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Home » "Science" » Computer Science & AI

Computer Science & AI

Unified Field Theory of the Large Language Manifold

Unified Field Theory of the Large Language Manifold

by Marcus Hermansson | Nov 17, 2025 | "Science", Computer Science & AI, Social & Behavioral Science

The Large Language Manifold A New Lens on Language and Cognition We keep pretending the interesting object is “the model”: a big neural net in a datacenter that you poke with prompts and read answers from. That picture is already obsolete. As soon as we plugged these...
The Geometry of Wisdom in Artificial Minds

The Geometry of Wisdom in Artificial Minds

by Marcus Hermansson | Nov 10, 2025 | "Science", Computer Science & AI

The Geometry of Wisdom in Artificial Minds Part III — returning with meaning. This piece completes the trilogy: Intuition finds the bend, Creativity explores the branch, and Wisdom returns with meaning. Read more on OSF: https://osf.io/w9b4e/ TL;DR One space of...
The Geometry of Creativity in Artificial Minds

The Geometry of Creativity in Artificial Minds

by Marcus Hermansson | Nov 7, 2025 | "Science", Computer Science & AI

Part II — turning the same machinery into a creative engine. Status: Concept proposal (unvalidated). More materials (work-in-progress): https://osf.io/wprfm/ TL;DR We turn “intuition” into stable creativity: Spot Conceptual Inflection Points (CIPs) — rungs where a...
The Geometry of Intuition in Artificial Minds

The Geometry of Intuition in Artificial Minds

by Marcus Hermansson | Nov 6, 2025 | "Science", Computer Science & AI

A proposal for teaching models when to zoom and where to jump. Status: Concept proposal (unvalidated). More materials (work-in-progress): https://osf.io/85z3u TL;DR We propose a method to teach AI something like intuition: Know when to zoom — use more representational...
Why LLMs Fail at Chess: Lessons from the Kaggle Tournament

Why LLMs Fail at Chess: Lessons from the Kaggle Tournament

by Marcus Hermansson | Aug 11, 2025 | "Science", Computer Science & AI

TL;DR: The midgame meltdowns weren’t random. They came from three predictable causes—state drift, context dilution + recency bias, and a training-data gap beyond the opening. Kaggle already sent a canonical board state (FEN) every turn and enforced legality. What...
Why We Built AGI Framework 3.0: Democratic, Transparent, and Secure by Design

Why We Built AGI Framework 3.0: Democratic, Transparent, and Secure by Design

by Marcus Hermansson | May 18, 2025 | "Science", Computer Science & AI

The Why Let’s cut through the hype: AGI isn’t just a question of clever code or “bigger models.” It’s a problem of how intelligence organizes itself—how decisions are made, who gets a say, what “truth” means, and how power is kept in check. If you don’t tackle those,...
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