Research that makes company agents safer,
faster, and more useful.
This archive supports Astro One delivery: retrieval quality, orchestration behavior, approval boundaries, latency, and the technical choices that decide whether an agent can be trusted with real work.
Research is kept when it improves retrieval, orchestration, evaluation, or workflow execution.
Architecture, benchmarks, and failure modes are more important than trend summaries.
The archive should help decide what gets shipped, constrained, or rejected.
Research stays public only when it clarifies a build decision, a tradeoff, or a repeatable agent pattern.
Every paper should surface architecture, evaluation logic, or technical tradeoffs clearly enough to act on.
The archive strengthens agent delivery decisions; it is not a disconnected lab notebook.
Trusted by leaders across finance, healthcare, infrastructure, and AI operations
Multi-agent calendar intelligence: hybrid LLM + CP-SAT for executive scheduling
Executive calendar management represents a constraint satisfaction problem characterized by high dimensionality, conflicting objectives, and dynamic updates. Traditional LLMs fail on complex scheduling (0.6% success on TravelPlanner). This research introduces the Cognitive Temporal Orchestration (CTO) framework—a hybrid architecture integrating heterogeneous LLM orchestration (GPT-5, Gemini 3 Pro, Claude Sonnet 4.5) with CP-SAT constraint programming. Through 81 test scenarios, we demonstrate 100% orchestration success, 100% high-value event identification, and 29.4% cost reduction. Critical analysis reveals 99% of latency originates from LLM inference, fundamentally informing optimization strategies. We validate three cognitive modules establishing a methodology for evaluating evolution from reactive assistants to proactive wealth management systems.
A structured archive with clearer status, category, and reading intent.
Instead of another card shelf, the archive works more like a research ledger. Readers can compare category, maturity, and intent at a glance.
Comparative LLM analysis for clinical decision support: routing across Gemini-3-Pro and GPT-5.1
This comprehensive evaluation of the Vitruviana Hybrid AI Architecture for clinical decision support analyzes model selection patterns, service integration, and clinical outcomes across 100+ automated tests. The hybrid architecture achieved 94.7% system reliability with intelligent task routing, demonstrating 100% optimal routing decisions and directing complex clinical reasoning to Gemini 3 Pro (67% of tasks) and structured tasks to GPT-5.1 (33% of tasks).
Enterprise AI Transformation: A Strategic Framework for 2025-2030
This whitepaper provides enterprise leaders with a comprehensive framework for AI transformation, covering strategy, implementation, risk management, and ROI optimization. Based on real-world deployments across 2,000+ organizations.
The archive exists to tighten the connection between experimentation and production systems.
Every strong paper in the archive clarifies a design choice, reveals a benchmark, or exposes a system tradeoff that directly shapes delivery work.
Define the question
Each paper starts with a technical or operational question that matters to delivery, not a generic trend narrative.
Document the method
Architectures, benchmarks, failure modes, and constraints are surfaced clearly enough for operators to evaluate.
Connect to application
Research only earns its place when it clarifies how Astro designs agentic systems, retrieval pipelines, or workflow intelligence in practice.
If a research question maps to work your team still does manually, the next step is usually delivery.
We use the archive to sharpen architecture, evaluation, and decision quality before a company agent touches production.