The papers and people behind this.
The collapse-models field has a small, dense literature. These are the ones the AI has been told to know about — starting points, modern bounds, methodologies we borrow.
Foundational collapse models
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Ghirardi, Rimini, Weber (1986) — Unified dynamics for microscopic and macroscopic systemsGRW
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Pearle (1989) — Combining stochastic dynamical state-vector reduction with spontaneous localizationCSL
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Ghirardi, Pearle, Rimini (1990) — Markov processes in Hilbert space and continuous spontaneous localizationCSL
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Diósi (1989) — Models for universal reduction of macroscopic quantum fluctuationsDP
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Penrose (1996) — On gravity's role in quantum state reductionDP
The comprehensive review
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Bassi, Lochan, Satin, Singh, Ulbricht (2013) — Models of wavefunction collapse, underlying theories, and experimental tests, Rev. Mod. Phys.RMP
Modern experimental bounds
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Bose et al. (2017) — Spin entanglement witness for quantum gravity, PRLkg
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Adler & Bassi (2018) — Collapse models with non-white noises: cosmological-scale evidence for the CSL X-ray boundX-ray
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Fein et al. (2019) — Quantum superposition of molecules beyond 25 kDa, Nature Physicsmatter
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Donadi et al. (2021) — Underground test of gravity-related wave function collapse, Nature PhysicsDP-X-ray
Methodology we borrow
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The sibling project — Emergent Gravity — uses the same orchestrator+bandit+reviewer patternlive
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Thompson sampling — Russo & Van Roy (2014) — Learning to optimize via posterior samplingbandit
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Decoherence — Zeh, Zurek, Joos & Zeh — explains what collapse models still don'tcontext