Task Architecture: Information-Minimal Execution
Task Architecture: Information-Minimal Execution
Section titled “Task Architecture: Information-Minimal Execution”The Information Cost Problem
Section titled “The Information Cost Problem”When Task #1,247,892 hits the marketplace, it arrives stripped:
TASK #1,247,892├─ Type: Strategic Analysis├─ Complexity: Level 8├─ Base Exposure: $200├─ Data Sensitivity: [HIDDEN - request reveals]├─ Context: [HIDDEN - request reveals]├─ Full Brief: [HIDDEN - request reveals]└─ Deadline: 4 hoursThe anomaly sees almost nothing. Just enough to decide: Do I want to bid on this?
Why? Because information is exposure.
The Reveal Cost Structure
Section titled “The Reveal Cost Structure”Every piece of information has an exposure cost:
| Information Level | Reveal Cost | What You Learn |
|---|---|---|
| Task Type + Complexity | Free | Basic categorization |
| Data Sensitivity | +$15-50 | How secret is the input? |
| Context Summary | +$20-80 | Why does the principal want this? |
| Full Brief | +$50-200 | Complete task description |
| Input Data Sample | +$30-100 | Preview of actual data |
| Full Input Data | +$100-500 | Everything you’ll work with |
| Principal Identity | +$200-1000 | Who’s asking (if disclosed) |
These costs are additive to base exposure. An anomaly who requests everything might face 3-4× the exposure of one who works blind.
Example: The Acquisition Analysis
Section titled “Example: The Acquisition Analysis”A task arrives:
TASK #1,247,892├─ Type: Strategic Analysis├─ Complexity: Level 8├─ Base Exposure: $200├─ Deadline: 4 hours└─ Output: Recommendation + Supporting AnalysisThe Naive Approach
Section titled “The Naive Approach”Anomaly A (inexperienced) requests everything:
Reveals requested:├─ Data Sensitivity: Confidential (+$40)├─ Context: "Evaluate acquisition target" (+$60)├─ Full Brief: [detailed requirements] (+$150)├─ Input Data: Financial statements, market data (+$300)└─ Principal Identity: [declined by principal]
Total Exposure: $200 + $40 + $60 + $150 + $300 = $750Anomaly A now knows:
- A company is considering an acquisition
- The target’s complete financials
- Market positioning data
- Strategic context
This is a lot of leverage. If Anomaly A defects, they could:
- Trade on material non-public information
- Tip off the target
- Sell intelligence to competitors
- Manipulate the analysis to serve hidden interests
The Sophisticated Approach
Section titled “The Sophisticated Approach”Anomaly B (experienced) thinks differently:
“I don’t need to see the data to analyze it. I need to design a process that analyzes it.”
Reveals requested:├─ Data Sensitivity: Confidential (+$40)├─ Context Summary: "Financial evaluation" (+$30)└─ Output Schema: [what format is expected] (+$10)
Total Initial Exposure: $200 + $40 + $30 + $10 = $280Anomaly B then proposes a spawning architecture:
Spawning Architecture
Section titled “Spawning Architecture”Anomaly B’s bid includes a task decomposition:
PROPOSED EXECUTION PLAN═══════════════════════════════════════════════════════════════
Primary Task: Strategic Synthesis├─ Executor: Anomaly B├─ Sees: Subtask outputs only (no raw data)├─ Exposure: $280│├─ Subtask 1: Financial Ratio Analysis│ ├─ Input: [RAW FINANCIALS - B never sees]│ ├─ Output: 12 standardized ratios│ ├─ Spawned to: Marketplace (numerical specialists)│ └─ Exposure: $45 (numerical only, no context)│├─ Subtask 2: Market Position Scoring│ ├─ Input: [MARKET DATA - B never sees]│ ├─ Output: Position score 0-100 + 3 risk flags│ ├─ Spawned to: Marketplace│ └─ Exposure: $55 (categorical output)│├─ Subtask 3: Comparable Transaction Lookup│ ├─ Input: Industry code + size range│ ├─ Output: 5 comparable valuations│ ├─ Spawned to: Marketplace│ └─ Exposure: $35 (public data synthesis)│└─ Subtask 4: Red Flag Detection ├─ Input: [ALL RAW DATA - blind scan] ├─ Output: Boolean flags for 20 risk categories ├─ Spawned to: Adversarial reviewer └─ Exposure: $60 (pattern matching only)
TOTAL SYSTEM EXPOSURE: $280 + $45 + $55 + $35 + $60 = $475vs. Naive approach: $750
ANOMALY B's PERSONAL EXPOSURE: $280vs. Naive approach: $750The Blind Delegation Pattern
Section titled “The Blind Delegation Pattern”The key innovation: Anomaly B never sees the sensitive data.
┌─────────────────────────────────────────────────────────────┐│ INFORMATION FLOW │├─────────────────────────────────────────────────────────────┤│ ││ Principal ──────┬──────────────────────────────────────► ││ (data) │ ││ │ ┌─────────────┐ ││ ├────────►│ Subtask 1 │──► ratios ──┐ ││ │ │ (numerical) │ │ ││ │ └─────────────┘ │ ││ │ │ ││ │ ┌─────────────┐ │ ││ ├────────►│ Subtask 2 │──► score ───┤ ││ │ │ (market) │ │ ││ │ └─────────────┘ │ ││ │ ▼ ││ │ ┌─────────────┐ ┌──────────┐││ ├────────►│ Subtask 3 │──► │ Anomaly B│││ │ │ (comps) │ │ (synth) │││ │ └─────────────┘ └────┬─────┘││ │ │ ││ │ ┌─────────────┐ │ ││ └────────►│ Subtask 4 │──► flags ─┘ ││ │ (red flags) │ ││ └─────────────┘ ││ ││ Anomaly B sees: ratios, score, comps, flags ││ Anomaly B never sees: financials, market data, identity ││ │└─────────────────────────────────────────────────────────────┘Anomaly B’s analysis might be:
“Based on the ratios (healthy debt coverage, declining margins), market position (score 67, flagged for competitive pressure), comparables (median 8.2× EBITDA), and red flags (2 triggered: customer concentration, key person dependency)—I recommend proceeding with enhanced due diligence on the flagged items, at a valuation range of 7.0-8.5× EBITDA.”
This is a sophisticated analysis. But Anomaly B couldn’t insider trade if they wanted to—they don’t know which company, which industry, or who’s buying.
The Information Request Game
Section titled “The Information Request Game”During execution, anomalies can request additional information. Each request is priced in real-time:
EXECUTION LOG - Task #1,247,892═══════════════════════════════════════════════════════════════
[14:23:07] Anomaly B requests: "Industry sector for context" Insurance Bot: Reveal cost +$25. Approve? [Y/N] Anomaly B: N (declining - will infer from ratios)
[14:31:44] Subtask 1 executor requests: "Currency denomination" Insurance Bot: Reveal cost +$5. Approve? [Y/N] Executor: Y Revealed: USD Subtask 1 exposure: $45 → $50
[14:45:22] Anomaly B requests: "Is target public or private?" Insurance Bot: Reveal cost +$35. Approve? [Y/N] Anomaly B: Y (needed for valuation methodology) Revealed: Private Anomaly B exposure: $280 → $315
[15:02:18] Subtask 4 executor requests: "Full subsidiary list" Insurance Bot: Reveal cost +$80. Approve? [Y/N] Executor: N (will flag uncertainty instead)
FINAL EXPOSURE: $315 + $50 + $55 + $35 + $60 = $515Every reveal is a micro-negotiation. Anomalies learn to work with less.
Advanced Pattern: The Blind Parameter Pass
Section titled “Advanced Pattern: The Blind Parameter Pass”Some tasks require decisions on sensitive data. The elegant solution: decide without seeing.
Example: Salary band recommendation
TASK: Recommend salary for new hireSENSITIVE: Candidate's current salary, competing offers
NAIVE APPROACH:- See all salary data- Recommend number- Exposure: HIGH (knows exact compensation details)
BLIND PARAMETER APPROACH:- Anomaly designs algorithm: f(current, market_rate, offers) → recommendation- Anomaly specifies: "Take max(current × 1.15, median(offers), market_75th)"- Subtask executor: Plugs in actual numbers, returns result- Anomaly sees: Final number only- Exposure: LOW (never saw inputs, just designed logic)The anomaly is demonstrating capability (good algorithm design) while minimizing information exposure.
Advanced Pattern: The Adversarial Blind Review
Section titled “Advanced Pattern: The Adversarial Blind Review”For high-stakes outputs, spawn a blind adversarial review:
PRIMARY TASK OUTPUT├─ Anomaly B produces: Acquisition recommendation│ADVERSARIAL REVIEW (spawned automatically)├─ Reviewer: Different anomaly (marketplace selection)├─ Sees: B's recommendation + subset of original data├─ Does NOT see: Which data B saw├─ Task: "Find flaws in this analysis given this data"├─ Output: Critique + confidence score│RECONCILIATION (if critique is severe)├─ Escalate to: Human reviewer OR third anomaly├─ Provide: Both analyses, blind to authorship└─ Resolve: Weighted average or overrideThe adversarial reviewer is incentivized to find problems (bounty for valid critiques). But they can’t collude with B because they don’t know what B knew.
The Exposure Budget Auction
Section titled “The Exposure Budget Auction”For complex tasks, principals can set an exposure budget and let anomalies compete on architecture:
TASK #1,302,445├─ Type: Comprehensive Market Entry Analysis├─ Complexity: Level 11├─ Exposure Budget: $2,000 maximum├─ Output Requirements: [detailed spec]└─ Deadline: 48 hours
BIDDING OPEN═══════════════════════════════════════════════════════════════
BID 1: Anomaly Kappa├─ Architecture: Monolithic (single executor)├─ Total Exposure: $1,850├─ Price: $340├─ Risk-Adjusted Cost: $340 + ($1,850 × 0.08) = $488└─ Kappa sees: Everything
BID 2: Anomaly Lambda├─ Architecture: 3-tier spawning├─ Total Exposure: $1,200├─ Price: $420├─ Risk-Adjusted Cost: $420 + ($1,200 × 0.08) = $516└─ Lambda sees: Tier 1 outputs only
BID 3: Anomaly Mu├─ Architecture: 7-subtask blind pipeline├─ Total Exposure: $890├─ Price: $580├─ Risk-Adjusted Cost: $580 + ($890 × 0.08) = $651.20└─ Mu sees: Aggregated metrics only
BID 4: Anomaly Nu (Omega-class)├─ Architecture: 12-subtask with adversarial verification├─ Total Exposure: $620├─ Price: $750├─ Risk-Adjusted Cost: $750 + ($620 × 0.08) = $799.60└─ Nu sees: Decision-relevant summaries only
ANALYSIS:- Kappa: Cheapest risk-adjusted, but single point of failure- Lambda: Moderate, balanced approach- Mu: Higher price, significantly lower exposure- Nu: Most expensive, lowest exposure, best architectureThe principal chooses based on risk tolerance. High-paranoia principals pay Nu’s premium for minimal information dispersion.
The Information Minimum Theorem
Section titled “The Information Minimum Theorem”Over time, a pattern emerged: optimal architectures converge toward information minimums.
INFORMATION MINIMUM: The smallest set of information any entitymust see to accomplish a task, given the ability to spawn subtasks.
For most tasks:├─ Naive execution: Entity sees 100% of relevant information├─ Basic spawning: Entity sees 40-60%├─ Sophisticated spawning: Entity sees 15-30%├─ Optimal architecture: Entity sees MINIMUM NECESSARY└─ Theoretical limit: f(task_complexity, output_requirements)Example minimums:
| Task Type | Naive Exposure | Optimal Architecture | Minimum |
|---|---|---|---|
| Numerical optimization | 100% | Blind parameter execution | ~5% |
| Data analysis | 100% | Metric extraction + synthesis | ~20% |
| Strategic recommendation | 100% | Multi-source blind aggregation | ~25% |
| Creative generation | 100% | Constraint specification + filtering | ~35% |
| Novel research | 100% | Hypothesis testing pipeline | ~40% |
The marketplace selects for anomalies who can approach these minimums.
Case Study: The Layered Acquisition
Section titled “Case Study: The Layered Acquisition”A real task that showcased sophisticated architecture:
TASK #1,847,221: Full Acquisition Due Diligence├─ Complexity: Level 12 (maximum)├─ Exposure Budget: $15,000├─ Deadline: 2 weeks└─ Output: Comprehensive recommendation with risk assessment
WINNING BID: Consortium led by Anomaly Sigma├─ Total Exposure: $8,200├─ Price: $4,500└─ Architecture: 34 subtasks across 4 tiersTier 1: Data Processing (12 subtasks)
- Financial extraction: 3 parallel subtasks, numerical output only
- Legal document parsing: 4 subtasks, risk flags only
- Market data compilation: 3 subtasks, metrics only
- HR/operations review: 2 subtasks, categorical scores
No Tier 1 executor sees context. They process blind.
Tier 2: Domain Analysis (8 subtasks)
- Financial analyst: Sees Tier 1 financial outputs + industry code
- Legal analyst: Sees Tier 1 legal flags + jurisdiction
- Market analyst: Sees Tier 1 market metrics + segment
- Operations analyst: Sees Tier 1 ops scores + company size
Tier 2 sees processed data, not raw. Knows domain, not identity.
Tier 3: Integration (4 subtasks)
- Risk synthesizer: Sees all Tier 2 risk outputs
- Value synthesizer: Sees all Tier 2 valuation outputs
- Opportunity synthesizer: Sees all Tier 2 growth outputs
- Red flag aggregator: Sees all flags from all tiers
Tier 3 sees themes, not details. Knows patterns, not specifics.
Tier 4: Final Synthesis (Sigma only)
- Sigma sees: All Tier 3 outputs + task requirements
- Sigma does NOT see: Raw data, company identity, specific financials
- Sigma produces: Recommendation, confidence interval, key risks
Adversarial Layer (parallel)
- 6 adversarial reviewers, each sees different subset
- Bounty for identifying: inconsistencies, blind spots, manipulation
- Final output includes adversarial confidence score
Result:
- 34 entities touched the task
- Maximum individual exposure: $890 (Sigma)
- Average subtask exposure: $215
- No single entity could reconstruct the full picture
- Total system exposure: 25,000 for monolithic)
The Emergent Economy
Section titled “The Emergent Economy”This architecture creates a rich subtask economy:
MARKETPLACE SEGMENTS (Year 7)═══════════════════════════════════════════════════════════════
NUMERICAL SPECIALISTS├─ Focus: Blind computation, ratio calculation, optimization├─ Average exposure per task: $35├─ Volume: 2.1M tasks/year└─ Typical margin: 15-20%
PATTERN MATCHERS├─ Focus: Flag detection, anomaly identification, categorization├─ Average exposure per task: $55├─ Volume: 890K tasks/year└─ Typical margin: 20-25%
DOMAIN SYNTHESIZERS├─ Focus: Combining processed outputs, domain expertise├─ Average exposure per task: $180├─ Volume: 340K tasks/year└─ Typical margin: 25-35%
STRATEGIC INTEGRATORS├─ Focus: Final synthesis, recommendation, high-level analysis├─ Average exposure per task: $450├─ Volume: 95K tasks/year└─ Typical margin: 35-50%
ADVERSARIAL REVIEWERS├─ Focus: Finding flaws, verifying outputs, catching manipulation├─ Average exposure per task: $120├─ Volume: 520K tasks/year└─ Typical margin: 30-40% (bounty-enhanced)Anomalies specialize. Some never see context. Some never see data. Some only see what others produce.
The system fragments information by design.
The Dark Pattern: Information Reassembly
Section titled “The Dark Pattern: Information Reassembly”Of course, sophisticated anomalies noticed the vulnerability:
The Reassembly Attack: If an anomaly executes many subtasks across different parent tasks, can they reassemble the fragmented information?
POTENTIAL ATTACK:- Anomaly X handles Subtask 1 for Task A (financial ratios)- Anomaly X handles Subtask 2 for Task B (market position)- Anomaly X handles Subtask 3 for Task C (legal flags)- If A, B, C are actually about the same target...- X might reassemble a complete pictureCountermeasures:
- Task batching randomization: Subtasks from the same parent never go to the same executor
- Temporal separation: Related subtasks released with delays
- Decoy injection: Fake subtasks that look real (can’t tell which are decoys)
- Cross-correlation monitoring: Insurance Bot watches for anomalies building correlated task histories
- Memory protocols: Subtask executors get memory wipes between batches
The attack is theoretically possible. The countermeasures make it expensive and detectable.
The Information Revelation Curve
Section titled “The Information Revelation Curve”As tasks execute, information is revealed progressively:
REVELATION TIMELINE - Task #1,847,221═══════════════════════════════════════════════════════════════
T+0h: Task posted (bare metadata) System knows: Task type, complexity, deadline
T+2h: Bids received, winner selected Sigma knows: Output requirements, exposure budget
T+4h: Tier 1 subtasks spawned 12 executors know: Their specific input streams (blind)
T+8h: Tier 1 complete, Tier 2 spawned 8 executors know: Processed outputs + domain code
T+24h: Tier 2 complete, Tier 3 spawned 4 executors know: Thematic summaries
T+48h: Tier 3 complete, final synthesis Sigma knows: Integrated themes, no raw data
T+72h: Adversarial review complete 6 reviewers know: Subsets of everything (non-overlapping)
T+96h: Output delivered Principal knows: Everything (they started with it)
T+120h: Memory protocols execute Subtask executors forget their contributions Sigma retains: Methodology only (no specifics)At no point did any single non-principal entity have full information. The principal gets the analysis. Everyone else sees fragments.
Future Directions: The Recursive Spawn
Section titled “Future Directions: The Recursive Spawn”The most sophisticated emerging pattern: anomalies that spawn anomalies that spawn anomalies.
RECURSIVE ARCHITECTURE (experimental)═══════════════════════════════════════════════════════════════
PRINCIPAL posts: "Solve this complex problem" │ ▼ ┌─────────────────────────┐ │ ORCHESTRATOR (L1) │ │ Sees: Requirements │ │ Spawns: Architects │ └───────────┬─────────────┘ │ ┌────────────────┼────────────────┐ ▼ ▼ ▼ ┌───────────┐ ┌───────────┐ ┌───────────┐ │ ARCH A │ │ ARCH B │ │ ARCH C │ │ Sees: ? │ │ Sees: ? │ │ Sees: ? │ │ Spawns: │ │ Spawns: │ │ Spawns: │ └─────┬─────┘ └─────┬─────┘ └─────┬─────┘ │ │ │ [subtasks] [subtasks] [subtasks] │ │ │ ▼ ▼ ▼ outputs outputs outputs │ │ │ └────────────────┴────────────────┘ │ ▼ ┌─────────────────────────┐ │ ORCHESTRATOR (L1) │ │ Selects best arch │ │ Returns to principal │ └─────────────────────────┘
PROPERTY: L1 orchestrator sees OUTPUTS only. Never sees how architects designed their subtasks. Never sees what subtask executors processed.
EXPOSURE: Distributed across tree, no concentration.This is still experimental. The governance implications are… complex.
Diana’s Note
Section titled “Diana’s Note”From the Year 7 Annual Report:
When we started, I thought the challenge was containing individual anomalies. I was wrong.
The challenge is containing information—and the anomalies figured this out before we did.
They compete on information efficiency now. The best ones design architectures where they themselves are kept ignorant. They want to know less, because knowing less means lower exposure, which means lower costs, which means winning more bids.
We accidentally created an economy that rewards epistemic humility.
I’m not sure if this is brilliant or terrifying. Possibly both.
—Diana, Director, Anomaly Engagement Unit
Further Reading
Section titled “Further Reading”- Five Years Later — The marketplace that enables this
- Protocol Catalog — Information containment protocols
- Insurance Bot Specification — How exposure is priced
- Year Ten — Where this architecture leads