Skip to content

DARPA_MASTER_AUDIT_2026-04-14.md

Latest commit

 

History

History
48 lines (42 loc) · 2.46 KB

DARPA_MASTER_AUDIT_2026-04-14.md

File metadata and controls

48 lines (42 loc) · 2.46 KB
Raw

DARPA Master Audit — Cognition Core

Current state

Cognition Core is a strong alpha control shell with a small real Python core and a much larger doctrine/report/config shell around it.

Real strengths

  • Clean repo structure
  • Reproducible packaging and release bundle path
  • Clear adapter boundary
  • Benchmark and scoring flow exists
  • Promotion flow exists
  • GGUF/backend intent exists
  • Structural validation is good

Main weak points before patching

  • Adapter responses were too thin to support real gate logic
  • Live backend verification could pass without a real live backend
  • Benchmark execution had no per-task failure structure or run ID
  • Scoring was keyword-driven rather than task-aware
  • Promotion ledgering was too thin for trustworthy comparisons
  • Full candidate gate skipped the declared backend check step

What was patched in this pass

  • Expanded ModelResponse with status, error, latency, token, and backend identity fields
  • Hardened llama_cpp_http adapter for real GGUF/OpenAI-style local inference handling
  • Hardened OpenAI-compatible adapter similarly
  • Marked heuristic adapter as synthetic and non-promotable
  • Added adapter normalization and cleaner factory routing
  • Added benchmark task validation during loading
  • Replaced generic keyword scoring with task-aware capability scoring
  • Upgraded backend check into a real gate with smoke generation
  • Added run IDs, failure capture, and richer records to benchmark execution
  • Upgraded score pipeline to bind scored outputs back to benchmark tasks
  • Upgraded promotion entries with candidate/run metadata, failure rate, latency summary, and deduping
  • Fixed full candidate gate ordering so backend verification is no longer skipped
  • Strengthened repo validation with benchmark schema and run-manifest coherence checks
  • Strengthened tests so heuristic can no longer masquerade as a required live backend

Remaining non-trivial gaps

  • Training/export chain is still mostly placeholder-level
  • Quantization retention still needs real full-precision vs GGUF task comparison
  • Corpus builders for external repos are not yet implemented
  • Long-horizon workflow benchmarks are not yet added
  • Tool adapters are still minimal shells rather than hardened production integrations

Correct product truth after this pass

Cognition Core is now a more honest and more operational alpha lab shell. It is not yet a complete production cognition system, but it is materially closer to being a trustworthy local candidate lab.