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Have you tried switching it off and on again? Mechanisms and therapeutic prospects of resetting homeostatic set points in medicine and neuropsychiatry
Thomas Pollak, Michael Levin, Anjali Bhat, Matt Butler, Rok Berlot, Mark J Edwards
osf.io
Many chronic neurological, psychiatric and interface disorders can be understood as conditions where maladaptive homeostatic set points become entrenched, driving pathological states that resist spontaneous resolution. This paper proposes a novel conceptual framework, the therapeutic reset, which unifies diverse interventions that transiently disrupt pathological equilibria, thereby leveraging the intrinsic plasticity of biological systems to facilitate recalibration toward healthier set points. Drawing insights from active inference, dynamical systems theory and nested somatic collective intelligences, we illustrate how treatments such as electroconvulsive therapy, psychedelics, inflammatory perturbations and anaesthetic interventions create brief periods of heightened plasticity.
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View paper →My response to AI 2027
Vitalik Buterin
vitalik.eth.limo
In April this year, Daniel Kokotajlo, Scott Alexander and others released what they describe as "a scenario that represents our best guess about what [the impact of superhuman AI over the next 5 years] might look like". The scenario predicts that by 2027 we will have made superhuman AI and the entire future of our civilization hinges on how it turns out: by 2030 we will get either (from the US perspective) utopia or (from any human's perspective) total annihilation.
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View paper →How to Correctly Report LLM-as-a-Judge Evaluations | alphaXiv
Unknown Author
alphaxiv.org
View 1 comment: Thank the authors for their insightful paper.
Can q\_1/q\_0 vary by question difficulty?
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View paper →Robust AI Personalization Will Require a Human Context Protocol
Anand Shah, Tobin South, Talfan Evans, Hannah Rose Kirk, Andrew Trask, E. Glen Weyl, Michiel Bakker
papers.ssrn.com
Robust AI personalization will require a Human Context Protocol (HCP): a user-owned, secure, and interoperable preference layer that grants individuals granular, revocable control over how their data steers AI systems. By replacing siloed, behavior-inferred signals with direct preference articulation, HCP unifies fragmented data, lowers switching costs, and enables seamless portability across AI services, fostering a more competitive ecosystem. This paper outlines core design principles-natural-language preference storage, scoped sharing, and strong authentication with revocation-that extend earlier personal-data architectures to the scale and stakes of modern generative AI. Centering control in users, HCP is not merely a technical convenience but a necessary foundation for AI systems that are genuinely personal, interoperable, and aligned with diverse human values.
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