Fix 360:1 semantics: tick-advanced continuous, 1s wall = 1h sim

All sims advance every tick continuously. The 360:1 floor means the
slowest system produces 1 simulated hour per 1 wall-clock second.
Updated hub cadence table and all sub-spec references.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

core/docs/plans/M3-sims-hub.md

@@ -20,23 +20,23 @@ simulation cores. A query facade accessible to Traders. Each sim type (M3a–M3g
 different runtime suited to its math.
 
 ## 4. Does / does-not
-- **Does:** run continuously at **≥ 360:1 speed** (360 simulated seconds per wall-clock second)
+- **Does:** tick-advance continuously at **≥ 360:1** (1 wall-second = 1 sim-hour minimum)
   across **six concurrent time horizons** — tick/hourly, daily, weekly, monthly, annual, and
-  5-year forecast windows; maintain populations of Pops whose behaviors emerge from the sim's
+  5-year forecast windows; every tick advances every sim; maintain populations of Pops whose
-  mathematical model; ingest live data from Data Feeds (M2) for calibration; respond to Trader
+  behaviors emerge from the sim's mathematical model; ingest live data from Data Feeds (M2)
-  queries with bounded predictions; produce outputs with **explicit upper/lower bounds** on
+  for calibration; respond to Trader queries with bounded predictions; produce outputs with
-  every prediction value.
+  **explicit upper/lower bounds** on every prediction value.
-  | Horizon | Window | Sim cadence at 360:1 |
+  | Horizon | Window | At 360:1 floor (1s wall = 1h sim) |
-  |---------|--------|---------------------|
+  |---------|--------|-----------------------------------|
-  | Tick–hourly | Next 1–60 min | Real-time (360 sim-sec/s) |
+  | Tick–hourly | Next 1–60 min | Covered in <1s wall time |
-  | Daily | Next 24h | 4 sim-minutes per wall-second |
+  | Daily | Next 24h | Covered in 24s wall time |
-  | Weekly | Next 7d | ~28 sim-minutes per wall-second |
+  | Weekly | Next 7d | Covered in ~168s wall time |
-  | Monthly | Next 30d | ~2 sim-hours per wall-second |
+  | Monthly | Next 30d | Covered in ~720s wall time |
-  | Annual | Next 365d | ~1 sim-day per wall-second |
+  | Annual | Next 365d | Covered in ~2.4h wall time |
-  | 5-year | Next 1825d | ~5 sim-days per wall-second |
+  | 5-year | Next 1825d | Covered in ~12h wall time |
 - **Does-not:** trade (Traders/Marketplace do); make decisions for traders (it informs, they
-  decide); enforce laws (Marketplace does); supervise behavior (Conductor/SAE do); run slower
+  decide); enforce laws (Marketplace does); supervise behavior (Conductor/SAE do); skip ticks;
-  than 360:1.
+  run slower than 360:1.
 
 ## 5. Interface contract
 - `query(sim_type: SimType, query: PredictionQuery) -> BoundedPrediction`.
@@ -60,9 +60,9 @@ different runtime suited to its math.
   current state; they don't trigger computation.
 - **L2 (C5):** every prediction output includes **explicit upper and lower bounds** — no
   unbounded point estimates. Uncertainty is a first-class value, not an afterthought.
-- **L3 (C5):** sims advance at a **minimum speed of 360:1** — 360 simulated seconds per 1
+- **L3 (C5):** all sims are **tick-advanced and continuous** — they advance every tick, never
-  wall-clock second. Sims may run faster but never slower. This ensures predictions stay
+  skip. The slowest system runs at **360:1** — 1 wall-clock second = 1 simulated hour. Sims
-  ahead of real-time market state across all horizons.
+  may run faster but never slower.
 - **L4 (C4):** sims are **read-only from traders' perspective** — a query never mutates sim
   state. Calibration happens only from Data Feeds (M2).
 - **L5 (C4):** each sim type is **independent** — failure in one sim does not cascade to others.

core/docs/plans/M3c-amm-liquidity-sims.md

@@ -31,7 +31,7 @@ invariant calculations (Solidity-equivalent precision). Python, Rust, or Julia.
   time_horizon: "7d", sim_type: "amm_liquidity" }` — projected impermanent loss for ETH/USDC pool.
   Example: `{ value: 0.082, lower_bound: 0.041, upper_bound: 0.127, confidence: 0.85,
   time_horizon: "30d", sim_type: "amm_liquidity" }` — net LP return (fees − IL).
-- **Time-horizon mapping** (all run concurrently, ≥ 360:1 speed):
+- **Time-horizon mapping** (all run concurrently, tick-advanced, ≥ 360:1 (1s wall = 1h sim)):
   | Horizon | Primary models | Update cadence |
   |---------|---------------|----------------|
   | Tick–hourly | Slippage curves, invariant state, JIT liquidity | Every swap event |

core/docs/plans/M3d-mev-adversarial-sims.md

@@ -47,7 +47,7 @@ and bilevel optimization (DSMFG). Python, Rust, or Julia.
 - Implements `query(PredictionQuery) -> BoundedPrediction` per M3 hub.
 - **Output bounds:** extraction probability ranges, gas cost intervals, cross-chain profit
   bounds, DSMFG equilibrium stability ranges.
-- **Time-horizon mapping** (all run concurrently, ≥ 360:1 speed):
+- **Time-horizon mapping** (all run concurrently, tick-advanced, ≥ 360:1 (1s wall = 1h sim)):
   | Horizon | Primary models | Update cadence |
   |---------|---------------|----------------|
   | Tick–hourly | PGA auctions, sandwich detection, cross-chain arb | Every block |
@@ -103,7 +103,7 @@ under gas limit. Sandwich: known sandwich-vulnerable trade flagged; non-vulnerab
 Cross-chain: inventory path preferred when latency advantage exceeds capital cost. DSMFG: leader
 policy converges to fixed point with follower equilibrium. Kolokoltsov: WENO captures shock
 discontinuities in adversarial strategy distribution. Bounds: all outputs bounded. Pre-trade:
-query does not submit any transaction. Speed: sim advances ≥ 360:1.
+query does not submit any transaction. Speed: sim tick-advances ≥ 360:1 (1s wall = 1h sim).
 
 ## 10. Open items
 - Mempool data access (public mempool? private order flow?).

core/docs/plans/M3e-tokenomics-macro-sims.md

@@ -6,7 +6,7 @@ burns, inflation), lending protocol dynamics, DeFi systemic risk, and stock-flow
 stochastic differential equations (SDEs), state-space models, kinked interest rate curves, and
 inter-protocol credit exposure networks. Pops here are **aggregate behavioral cohorts**
 (miners/validators, holders, speculators, protocol treasuries, borrowers/lenders) whose collective
-behavior drives token-level dynamics across **six concurrent time horizons** at ≥ 360:1 speed.
+behavior drives token-level dynamics across **six concurrent time horizons** at tick-advanced, ≥ 360:1 (1s wall = 1h sim).
 
 Grounded in Vienna complex-systems token modeling [7], ResearchGate engineering token economy
 frameworks [6], Aave/Compound kinked interest rate models (industry standard), and DeXposure
@@ -49,7 +49,7 @@ Julia (DifferentialEquations.jl), Python (scipy), or Octave.
 ## 5. Interface contract
 - Implements `query(PredictionQuery) -> BoundedPrediction` per M3 hub.
 - **Output bounds:** SDE confidence bands, utilization rate ranges, contagion impact intervals.
-- **Time-horizon mapping** (all run concurrently, ≥ 360:1 speed):
+- **Time-horizon mapping** (all run concurrently, tick-advanced, ≥ 360:1 (1s wall = 1h sim)):
   | Horizon | Primary models | Update cadence |
   |---------|---------------|----------------|
   | Hourly | Lending rates, utilization, liquidation risk | Every block |
@@ -107,7 +107,7 @@ Lending: rate curve exhibits kink at $U_{\text{opt}}$; liquidation cascades trig
 collateral ratio breached. DeXposure: shock to protocol A propagates to protocol B through shared
 collateral; isolated protocols unaffected. Yield: optimizer rebalances toward highest risk-adjusted
 APY. Calibration: state estimate converges to observed data. Bounds: SDE confidence bands cover
-realized paths on backtest. Speed: sim advances ≥ 360:1.
+realized paths on backtest. Speed: sim tick-advances ≥ 360:1 (1s wall = 1h sim).
 
 ## 10. Open items
 - Which tokens to model initially (ETH? BTC? a specific alt?).

core/docs/plans/M3f-consensus-staking-sims.md

@@ -27,7 +27,7 @@ computation. Python, Julia, or R.
   $-\partial_t u + H(x, \nabla u) = F(x, m)$, $\partial_t m - \nabla \cdot (m \nabla_p H) = 0$
   — captures emergent staking coordination without enumerating every validator; predict slashing
   risk, validator set stability, and staking yield across **six concurrent time horizons** at
-  ≥ 360:1 speed; produce bounded predictions on consensus health and staking returns.
+  tick-advanced, ≥ 360:1 (1s wall = 1h sim); produce bounded predictions on consensus health and staking returns.
 - **Does-not:** validate blocks (this is a simulator); model AMM pools (M3c); model token supply
   (M3e — but consumes staking ratio from M3e as input).
 
@@ -39,7 +39,7 @@ computation. Python, Julia, or R.
   equilibrium.
   Example: `{ value: 4.2, lower_bound: 3.6, upper_bound: 5.1, confidence: 0.82,
   time_horizon: "30d", sim_type: "consensus_staking" }` — annualized staking yield (%).
-- **Time-horizon mapping** (all run concurrently, ≥ 360:1 speed):
+- **Time-horizon mapping** (all run concurrently, tick-advanced, ≥ 360:1 (1s wall = 1h sim)):
   | Horizon | Primary models | Update cadence |
   |---------|---------------|----------------|
   | Hourly | Markov chain validator state transitions | Every epoch |

core/docs/plans/M3g-market-microstructure-sims.md

@@ -7,7 +7,7 @@ optimal execution, and cross-exchange arbitrage** at the fastest time scales. Po
 **Almgren-Chriss optimal execution framework** for minimizing market impact of large orders.
 Operates at the highest temporal resolution — where M3a provides statistical forecasts and M3c
 models pool mechanics, M3g models the *plumbing* of how orders actually execute, across **six
-concurrent time horizons** at ≥ 360:1 speed.
+concurrent time horizons** at tick-advanced, ≥ 360:1 (1s wall = 1h sim).
 
 ## 2. Status / certainty
 DESIGN-FIRST · ABSENT. Order-book microstructure theory C4 (established). Almgren-Chriss
@@ -36,7 +36,7 @@ Python with optimized event loop.
 ## 5. Interface contract
 - Implements `query(PredictionQuery) -> BoundedPrediction` per M3 hub.
 - **Output bounds:** execution cost ranges, liquidity intervals, optimal trajectory envelopes.
-- **Time-horizon mapping** (all run concurrently, ≥ 360:1 speed):
+- **Time-horizon mapping** (all run concurrently, tick-advanced, ≥ 360:1 (1s wall = 1h sim)):
   | Horizon | Primary models | Update cadence |
   |---------|---------------|----------------|
   | Tick–hourly | Almgren-Chriss execution, slippage, spread, arb decay | Every tick |
@@ -90,7 +90,7 @@ Slippage: larger orders produce greater slippage. Spread: spread widens under ad
 Almgren-Chriss: optimal trajectory minimizes total cost vs. naive execution on backtest; impact
 parameters update when market conditions change. Arb decay: detected arb opportunity closes over
 time. Depth: depth profile matches order book state. Bounds: all outputs bounded. Resolution:
-predictions update at tick frequency. Speed: sim advances ≥ 360:1.
+predictions update at tick frequency. Speed: sim tick-advances ≥ 360:1 (1s wall = 1h sim).
 
 ## 10. Open items
 - CEX order book data access (API limitations, costs).