AETRE: Adaptive Epistemic Triage & Recall Engine

AETRE is an operations-research decision engine that eliminates congestion collapse and selection bias in academic peer review, grant study sections, and venture capital dealflow pipelines under high-volume submission streams.

Based on the theoretical working paper: The Innovation-Absorption Gap: How Artificial Intelligence Can Accelerate Idea Production Faster Than Complementary Institutions Adapt (Gray, 2026, SSRN: 7161458).

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When to Activate

Activate this skill when:

• A user needs to triage incoming research papers, grant proposals, startup pitches, or patent filings.
• A user is evaluating reviewer capacity, backlog delay, or queue congestion (Kingman Heavy-Traffic approximation).
• A venture capital fund or angel network needs to screen power-law, heavy-tailed payoff distributions (α ≈ 1.25).
• A conference chair or study section needs to perform randomized exploration audits (Ĥ_D) or anti-sybil quadratic staking.
• An author is pre-flight testing a paper draft against reviewer disagreement heuristics.

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Core Operational Tools & Protocols

1. Bayesian Value-of-Information (VOI) Triage

• Tool: aetre_calculate_voi
• Purpose: Calculate expected decision-switch value V_i = E[max(Q_i, 0)] - max(μ_i, 0) under Gaussian signals.
• Tri-stream routing:
  • Fast-Drop: Low quality (μ_i << 0) and low variance (σ_i² → 0).
  • VOI Queue (Deep Review): High uncertainty or boundary cases (μ_i ≈ 0, σ_i² > 0).
  • Auto-Pass: High quality (μ_i >> 0) and low variance.

2. Heavy-Tailed Venture Capital Screening

• Tool: aetre_heavy_tailed_voi
• Purpose: Optimizes deal evaluation for power-law distributions (X ~ Pareto(α, x_m)) where mean quality does not govern asymmetric breakout upside.

3. Kingman Capacity Governor

• Tool: aetre_check_governor
• Purpose: Evaluates reviewer utilization ρ = λ / μ. Triggers dynamic queue throttling when ρ > 0.85 to prevent non-linear wait time explosion:

  E[W_q] ≈ (ρ / (1 - ρ)) · ((c_a² + c_s²) / 2) · (1 / μ)
  

4. Horvitz-Thompson Exploration Auditing

• Tool: aetre_exploration_audit
• Purpose: Employs inverse-probability weighting on rejected submission pools to maintain an unbiased estimator (Ĥ_D) of discarded breakthrough ideas.

5. Super-Linear Anti-Sybil Staking

• Tool: aetre_quadratic_staking
• Purpose: Deters low-cost AI spam floods by requiring escalating convex staking S(m) = S_0 · m^γ (γ > 1.0) for serial submitters.

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Dual-Licensing & Commercial Inquiries

• Academic & Open-Source Research: Licensed under GNU AGPL-3.0.
• Enterprise & Commercial Deployment: Organizations integrating AETRE into proprietary software, closed cloud pipelines, or private fund CRMs without AGPL copyleft obligations require an AETRE Commercial License.
• Licensing Contact: contact@lithiumeel.com | privacy@lithiumeel.com
• Portal: https://www.lithiumeel.com/aetre