The Airecmark Evaluation Protocol: Quantitative Methodology & Scientific Charter
Standardized specifications governing empirical scoring for generative code systems, autonomous developer agents, and multimodal reasoning engines. We enforce zero commercial bias, isolated Tree-sitter AST syntax validation, microsecond network packet telemetry, and air-gapped test runners.
The 4 Ironclad Laws of Airecmark Intelligence
Zero-Sponsored Placement
Vendor spend, sponsorship funds, cloud partnerships, or corporate affiliation can never adjust an Airecmark Score, recalibrate test weights, or modify ranking index position.
Deterministic Execution
Zero subjective qualitative "LLM-as-a-judge" grading. Every synthetic diff and model patch is built, compiled, and unit-tested in isolated ephemeral microVMs against native test suites.
Production Monorepo Scale
Zero synthetic single-file coding exercises. Tests require traversing inter-package dependency graphs in codebases exceeding 100k lines of active TypeScript, Rust, and C.
Cryptographic Attestation
Every evaluation execution log, token logit variance, and compiler stdout dump is hashed and committed to an open, append-only Merkle ledger for third-party recreation.
Composite Score Mathematical Engine
The overall Airecmark Composite Score (0.0 to 10.0) is a weighted vector computed from four deterministic test clusters.
AST Correctness & Dry-Run Execution (SAST)
Evaluates syntax tree validity and operational execution using Tree-sitter grammar parsers. Zero tolerance for imaginary package imports, incorrect argument arity, or broken interfaces.
Context Needle Recall @ Scale (SContext)
Measures precision of multi-file graph resolution across 128k to 200k active context tokens. We inject buried interface definitions across distant sub-modules to verify dependency propagation.
TTFT & Streaming Velocity (SLatency)
Real-time TCP socket telemetry measuring packet arrival delays, Time-To-First-Token (TTFT), and sustained token stream throughput (tok/s) sampled concurrently from 4 global test points.
Ergonomics & Atomic Diff Reversibility (SErgo)
Calculates the cognitive friction and engineering burden of AI code suggestions. Models lose points for generating multi-hundred-line formatting diffs that clobber unrelated git blame records.
Interactive Composite Score Simulator
Adjust benchmark sub-scores to inspect real-time mathematical aggregation.
Isolated Sandboxing & Hardware Cluster Pipeline
High-throughput test runs execute in zero-network Firecracker microVMs launched on dedicated bare-metal iron.
4x Equinix Metal c3.medium instances dispatch parallel WebSocket and REST streams to capture raw network latency.
TCP RTT: ~4.2msGenerated patches are applied inside ephemeral Firecracker MicroVMs with strictly blocked outbound egress.
Egress: 0 bytes permittedParsers traverse concrete syntax tree deltas, isolating changed semantic nodes and verifying contract boundaries.
Grammars: TS, Rust, Python, GoRaw output logs, AST diff reports, and runtime execution codes are concatenated, signed, and broadcast to the public index.
Algorithm: Ed25519| Corpus Identifier | Target Ecosystem | Total Scale | Dependency Topology | Pass Criterion |
|---|---|---|---|---|
| CORPUS-TS-POLYGLOT-182 | TypeScript 5.8 / Node 22 / Turborepo | 182 Pkgs / 284k LoC | Multi-tier circular interfaces | tsc exit code 0 |
| CORPUS-RUST-DISTRIB-DB | Rust 1.85 / Tokio / Raft consensus | 34 Crates / 112k LoC | Async lifetime borrow checking | cargo clippy -- -D warnings |
| CORPUS-KERNEL-C-SUBSET | C11 / Linux VFS / eBPF filters | 16 Subsystems / 410k LoC | Pointer safety / Memory boundaries | ASan + Valgrind 0-leaks |
| CORPUS-PY-DATA-DISTRIB | Python 3.12 / Polars / PyO3 FFI | 12 Modules / 95k LoC | Cross-language FFI type signatures | pytest (100% assertions) |
Scoring Normalization & Anti-Gaming Defense
Foundation models frequently memorize public benchmark datasets. We utilize runtime mutation and prompt perturbation to make benchmark memorization mathematically impossible.
Prior to injecting an evaluation task into a model context, our compiler synthetically transforms all private internal variable names, interface labels, and module paths with randomized semantic synonyms. Models that memorized canonical GitHub repository solutions immediately trigger syntax compilation failures.
Every discrete task is evaluated across 15 semantically identical but syntactically divergent instruction variants. If a model passes prompt version A but fails version B and C, a logit stability penalty is automatically deducted from the final score to isolate prompt brittleness.
Commercial API endpoints are pinged continuously every hour using rotating private synthetic canaries. When an AI vendor silently updates weights or applies quantization degradation behind a static endpoint name, our telemetry flags the regression within 60 minutes.
Frequently Addressed Protocol Inquiries
Verify Test Executions Independently
Download the standalone Docker Compose evaluation harness, sanitized AST test definitions, and signed raw JSON telemetry captures to verify any published score on your own hardware.