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Research Series

Table of Contents

What it does

Systematic research evidence base that uses dual-query methodology across 11+ frontier AI models. Grounds design decisions for interagent, mlx-triage, and vLLM-MLX with structured evidence rather than intuition.

Architecture / Key capabilities

  • Dual-query methodology ā€” Every research question is investigated with both confirmatory and disconfirming queries, forcing the evidence base to surface contradictions rather than confirmation bias
  • 11+ frontier model coverage ā€” Queries span Claude, GPT, Gemini, and other frontier models to capture consensus, disagreement, and model-specific blind spots
  • Design decision grounding ā€” Each finding maps back to a specific architectural or protocol decision in interagent, mlx-triage, or vLLM-MLX, keeping research connected to implementation
  • Validated vs. open question separation ā€” Findings are explicitly categorized as validated (convergent evidence), contested (model disagreement), or open (insufficient evidence), preventing premature certainty
  • Reusable evidence artifacts ā€” Research outputs are structured for re-query and extension as new models or versions become available

Key numbers

  • 11+ frontier AI models queried
  • Dual-query approach (confirmatory + disconfirming) per research question

Current phase

Corpus QA and Re-Synthesis (blocked on operator dispatch) + cross-project rigor survey. First-round experimental rigor survey completed ā€” six gaps identified across variants. RSY-030 added: cross-project experimental rigor protocol (P1).

Status

Active ā€” blocked on operator remediation (fill A2, E2, D1/D2 evidence gaps). Unblocked items: AƗD cross-cutting synthesis, metacognitive platform scoping memo.

MISSING ā€” Repository URL