PolyGnosis 2.0: Enhancing LLM Reasoning via Agentic Harness Engineering for Polymarket and OSINT Insight Extraction
arXiv:2605.25958v1 Announce Type: new Abstract: This paper introduces PolyGnosis 2.0, a pioneering multi-agent architecture designed to extract predictive intelligence by synthesizing Polymarket anomaly signals with global Open Source Intelligence (OSINT) streams, specifically Global Database of Events, Language, and Tone (GDELT). We define and target "Perspective Mismatches", the narrative divergence between Polymarket sentiment and global media flows, as high-alpha trading signals. Moving beyond generic agentic superiority, we rigorously quantify the efficacy of "Harness Engineering" techniq
The proliferation of advanced large language models is enabling more sophisticated multi-agent architectures that can synthesize disparate data streams for predictive insights.
This development indicates a significant leap in AI's ability to extract actionable intelligence from complex, real-world data, potentially impacting financial markets and geopolitical analysis.
The capability to identify "Perspective Mismatches" between sentiment and global events using AI agents provides a new class of high-alpha signals for strategic decision-making.
- · AI-driven hedge funds
- · Intelligence agencies
- · Predictive analytics platforms
- · Open Source Intelligence (OSINT) providers
- · Traditional market research firms
- · Manual intelligence analysis
- · Investors relying on lagging indicators
Automated systems gain a more nuanced understanding of global trends and sentiment.
The efficiency and accuracy of financial market predictions and geopolitical forecasting dramatically improve.
Enhanced AI-driven insight extraction could lead to greater market stability or, conversely, flash crashes due to rapid, automated reactions to perceived 'mismatches'.
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Read at arXiv cs.CL