Autonomous research infrastructure
Multi-agentic research infrastructure for idiosyncratic evidence.
Gothos is being developed as a highly autonomous system of locally trained research agents, using proprietary data to identify emerging evidence, evaluate probabilistic theses, and improve across macroeconomic, sectoral, and thematic domains.
About / Method
Locally trained agents. Adversarial by design.
The end-state is a private, locally trained, multi-agentic system that applies a consistent methodology across signal discovery, thesis generation, adversarial review, and historical evaluation. Human oversight remains strategic; the operating model is designed to preserve institutional memory and compound research judgment over time.
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01
Signal discovery
Specialized agents evaluate emerging evidence across technical, commercial, institutional, and market-adjacent contexts for signs of changing constraints and beliefs.
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02
Thesis generation
Candidate themes are converted into structured probabilistic theses with defined assumptions, time horizons, and conditions for revision.
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03
Adversarial review
Separate agents challenge the thesis, surfacing counterevidence, base-rate errors, confounders, and narrative overfit.
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04
Scoring and memory
Judgments and outcomes are retained in a private feedback loop so the system can improve without overstating precision or certainty.
Operating principles
Gothos is not a dashboard, scraper, or trading bot. It is as a highly autonomous, locally trained, multi-agentic research system for alpha generation and instrumental execution.
The objective is to capture discretion through structure, test conviction against evidence, and support repeatable identification of emerging themes before they become consensus.
Focus areas
Current domains.
- Macroeconomic regimes
- Thematic AI
- Structural bottlenecks
- Market reflexivity
- Legal and institutional signals
- Market behavioural regimes