The P = NP problem is considered the Holy Grail of computer science. The dogmatic assumption: NP-hard problems require exponential search times.
My new data proves: The problem lies not in complexity, but in representation.
In my new preprint "NP-Hardness Collapsed: Deterministic Resolution of Spin-Glass Ground States via Information-Geometric Manifolds (Scaling from N=8 to N=100)" I demonstrate a mechanism that allows Spin-Glass state spaces to deterministically collapse.
The Performance Benchmark:
Total runtime for N=100: 90 minutes.
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But here is the sensation: In this single 90-minute run, every intermediate solution from N=2 to N=100 was solved simultaneously.
The Facts:
✅ Speed: 90 Minutes for the full spectrum (N=2...100).
✅ Exactness: Deterministic verification, not probabilistic approximation.
✅ Scalability: Information-geometric field dynamics that encompass all sub-solutions instantly.
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It needs no searching algorithms. It only needs information and geometry.
Anyone claiming I have 'solved P=NP' is arguing against a straw man; I am demonstrating that specific NP-hard representations can be bypassed geometrically.
As explicitly stated in my paper 'NP-Hardness Collapsed: Deterministic Resolution of Spin-Glass Ground States via Information-Geometric Manifolds' on Page 32: '๐๐ฉ๐ช๐ด ๐ฅ๐ฐ๐ฆ๐ด ๐ฏ๐ฐ๐ต ๐ณ๐ฆ๐ด๐ฐ๐ญ๐ท๐ฆ ๐ต๐ฉ๐ฆ ๐=๐๐ ๐ฑ๐ณ๐ฐ๐ฃ๐ญ๐ฆ๐ฎ...' – but it opens a perspective that may lead to a new understanding. Perhaps the P = NP or P ≠ NP question cannot be resolved in its classical formulation, but the underlying structure can be addressed geometrically.
Information is all it needs – geometry follows.
๐ Read the Preprint here:
DOI:10.20944/preprints202512.0207.v2
Link: NP-Hardness Collapsed: Deterministic Resolution of Spin-Glass Ground States via Information-Geometric Manifolds (Scaling from N=8 to N=100)[v2] | Preprints.org
Physics ArtificialIntelligence InformationGeometry
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