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 — 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 — Queries span Claude, GPT, Gemini, and other frontier models to capture consensus, disagreement, and model-specific blind spots
- Design decision grounding — 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 — Findings are explicitly categorized as validated (convergent evidence), contested (model disagreement), or open (insufficient evidence), preventing premature certainty
- Reusable evidence artifacts — 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 — six gaps identified across variants. RSY-030 added: cross-project experimental rigor protocol (P1).
Status
Active — blocked on operator remediation (fill A2, E2, D1/D2 evidence gaps). Unblocked items: A×D cross-cutting synthesis, metacognitive platform scoping memo.
Links
MISSING — Repository URL