AETRE: Adaptive Epistemic Triage & Recall Engine

"Empowering Breakthrough Ideas in the Age of Abundance."
A high-performance, mathematically rigorous operations-research engine that optimizes academic peer-review pipelines, grant study sections, and venture capital dealflow.

Release status: experimental public alpha. The software and mathematical simulations are testable, but the bundled data are synthetic and do not establish prospective effectiveness in a live conference, grant, or investment workflow. Use outputs as decision-support diagnostics, not as autonomous acceptance, rejection, funding, or investment decisions.

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

The Problem: The Innovation-Absorption Gap

When Artificial Intelligence makes idea generation cheap ($c_{\text{gen}} \to 0$), proposal volume ($N$) explodes. However, downstream evaluation, laboratory validation, and human review capacity ($K$) remain strictly finite.

This creates three critical pipeline pathologies:

1. The Kingman Delay Explosion: When evaluator utilization $\rho = \lambda / \mu$ approaches saturation ($\rho > 0.85$), wait times shoot up non-linearly according to Kingman's Heavy-Traffic equation: $$E[W_q] \approx \frac{\rho}{1-\rho} \cdot \frac{c_a^2 + c_s^2}{2} \cdot \frac{1}{\mu}$$

2. The Asymmetric Payoff Trap: In heavy-tailed domains like venture capital and breakthrough scientific discovery (Pareto index $\alpha \approx 1.25$), consensus-seeking scoring systems penalize high-variance, transformative outliers in favor of safe, incremental proposals.

3. The Finite-Capacity Recall Ceiling (Proposition 1): Without active epistemic triage, true breakthrough recall asymptotically decays towards zero as arrival rates surge: $$R_N \le \min\left(1, \frac{K_N}{H_N}\right) \to 0 \quad \text{as } N \to \infty$$

Related MCP server: Adaptive Recall

The Solution: The AETRE 4-Pillar Pipeline

               INCOMING PROPOSAL STREAM (N)
                            │
                            ▼
    ┌───────────────────────────────────────────────────┐
    │ 1. Bayesian Value-of-Information (VOI) Triage     │
    │    Routes attention strictly where it changes     │
    │    the downstream decision (μ_q, σ_q^2).          │
    └───────────────────────────────────────────────────┘
                            │
        ┌───────────────────┼───────────────────┐
        ▼                   ▼                   ▼
  [ Fast-Drop ]       [ VOI Queue ]       [ Auto-Pass ]
  Low Q, Low Var      High Uncertainty    High Q, Low Var
  (Quick reject)      (Deep review)       (Direct accept)
                            │
                            ▼
    ┌───────────────────────────────────────────────────┐
    │ 2. Kingman Heavy-Traffic Capacity Governor        │
    │    Dynamically throttles queues to preserve       │
    │    reviewer quality and prevent burnout (ρ ≤ 0.85)│
    └───────────────────────────────────────────────────┘
                            │
        ┌───────────────────┴───────────────────┐
        ▼                                       ▼
  [ Selected Cohort (K) ]             [ 3. Exploration Audit Pool ]
  Optimal High-Conviction             Randomized Non-Consensus Ideas
                                                │
                                                ▼
                                      [ 4. Counterfactual Tracker ]
                                      Unbiased Horvitz-Thompson H_hat_D

Repository Structure

.
├── Cargo.toml                  # Workspace manifest (AGPL-3.0)
├── crates/
│   ├── aetre-core/             # Pure Rust decision engine (VOI, Kingman, Pareto, Staking)
│   ├── aetre-cli/              # Command-line interface, VC benchmark & validation tool
│   └── aetre-mcp/              # Model Context Protocol server (20 tools, 4 resources, 3 prompts)
├── examples/
│   ├── datasets/               # Held-out review and dealflow test splits
│   ├── proposals.json          # Benchmark evaluation candidates
│   └── mcp_config.json         # Claude Desktop & Cursor connection template
├── CITATION.cff                # Citation File Format (Zenodo DOI & SSRN: 7161458)
├── Dockerfile                  # Production container definition
├── fly.toml                    # Serverless Cloud deployment config
├── DATASETS.md                 # Fixture provenance and third-party data guidance
├── LICENSE                     # GNU Affero General Public License v3.0 text
├── LICENSING.md                # AGPL/commercial licensing overview
└── README.md

Quickstart & CLI Usage

1. Run the Rust Test Suite & Verification

cargo test --workspace
cargo clippy --workspace --all-targets -- -D warnings

2. Run the Multi-Regime Monte Carlo Benchmark

cargo run -p aetre-cli -- benchmark --replications 500
# Export results to JSON or CSV:
cargo run -p aetre-cli -- benchmark --replications 500 --json
cargo run -p aetre-cli -- benchmark --replications 500 --csv

3. Run the Venture Capital Pareto Dealflow Benchmark

Simulates asymmetric power-law distributions ($\alpha = 1.25$, $x_m = $50\text{k}$, 10,000 deals, 60 unicorn targets):

cargo run -p aetre-cli -- vc-benchmark --deals 10000 --budget 100 --alpha 1.25

4. Run Backtests on Held-Out Datasets

# Smoke-test the 8-policy backtest with the included synthetic fixture
cargo run -p aetre-cli -- backtest --file examples/datasets/openreview_heldout_backtest.json --budget 4 --boundary 6.0

# Run Level 4 prospective shadow pilot simulation & 3-arm trial
cargo run -p aetre-cli -- shadow-pilot --mode simulate --budget 50 --audit-rate 0.05

# Validate predictions file against frozen test split
cargo run -p aetre-cli -- validate-predictions --file examples/validation_schema.json --budget 20 --threshold 0.5

The retrospective backtest's AETRE policy is a frozen Calibrated Rescue Score with empirically specified coefficients. It is motivated by boundary-crossing value of information, but it is distinct from the analytical Bayesian VOI calculation implemented by aetre-core.

5. Evaluate Theoretical Proposition 1 Bounds

cargo run -p aetre-cli -- bound --arrivals 5000 --capacity 200 --high-rate 0.067 --csv

6. Run Kingman Capacity Governor Telemetry

cargo run -p aetre-cli -- queue --arrival-rate 95 --service-rate 100

7. Calculate Horvitz-Thompson Exploration Audit ($\hat{H}_D$)

cargo run -p aetre-cli -- audit --pool 4800 --sample 25 --found 1

8. Compute Super-Linear Anti-Sybil Staking Requirements

cargo run -p aetre-cli -- staking --base 100 --exponent 1.5 --submissions 20

Model Context Protocol (MCP) Integration

AETRE provides a native, high-speed Model Context Protocol (MCP) server implementing 20 Tools, 4 Resources, and 3 Pre-Configured Prompts for Claude Desktop, Cursor, and other MCP clients.

Configuration (Claude Desktop / Cursor)

AETRE runs locally as a high-performance native JSON-RPC 2.0 stdio MCP server. Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "aetre": {
      "command": "cargo",
      "args": ["run", "--release", "--manifest-path", "/PATH/TO/aetre/Cargo.toml", "-p", "aetre-mcp"]
    }
  }
}

Optional local HTTP mode

cargo run -p aetre-mcp -- --serve --headless

HTTP mode binds to 127.0.0.1:8080 by default and does not enable cross-origin browser access. For container deployment, set AETRE_BIND_ADDRESS=0.0.0.0 and set a strong AETRE_HTTP_SERVER_TOKEN. Non-loopback startup fails closed when that token is absent. POST clients must send it in the X-AETRE-Server-Token header. Also place the service behind a TLS reverse proxy. The bundled Dockerfile supplies the bind address and runs as a non-root user.

Key MCP Tools Included:

1. aetre_calculate_voi: Core Bayesian Value-of-Information expected-utility calculation.

2. aetre_heavy_tailed_voi: Pareto power-law venture capital screening ($\alpha \approx 1.25$) for asymmetric bets.

3. aetre_author_preflight_benchmark: Pre-flight draft diagnostic evaluating reviewer disagreement and variance risk.

4. aetre_check_governor: Kingman queue utilization ($\rho$) delay forecasting and capacity governor actions.

5. aetre_congestion_matching: Optimal reviewer-paper bipartite matching under workload constraints.

6. aetre_sequential_stopping_rule: Wald sequential likelihood ratio multi-round review termination.

7. aetre_correlated_posterior_update: Multi-agent reviewer consensus correlation debiasing.

8. aetre_exploration_audit: Horvitz-Thompson unbiased audit estimator ($\hat{H}_D$) on rejected pools.

9. aetre_quadratic_staking: Super-linear anti-sybil staking curves to deter spam.

10. aetre_batch_triage: Bulk dataset triage and three-stream routing.

Open Engine vs. Enterprise Commercial SaaS

AETRE follows an Open Engine / Dual-Track Architecture:

Citation & Academic Reference

If you use AETRE in your research, peer-review systems, or investment analysis, please cite:

@article{gray2026innovation,
  title={The Innovation-Absorption Gap: How Artificial Intelligence Can Accelerate Idea Production Faster Than Complementary Institutions Adapt},
  author={Gray, Clayton},
  journal={SSRN Electronic Journal},
  year={2026},
  doi={10.2139/ssrn.7161458},
  url={https://ssrn.com/abstract=7161458}
}

License & Inquiries

This software is distributed under a Dual-License Model:

• Open-source option: The code is licensed under AGPL-3.0-or-later, including for commercial use, subject to the AGPL's terms.

• Commercial option: Organizations wishing to use AETRE without the AGPL's copyleft obligations may negotiate a separate written commercial license.

All bundled example datasets are synthetic test fixtures, not empirical validation corpora. See DATASETS.md before using or redistributing external data. Evaluation fingerprints emitted by the engine are deterministic reproducibility identifiers; they are not signed receipts or proof of external validation.

• Author & Maintainer: Clayton Gray

• Portal & Licensing: https://www.lithiumeel.com/aetre

• Inquiries: contact@lithiumeel.com | privacy@lithiumeel.com