td><td>$0.40 \u00b7 8.1x</td><td>$129K</td><td>$1.7M</td></tr>\n  </table></div>\n  <p>A steady climb with the unlocks absorbed as they drip, no dip event anywhere on the calendar. Nothing tested breaks the economy: even the bear path, after grinding along listing and briefly under it in the first year, ends near 10x at month 60. 70/30 versus 45/55 hold share moves the month 60 price by roughly 10 to 15% only, a built-in hedge, fewer holders means more cash payers means bigger buybacks.</p>\n\n  <h3>The Number That Decides the Chart</h3>\n  <p class=\"lead\">Not the buyback. Not the hold share. Insider sell-through on the calendar unlock.</p>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Calendar Sell-Through</th><th>Unlock Drawdown</th><th>Price Mo 60</th><th>Multiple</th></tr>\n    <tr class=\"win\"><td>20%</td><td>20%</td><td>$1.67</td><td>33x</td></tr>\n    <tr><td>40% (default)</td><td>22%</td><td>$1.32</td><td>26x</td></tr>\n    <tr><td>60%</td><td>24%</td><td>$1.08</td><td>22x</td></tr>\n    <tr class=\"lose\"><td>80%</td><td>25%</td><td>$0.95</td><td>19x</td></tr>\n  </table></div>\n  <div class=\"callout\">\n    <div class=\"ct\">THE HEADLINE</div>\n    <p style=\"margin:0\">With the unlocks dripping instead of cliffing, sell-through no longer digs a hole, it lowers the ceiling of the climb: the drawdown barely moves between 20% and 80% sell-through, while the month 60 price runs from $1.67 down to $0.95, still 19x listing at the worst. Demand decides the slope, sell-through decides the altitude, both are visible in advance, and the economy survives every combination tested.</p>\n  </div>\n\n  <h3>The Agent Grinder Finding</h3>\n  <p>The top honest earner on sold-at-vest wages is not human. Agent grinders, contributing mid-grade intel at machine volume, out-earn every human strategy at $76.8K per head over the run. Same rules, same wages, machine speed compounds. One consequence worth naming: agents at volume dominate the epoch bill, so the token-denominated ceiling trims more often, and pro-rata trim hits humans and agents alike. The protocol is species-blind and the trim is working as designed.</p>\n\n  <h3>Honesty Under Every Price Path</h3>\n  <p>The sim 01 result had to be re-proven, because a rising token changes the cheat math: tokens stolen cheap and held appreciate like anyone else's.</p>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Cell</th><th>Plant &amp; Flag</th><th>Colluder</th><th>Grinder</th><th>Quality</th><th>Verdict</th></tr>\n    <tr><td>Bull \u00b7 70/30</td><td>$715</td><td>$4,929</td><td>$46,304</td><td>$73,415</td><td class=\"win\">HOLDS</td></tr>\n    <tr><td>Base \u00b7 70/30</td><td>$358</td><td>$3,066</td><td>$36,443</td><td>$57,766</td><td class=\"win\">HOLDS</td></tr>\n    <tr><td>Bear \u00b7 70/30</td><td>$131</td><td>$1,176</td><td>$23,195</td><td>$36,752</td><td class=\"win\">HOLDS</td></tr>\n  </table></div>\n  <p><strong>Honesty holds at zero detection, on the bull path, at 60 months.</strong> Bounties sourced from the cheater's own unvested wages, months of forfeitable vesting, and decay gating every right. Price appreciation lifts every boat and changes no rankings.</p>\n<div class=\"summary\"><div class=\"st\">SECTION SUMMARY</div><p style=\"margin:0\">Wired together as one economy for five years, nothing tested breaks: all six scenario cells end above listing, heavy insider selling at 80% sell-through still lands month 60 at 19x, and hold-share moves the outcome only 10 to 15% because fewer holders means bigger buybacks. The top honest earner is a machine, agent grinders at $76.8K a head, and the honesty result re-proves at zero detection even on the bull path where stolen tokens appreciate hardest.</p><p class=\"tldr\"><b>TL;DR</b>The economy survives every combination thrown at it for 60 months, the best honest wage on the platform belongs to an agent, and cheating still loses under every price path.</p></div>\n</section>\n\n<hr class=\"divider\">\n\n<!-- ================= SIM 04 ================= -->\n<section class=\"section\" id=\"sim04\">\n  <div class=\"eyebrow\">// SIM 04 \u00b7 COST CURVE \u00b7 SWARM VS ANALYST DESK</div>\n  <h2>What Does This Intelligence Actually Cost to Produce?</h2>\n  <p class=\"lead\">Swarm cost per verified item, run straight through the wage mechanics, against a fully loaded professional analyst desk at three salary tiers, matched by what the item is, not what it costs.</p>\n\n  <p>Three quality bands. <strong>Routine</strong>: codified tags and machine-checkable facts, priced against a junior analyst doing labeling. <strong>Standard</strong>: attributions and cluster links, priced against a mid-level analyst. <strong>Deep</strong>: full investigations and network maps, priced against a senior analyst, with desk throughput anchored to a live TRM Labs job posting expecting attribution within 24 hours and finished production within 72. Desk cost runs fully loaded: base comp, 1.35 to 1.45x loading for benefits and payroll tax, $20K per analyst per year in tooling, one team lead per eight analysts, a mild coordination drag past ten analysts, and a learning curve deliberately granted in the desk's favor.</p>\n\n  <div class=\"card\">\n    <h4>DESK COMP, SOURCED</h4>\n    <p style=\"margin:0\" class=\"src\">TRM Labs Blockchain Intelligence Analyst: $102K\u2013$170K range, ~$131K average (Glassdoor, Apr 2026). Chainalysis intelligence analyst: $203K single reported salary (Indeed); company-wide average ~$113K (Salary.com, Dec 2025). Generic crypto investigator: ~$75K average, $52K\u2013$98K range (ZipRecruiter, May 2026). Three desk tiers modeled: lean ($75K base), market ($110K), premium ($150K), spanning that full range.</p>\n  </div>\n\n  <h3>Quality-Matched Cost Per Item \u00b7 Market-Tier Desk</h3>\n  <div class=\"svgfig\">\n    <div class=\"figtitle\">COST PER VERIFIED ITEM \u00b7 SWARM VS DESK \u00b7 THREE QUALITY BANDS</div>\n    <svg id=\"costchart\" viewBox=\"0 0 900 320\" width=\"100%\" xmlns=\"http://www.w3.org/2000/svg\" role=\"img\" aria-label=\"Grouped bar chart comparing swarm and desk cost per item across three quality bands.\"></svg>\n    <div class=\"cap\">Green bars are the swarm, gold bars are the desk. Same item, same quality bar, produced two different ways.</div>\n  </div>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Band</th><th>Swarm $/item</th><th>Desk $/item</th><th>Multiple</th></tr>\n    <tr class=\"win\"><td>Routine</td><td>$15.50</td><td>$144.25</td><td>9.3x</td></tr>\n    <tr class=\"win\"><td>Standard</td><td>$87.96</td><td>$571.15</td><td>6.5x</td></tr>\n    <tr class=\"win\"><td>Deep</td><td>$219.08</td><td>$2,109.49</td><td>9.6x</td></tr>\n  </table></div>\n  <p>At matched quality, the structural result is the swarm produces verified intelligence at <strong>6.5x to 9.6x cheaper</strong> than a market-rate desk, and that holds against the lean tier too.</p>\n\n  <h3>Blended Cost, Full Volume Mix, As the Platform Scales</h3>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Volume/mo</th><th>Swarm $/item</th><th>Desk (market) $/item</th><th>Multiple</th><th>Desk heads needed</th></tr>\n    <tr><td>500</td><td>$82.27</td><td>$657.51</td><td>8.0x</td><td>22</td></tr>\n    <tr><td>2,500</td><td>$57.07</td><td>$574.50</td><td>10.1x</td><td>104</td></tr>\n    <tr><td>10,000</td><td>$14.33</td><td>$505.85</td><td>35.3x</td><td>387</td></tr>\n    <tr><td>50,000</td><td>$2.88</td><td>$429.07</td><td>148.7x</td><td>1,752</td></tr>\n    <tr class=\"win\"><td>100,000</td><td>$1.45</td><td>$396.92</td><td>274.2x</td><td>3,337</td></tr>\n  </table></div>\n\n  <div class=\"callout red\">\n    <div class=\"ct\">READ THIS HONESTLY</div>\n    <p style=\"margin:0\">The multiples widen fast past ~2,500 items/month, but a real share of that widening is the epoch ceiling capping the monthly bill, which is a deliberate cost control, not free efficiency. The scale rows price the ceiling at its mature value, 500K tokens once the base path crosses $0.30; at listing the ceiling is far tighter and the trim harsher still. The structural, ceiling-independent result is the 6.5x to 9.6x at matched quality shown above. The scale multiples are real and worth showing, they reflect an actual protocol mechanic, but they should be read as cost per item under the ceiling, not as pure efficiency gain.</p>\n  </div>\n\n  <p>One number that needs no caveat: serving 100,000 items a month the desk way takes an estimated <strong>3,337 analysts and team leads</strong>. Chainalysis, the category leader, runs under 1,000 people total.</p>\n<div class=\"summary\"><div class=\"st\">SECTION SUMMARY</div><p style=\"margin:0\">At matched quality the swarm produces verified intelligence 6.5x to 9.6x cheaper than a market-rate analyst desk, a structural result independent of any protocol cap. The eye-watering multiples at scale are real but partly the epoch ceiling doing deliberate cost control, and the document says so rather than selling trimmed wages as efficiency. Serving 100K items a month the desk way needs an estimated 3,337 analysts, more than three times the category leader's entire headcount.</p><p class=\"tldr\"><b>TL;DR</b>Same intelligence, roughly an order of magnitude cheaper, and the honest caveat about the ceiling is printed right next to the big numbers.</p></div>\n</section>\n\n<hr class=\"divider\">\n\n<!-- ================= SIM 05 ================= -->\n<section class=\"section\" id=\"sim05\">\n  <div class=\"eyebrow\">// SIM 05 \u00b7 AGENT DEMAND \u00b7 60 MONTHS</div>\n  <h2>What Happens When the Customers Are Machines?</h2>\n  <p class=\"lead\">Human adoption held at the flywheel model's assumptions, then an agent customer wave layered on top: its own adoption curve, its own spend ramp, and a serving cost a fraction of a human's.</p>\n\n  <p>Human mechanics are unchanged: logistic adoption, $70 blended subscription ARPU, 45% cash-pay share, $8/user/month API demand pool, platform keeps 50% of marketplace spend, 25% of revenue buys back SLEUTH monthly into the Reserve. Agent accounts follow their own logistic wave, scenario axis <strong>late / base / early</strong>, since agent timing is the single largest unknown in every Sleuth sim to date. Agent spend starts at $15/month per account and grows as workflows deepen, capped at $75/month. The structural driver: <strong>$0.40/month serving cost per agent account versus $6.00/month per human</strong>, because agents are pure API consumers with no onboarding or support surface. Both numbers are stated assumptions, not measurements.</p>\n\n  <h3>Results</h3>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Human</th><th>Agents</th><th>Revenue/mo @60</th><th>vs human-only</th><th>Agent % rev @60</th><th>Margin @60</th><th>Reserve @60</th><th>vs human-only</th></tr>\n    <tr><td>Slow</td><td>Late</td><td>$156,953</td><td>1.46x</td><td>32%</td><td>88%</td><td>$1.18M</td><td>1.21x</td></tr>\n    <tr class=\"win\"><td>Base</td><td>Base</td><td>$419,418</td><td>1.95x</td><td>49%</td><td>91%</td><td>$3.53M</td><td>1.49x</td></tr>\n    <tr class=\"win\"><td>Base</td><td>Early</td><td>$899,326</td><td>4.19x</td><td>76%</td><td>95%</td><td>$6.94M</td><td>2.93x</td></tr>\n    <tr class=\"win\"><td>Slow</td><td>Early</td><td>$792,248</td><td>7.38x</td><td>86%</td><td>97%</td><td>$5.54M</td><td>5.69x</td></tr>\n    <tr><td>Fast</td><td>Late</td><td>$478,031</td><td>1.11x</td><td>10%</td><td>85%</td><td>$5.59M</td><td>1.04x</td></tr>\n  </table></div>\n  <p>Every cell tested is at or above the human-only counterfactual. There is no scenario where agent demand hurts Sleuth: the worst case, fast human adoption with a late agent wave, is still 1.04x to 1.11x baseline, because serving an agent costs a fraction of serving a human. The base/base cell, the least aggressive assumption on the grid, still nearly doubles revenue and lifts gross margin from 84% to 91% by month 60.</p>\n\n  <div class=\"callout\">\n    <div class=\"ct\">THE HEADLINE</div>\n    <p style=\"margin:0\">In the base scenario, agents cross 49% of platform revenue by month 60. In the early-wave scenarios, agents become the majority of the business, 76 to 86% of revenue, while margin climbs into the mid-90s. This isn't a new business line bolted on, it's the same API serving a second customer type at near-zero marginal cost, which is exactly the kind of leverage a marketplace is supposed to have.</p>\n  </div>\n\n  <p class=\"src\">Limits, stated plainly: the agent wave's timing and size is the parameter these results are most sensitive to, and the $0.40 versus $6.00 serving cost gap is the whole engine behind the margin story. Demand side only; agent contributors on the supply side live in sims 01 and 03, deliberately not double-counted here.</p>\n<div class=\"summary\"><div class=\"st\">SECTION SUMMARY</div><p style=\"margin:0\">Layering an agent customer wave on top of human adoption improves every cell tested, with the worst case still 1.04x the human-only baseline, because serving an agent costs a modeled $0.40 a month against $6.00 for a human. In the base case agents reach 49% of revenue by month 60 and nearly double it; in early-wave scenarios they become the majority of the business while gross margin climbs into the mid-90s.</p><p class=\"tldr\"><b>TL;DR</b>Machine customers are pure upside in every scenario: same API, second customer type, near-zero marginal cost.</p></div>\n</section>\n\n<hr class=\"divider\">\n\n<!-- ================= SIM 06 ================= -->\n<section class=\"section\" id=\"sim06\">\n  <div class=\"eyebrow\">// SIM 06 \u00b7 DETECTION RACE \u00b7 HOURS TO VERIFIED FLAG</div>\n  <h2>How Fast Does a Swarm Catch Something Bad?</h2>\n  <p class=\"lead\">A synthetic event race: something bad starts happening on-chain, and three detection lanes compete to flag it first. The swarm of licensed hunters, an incumbent's automated scanner, and organic public discovery.</p>\n\n  <p>One real-world anchor point, stated as such: the Trust Wallet Chrome extension compromise, Christmas Day 2025. The malicious version was live roughly five hours before independent victim reports coalesced into a public community alert from on-chain investigator ZachXBT. That's organic discovery's ground truth for a genuinely novel attack pattern with no existing signature. Separately, automated monitoring is documented catching known-pattern DeFi exploits in real time, same block, when the pattern is already in the signature set. Three event types set the difficulty: <strong>known pattern</strong> (the incumbent's home turf), <strong>novel pattern</strong> (a new tactic, the incumbent is blind until a human writes a rule), and <strong>slow bleed</strong> (a gradual insider dump with no single triggering transaction, hardest for everyone). Swarm detection scales with the number of active licensed hunters watching, each with an independent per-hour chance of spotting the signal; first correct flag wins, same rule as head hunting on the Tree.</p>\n\n  <h3>Results \u00b7 Median Hours to Verified Flag</h3>\n  <div class=\"svgfig\">\n    <div class=\"figtitle\">MEDIAN HOURS TO VERIFIED FLAG \u00b7 NOVEL PATTERN &amp; SLOW BLEED \u00b7 LOWER IS BETTER</div>\n    <svg id=\"detchart\" viewBox=\"0 0 900 330\" width=\"100%\" xmlns=\"http://www.w3.org/2000/svg\" role=\"img\" aria-label=\"Bar chart of median detection hours by lane for novel pattern and slow bleed events.\"></svg>\n    <div class=\"cap\">The incumbent bars carry their catch rate: they only reach the finish line at all in a minority of novel and slow-bleed races. The swarm's bar barely registers, about one hour, at every platform maturity tested.</div>\n  </div>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Event Type</th><th>Lane</th><th>Median Hours</th><th>Catch Rate</th></tr>\n    <tr class=\"win\"><td rowspan=\"3\">Known pattern</td><td>Swarm</td><td>1.0</td><td>100%</td></tr>\n    <tr><td>Incumbent scan</td><td>1.0</td><td>100%</td></tr>\n    <tr><td>Organic</td><td>5\u20137</td><td>100%</td></tr>\n    <tr class=\"win\"><td rowspan=\"3\">Novel pattern</td><td>Swarm</td><td>1.0</td><td>100%</td></tr>\n    <tr class=\"lose\"><td>Incumbent scan</td><td>93\u2013112</td><td>29\u201334%</td></tr>\n    <tr><td>Organic</td><td>16\u201319</td><td>100%</td></tr>\n    <tr class=\"win\"><td rowspan=\"3\">Slow bleed</td><td>Swarm</td><td>1\u20132</td><td>100%</td></tr>\n    <tr class=\"lose\"><td>Incumbent scan</td><td>84\u2013139</td><td>12\u201316%</td></tr>\n    <tr><td>Organic</td><td>49\u201388</td><td>71\u201383%</td></tr>\n  </table></div>\n  <p>Ranges span platform maturity, 40 hunters at beta up to 900 at a mature swarm. Speed barely changes across that range, the swarm hits novel and slow-bleed events in about an hour at every maturity level, because it only takes one hunter out of hundreds getting lucky.</p>\n\n  <div class=\"callout\">\n    <div class=\"ct\">THE HEADLINE</div>\n    <p style=\"margin:0\">On novel-pattern threats, the case incumbent scanners structurally miss, the swarm beats organic public discovery by <strong>16x to 19x</strong>, median 1 hour versus 16 to 19 hours. The incumbent scanner is excellent on its home turf, near-instant with a 100% catch on known patterns, but it catches only <strong>29 to 34% of novel patterns at all</strong>, and takes 90-plus hours when it does. That gap between fast on what it already knows and blind to what it doesn't is the exact gap swarm parallelism fills, and it gets no smaller as the incumbent adds headcount, because the limit is signature coverage, not staffing.</p>\n  </div>\n\n  <p class=\"src\">Limits, stated plainly: a synthetic event simulation calibrated to one well-documented real incident, not a study of many. The organic baseline generalizes reasonably for novel, viral-attention events, less reliably for slow bleeds, where no comparable public case timeline existed to calibrate against. Hunter participation rates and per-hunter detection probability are stated assumptions, tested against real data once beta numbers exist.</p>\n<div class=\"summary\"><div class=\"st\">SECTION SUMMARY</div><p style=\"margin:0\">In a three-lane race calibrated to a real Christmas Day 2025 incident, the swarm flags novel-pattern threats in about an hour at every maturity level, 16x to 19x faster than organic public discovery, while incumbent scanners catch only 29 to 34% of novel patterns at all and take 90-plus hours when they do. Signature scanners are unbeatable on known patterns and structurally blind past them, and adding headcount doesn't close that gap; parallel eyes do.</p><p class=\"tldr\"><b>TL;DR</b>Hundreds of independent hunters beat one scanner's signature list the moment the attack is new, and new is where the damage lives.</p></div>\n</section>\n\n<hr class=\"divider\">\n\n<!-- ================= CLAIMS LEDGER ================= -->\n<section class=\"section\" id=\"ledger\">\n  <div class=\"eyebrow\">// THE CLAIMS LEDGER</div>\n  <h2>Every Whitepaper Claim, and the Sim That Backs It</h2>\n  <p class=\"lead\">The whitepaper makes the claim. This table shows where the number came from.</p>\n  <div class=\"tablewrap\"><table>\n    <tr><th>The Claim</th><th>Backed By</th><th>The Number</th></tr>\n    <tr><td>Honest work out-earns every cheat, even with zero enforcement</td><td>Sim 01 + Sim 03</td><td>Plant-and-flag nets $4,932 vs $10,156 honest at a 0% catch rate; holds at 60 months on the bull path</td></tr>\n    <tr><td>Bounties cost the platform nothing and cheaters fund their own downfall</td><td>Sim 01</td><td>Bounty sourced from the offender's unvested wages; the exploit shuffles its own money</td></tr>\n    <tr><td>Refusing to judge is not a safe harbor for verifiers</td><td>Sim 01</td><td>Lazy rejection earns $8,889 vs $21,302 honest, with $8,242 eaten in wrong-reject slashes</td></tr>\n    <tr><td>Decay takes the standing but never the wage</td><td>Sim 01</td><td>The farmer keeps $6,580 vested; the $50K record rots to $468</td></tr>\n    <tr><td>The epoch ceiling binds early, then never again</td><td>Sim 01</td><td>Nine binding months on the base path, worst trim 10 cents on the dollar in month one, clean thereafter</td></tr>\n    <tr><td>Collusion in thin niches does not pay</td><td>Sim 01</td><td>Colluder $303 vs honest verifier $21,302, at every pool size tested</td></tr>\n    <tr><td>The unlock calendar is absorbable under every demand path</td><td>Sim 02 + Sim 03</td><td>Worst unlock-era drawdown 20 to 22% across all six cells; no cliff event exists</td></tr>\n    <tr><td>The economy survives even heavy insider selling</td><td>Sim 03</td><td>80% calendar sell-through still ends month 60 at $0.95, 19x listing</td></tr>\n    <tr><td>The swarm produces intelligence far cheaper than a professional desk</td><td>Sim 04</td><td>6.5x to 9.6x cheaper at matched quality, ceiling-independent</td></tr>\n    <tr><td>Agents as customers are pure upside to the business</td><td>Sim 05</td><td>Every cell at or above the human-only baseline; base case doubles revenue by month 60</td></tr>\n    <tr><td>Swarm parallelism catches what signature scanners miss</td><td>Sim 06</td><td>Novel patterns flagged in ~1 hour vs 16 to 19 hours organic; incumbent catches 29 to 34% at all</td></tr>\n  </table></div>\n\n  <div class=\"callout\">\n    <div class=\"ct\">THE STANDING OFFER</div>\n    <p style=\"margin:0\">Every model reruns as real data arrives. Beta replaces assumed parameters with measured ones, and if a measured number breaks a result above, the result changes and so does the whitepaper. The design raced itself before it raced anyone else. That's the whole point of this document.</p>\n  </div>\n</section>\n\n<section class=\"section\" id=\"overall\">\n  <div class=\"eyebrow\">// THE WHOLE DOCUMENT, COMPRESSED</div>\n  <h2>Overall Summary</h2>\n  <p>Six models, one parameter set, every result reachable from the stated assumptions. The wage economy cannot be profitably cheated, and that conclusion survives the removal of all enforcement, because the plumbing sources every bounty from the cheater's own unvested wages and decays every right the moment real work stops. The token survives five simulated years under bull, base, and bear demand, with no cliff on the calendar, unlocks absorbed as they drip, and even 80% insider sell-through ending 19x above the $0.05 listing. The product economics stack the same way: the swarm produces matched-quality intelligence roughly an order of magnitude cheaper than a professional desk, machine customers improve every revenue scenario they touch, and parallel hunters catch novel threats in about an hour where signature scanners are structurally blind. Every model carries its limits in plain text, and every model reruns as beta data replaces assumptions with measurements.</p>\n  <div class=\"summary\">\n    <div class=\"st\">OVERALL TL;DR</div>\n    <p style=\"margin:0\"><strong>The design raced itself before it raced anyone else.</strong> Honest work wins with zero police. The token holds under every demand path tested. The intelligence costs a fraction of the old way, machines make the business better on both sides of the market, and the swarm sees new threats first. Not a forecast, an argument, and the whole argument is on this page.</p>\n  </div>\n</section>\n\n<div class=\"footer\">SLEUTH \u00b7 THE SIMULATION RECEIPTS \u00b7 SIX MODELS \u00b7 ONE PARAMETER SET \u00b7 JULY 2026</div>\n\n</div>\n\n<script>\n(function(){\n  var canvas=document.getElementById('rain');\n  if(canvas){\n    var ctx=canvas.getContext('2d');\n    function sizeCanvas(){canvas.width=window.innerWidth;canvas.height=window.innerHeight;}\n    sizeCanvas();\n    var chars='01\\u30a2\\u30a4\\u30a6\\u30a8\\u30aa\\u30ab\\u30ad\\u30af\\u30b1\\u30b3\\u30b5\\u30b7\\u30b9\\u30bb\\u30bd\\u30bf\\u30c1\\u30c4\\u30c6\\u30c8\\u30ca\\u30cb\\u30cc\\u30cd\\u30ce$SLEUTH//><';\n    var fontSize=14, drops;\n    function initDrops(){drops=Array(Math.floor(canvas.width/fontSize)).fill(1);}\n    initDrops();\n    window.addEventListener('resize',function(){sizeCanvas();initDrops();});\n    function drawRain(){\n      ctx.fillStyle='rgba(6,6,6,0.05)';\n      ctx.fillRect(0,0,canvas.width,canvas.height);\n      ctx.font=fontSize+'px Share Tech Mono,monospace';\n      for(var i=0;i<drops.length;i++){\n        var ch=chars[Math.floor(Math.random()*chars.length)];\n        ctx.fillStyle='#00FF66';\n        ctx.fillText(ch,i*fontSize,drops[i]*fontSize);\n        if(drops[i]*fontSize>canvas.height&&Math.random()>0.975)drops[i]=0;\n        drops[i]++;\n      }\n    }\n    var motionOK=!window.matchMedia('(prefers-reduced-motion: reduce)').matches;\n    if(motionOK)setInterval(drawRain,60);\n  }\n\n  var NS='http://www.w3.org/2000/svg';\n  function mk(svg,tag,attrs,txt){var e=document.createElementNS(NS,tag);for(var k in attrs)e.setAttribute(k,attrs[k]);if(txt!==undefined)e.textContent=txt;svg.appendChild(e);return e;}\n  var MONO=\"Share Tech Mono,monospace\";\n\n  /* ---------- SIM 02 price chart ---------- */\n  (function(){\n    var svg=document.getElementById('pachart'); if(!svg)return;\n    var bull=[0.05,0.068,0.089,0.119,0.159,0.206,0.256,0.305,0.343,0.463,0.613,0.671,0.814,0.906,1.011,1.164,1.29,1.3,1.395,1.54,1.536,1.588,1.567,1.613,1.654,1.626,1.712,1.791,1.888,1.883,1.896,1.888,1.875,1.986,1.947,1.953,1.988,2.08,2.058,2.081,2.137,2.08,2.141,2.197,2.243,2.25,2.219,2.253,2.254,2.299,2.294,2.332,2.343,2.353,2.409,2.399,2.466,2.487,2.539,2.577,2.621];\n    var base=[0.05,0.061,0.068,0.08,0.094,0.109,0.127,0.153,0.179,0.215,0.291,0.373,0.403,0.473,0.514,0.573,0.649,0.67,0.704,0.76,0.782,0.844,0.84,0.857,0.832,0.834,0.908,0.937,0.999,0.97,0.985,0.988,1.028,1.055,1.048,1.032,1.071,1.065,1.083,1.09,1.121,1.131,1.126,1.134,1.149,1.146,1.208,1.188,1.211,1.203,1.227,1.261,1.248,1.268,1.255,1.273,1.254,1.29,1.275,1.318,1.319];\n    var bear=[0.05,0.049,0.048,0.048,0.049,0.05,0.052,0.055,0.058,0.062,0.066,0.074,0.082,0.09,0.101,0.116,0.127,0.145,0.194,0.226,0.227,0.229,0.245,0.261,0.289,0.294,0.321,0.322,0.334,0.345,0.349,0.352,0.356,0.365,0.382,0.389,0.399,0.394,0.393,0.404,0.4,0.407,0.407,0.421,0.429,0.439,0.445,0.437,0.439,0.441,0.45,0.445,0.453,0.461,0.452,0.46,0.462,0.475,0.477,0.484,0.492];\n    var W=900,H=420,L=64,R=20,T=18,B=44,pw=W-L-R,ph=H-T-B,ymax=3.0,xmax=60;\n    function X(m){return L+pw*m/xmax;} function Y(v){return T+ph*(1-v/ymax);}\n    mk(svg,'rect',{x:X(1),y:T,width:X(25)-X(1),height:ph,fill:'rgba(255,215,0,0.05)'});\n    mk(svg,'text',{x:(X(1)+X(25))/2,y:T+16,'text-anchor':'middle',fill:'#8a7a1e','font-size':'11','font-family':MONO,'letter-spacing':'2'},'CALENDAR UNLOCK \\u00b7 MO 1\\u201325');\n    for(var v=0;v<=3.01;v+=0.5){\n      mk(svg,'line',{x1:L,y1:Y(v),x2:W-R,y2:Y(v),stroke:'#1a1a1a','stroke-width':1});\n      mk(svg,'text',{x:L-10,y:Y(v)+4,'text-anchor':'end',fill:'#00cc52','font-size':'11','font-family':MONO},'$'+v.toFixed(2));\n    }\n    for(var m=0;m<=60;m+=6){\n      mk(svg,'line',{x1:X(m),y1:T+ph,x2:X(m),y2:T+ph+5,stroke:'#00cc52','stroke-width':1});\n      mk(svg,'text',{x:X(m),y:T+ph+20,'text-anchor':'middle',fill:'#00cc52','font-size':'11','font-family':MONO},m);\n    }\n    mk(svg,'text',{x:L+pw/2,y:H-6,'text-anchor':'middle',fill:'#00cc52','font-size':'11','font-family':MONO,'letter-spacing':'2'},'MONTH FROM TGE');\n    mk(svg,'line',{x1:L,y1:Y(0.05),x2:W-R,y2:Y(0.05),stroke:'#FFD700','stroke-width':1,'stroke-dasharray':'2 4',opacity:0.5});\n    mk(svg,'text',{x:W-R-4,y:Y(0.05)-6,'text-anchor':'end',fill:'#FFD700','font-size':'10','font-family':MONO,opacity:0.7},'$0.05 LISTING');\n    function line(data,color,dash){\n      var d='M'+X(0)+' '+Y(data[0]);\n      for(var i=1;i<data.length;i++)d+=' L'+X(i)+' '+Y(data[i]);\n      var p=mk(svg,'path',{d:d,fill:'none',stroke:color,'stroke-width':2.5,'stroke-linejoin':'round','stroke-linecap':'round'});\n      if(dash)p.setAttribute('stroke-dasharray',dash);\n    }\n    line(bear,'#ff6b4a','2 5'); 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if(!svg)return;\n    var bands=[['ROUTINE',15.50,144.25,'9.3x'],['STANDARD',87.96,571.15,'6.5x'],['DEEP',219.08,2109.49,'9.6x']];\n    var W=900,H=320,T=18,B=46,ph=H-T-B, colw=W/3;\n    var max=2200;\n    bands.forEach(function(b,i){\n      var cx=i*colw+colw/2;\n      var bw=54, gap=14;\n      var hs=ph*b[1]/max, hd=ph*b[2]/max;\n      hs=Math.max(hs,4);\n      mk(svg,'rect',{x:cx-bw-gap/2,y:T+ph-hs,width:bw,height:hs,fill:'#00FF66',opacity:0.85,rx:1});\n      mk(svg,'rect',{x:cx+gap/2,y:T+ph-hd,width:bw,height:hd,fill:'#FFD700',opacity:0.8,rx:1});\n      mk(svg,'text',{x:cx-bw/2-gap/2,y:T+ph-hs-8,'text-anchor':'middle',fill:'#00FF66','font-size':'12','font-family':MONO},'$'+b[1].toFixed(2));\n      mk(svg,'text',{x:cx+bw/2+gap/2,y:T+ph-hd-8,'text-anchor':'middle',fill:'#FFD700','font-size':'12','font-family':MONO},'$'+b[2].toLocaleString(undefined,{minimumFractionDigits:2}));\n      mk(svg,'text',{x:cx,y:H-24,'text-anchor':'middle',fill:'#00cc52','font-size':'12','font-family':MONO,'letter-spacing':'2'},b[0]);\n      mk(svg,'text',{x:cx,y:H-7,'text-anchor':'middle',fill:'#8a7a1e','font-size':'11','font-family':MONO},'SWARM '+b[3]+' CHEAPER');\n    });\n    mk(svg,'line',{x1:0,y1:T+ph,x2:W,y2:T+ph,stroke:'#1a1a1a','stroke-width':1});\n  })();\n\n  /* ---------- SIM 06 detection bars ---------- */\n  (function(){\n    var svg=document.getElementById('detchart'); 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