Harmonising LLM Agents for Real-World Market Intelligence:[...]
summary
Claims a concrete pipeline called Decision Alignment Protocols that coordinates domain agents (financial, regulatory, sentiment) via intracluster consensus, intercluster negotiation and a confidence‑weighted synthesis. Implementation uses LLM agents (mention of Grock and GPT‑style models), structured debating, voting and numeric weights; it reports 99.7% alignment, 91.2% accuracy versus 85.1% baseline, a 6.1% sustained advantage and stability over 24 months. The system is built as a flowcharted agent orchestration with scoring and rules; tradeoffs are added complexity and potential weighting bias unless governance is enforced.
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chapters (8)
- 0:00LLM agents and DAPs
- 2:33Isolated AI agents fail decisions
- 4:24How DAPs work
- 5:49Multi-perspective conflict resolution
- 7:15Case study market conflicts
- 9:51DAPs performance metrics
- 11:39Risks of DAPs
- 12:22Conclusion
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