DAEDALUS Research Convergence Matrices: Multi-Model Analysis of Cognitive Architecture Patterns for AI Agents

Research dataset containing two convergence matrices analyzing architectural patterns for cognitive AI agents, produced as part of the DAEDALUS research project.

METHODOLOGY:
Identical research questions about agent memory architecture and cognitive scaffolding patterns were independently posed to three to four AI models (Claude Opus 4.5, Gemini, ChatGPT, Mistral). Each model conducted literature- grounded analysis, and responses were systematically compared to identify convergent patterns (high confidence), divergent recommendations, and unique insights. Primary sources cited by the models were manually verified against original publications.

This methodology treats AI models as parallel literature survey tools, not as evaluators. The convergence is in the published literature the models reference, not in the models' opinions.

DOCUMENT 1: DAEDALUS Convergence Matrix (January 27, 2026) Three-source synthesis (Claude, Gemini, ChatGPT) covering 23 architectural patterns across 10 domains: memory architecture, reasoning, self-reflection, safety, multi-agent coordination. Includes tiered confidence levels, anti-pattern warnings, and gap analysis against 12 existing agent memory frameworks. Systems analyzed: Zep/Graphiti, MemGPT/Letta, Mem0, LangChain, Clawdbot/Moltbot, Stanford Generative Agents, EverMemOS, HippoRAG, Reflexion, CASS, Omi, LobeHub.

DOCUMENT 2: Cognitive Scaffolding Convergence Matrix (February 2, 2026) Four-source synthesis (Claude, Gemini, ChatGPT, Mistral) covering 21 cognitive scaffolding patterns from SOAR, ACT-R, CLARION, LIDA, SOFAI architectures, Extended Mind thesis, Distributed Cognition, and Cognitive Tools research.

KEY FINDINGS:
- 100% convergence (4/4 models) on: propose-validate separation, temporal memory, trust gradients, failure-indexed memory, bounded working memory, metacognitive monitoring, episodic/semantic distinction
- 0/12 production memory systems implement trust-level differentiation between verified facts and AI-generated hypotheses
- Three critical gaps identified: global workspace/competitive attention, consolidation/forgetting processes, visual reasoning scratchpads

This gap analysis directly motivates the DAEDALUS dual-memory architecture with tri-color trust model (blue: human-verified, green: AI-suggested, red: constraint violations).

Parent project: DAEDALUS (Cognitive AI Agent Architecture)
Institution: University of Oradea, Romania