Abstract Using 2.6 billion geolocated tweets (2014–2022) and a fine-tuned generative language model, we construct county-level indicators of life satisfaction and happiness for the United States. We document an apparent rural–urban paradox : even in unadjusted county-level means, rural counties express higher life satisfaction while urban counties exhibit greater happiness . This opposite gradient persists and is further characterized once the two are treated as distinct layers of subjective well-being, evaluative vs. hedonic, showing that each maps differently onto place, politics, and time. Democratic-leaning areas show a suggestive negative association with evaluative well-being, conditional on structural and temporal controls, but this effect is modest and does not extend to happiness, where no meaningful partisan gradient emerges. Temporal shocks dominate the hedonic layer: happiness falls sharply during 2020–2022, whereas life satisfaction moves more modestly. These patterns are robust across logistic and OLS specifications with clustered standard errors and align with well-being theory. Interpreted as associations for the population of geolocated tweets, the results show that large-scale, language-based indicators can help clarify why prior findings about the rural–urban divide may differ by distinguishing the type of well-being expressed, offering a transparent, reproducible complement to traditional surveys.
A production platform built on large language models makes two kinds of decision, and most of its trouble comes from writing both into one clause. An optimization decision improves an objective: lower latency, lower cost, higher quality, fewer tests run. A boundary decision fixes a constraint that may not be relaxed for any gain: a residency rule, a least-privilege scope, a human-review threshold. When the two share a clause, improving one silently erodes the other, which is why efficiency and accountability are so often reported as a trade. This specification is built on one invariant: a boundary is a clause the optimizer may not cross, and everything else is optimization. The contribution is a cross-layer architectural method for separating non-negotiable constraints from adaptive optimization and binding both to reconstructable evidence, applied identically across model routing, agent orchestration and AI-native delivery. The seventeen patterns are instances of that method rather than the contribution itself. Each pattern is specified in the classical pattern form and carries three architectural declarations: the boundary it fixes, the optimizer it frees, and the evidence proving the boundary held. Every boundary is assigned to one of five classes covering data, authority, decision, resource and process constraints. Section 3 states the derivation method by which candidates were admitted or rejected, and publishes the rejections alongside the admissions so that the criterion can be examined rather than trusted. Three mechanisms make the language operate as a language rather than a list. A pattern relationship graph names which pattern supplies the artifact, evidence or authority another depends on, including the single cycle by which a workflow improves from its own structural record and the economic chain running the full height of the stack. A normative event identity, with rules for causal parentage, retries, provider boundaries and retention, turns the requirement that evidence be joinable into something an implementation can satisfy or fail. And per-pattern applicability conditions replace categorical requirements, so that a pattern governing a mechanism an institution does not operate is out of scope rather than a gap. Conformance is self-declared and published as a profile carrying the environment, the applicable set, per-pattern status, an evidence date and documented gaps. It is not a certification scheme, and no conformity assessment body operates against it. The contribution is architectural rather than empirical. Every pattern carries an evidence level, and no pattern reaches the highest level, because no implementation unconnected to the author has been evaluated. Nothing has been measured. The specification separates what would falsify the invariant from what would falsify an individual pattern and from what would falsify the composition and adoption sequence, poses six research questions, and records the absence of a real implementation profile as a known deficiency of version 1.0. An appendix reconciles the pattern identifiers with the names used across the author's papers and companion book series, including the acronyms PEVG and PARA, so that the two bodies of work can be cited as one. Version 1.1 names two constructs the specification already contained. The central proposition is named the Boundary Invariant, and the three architectural declarations required of every pattern are together named the BOE Declaration. Neither carries a trademark, both are offered for use with attribution under this document's licence, and neither changes any requirement: the proposition, its wording and its priority date are those of version 1.0. Section 11 gains the two-family naming convention and a precedence rule fixing which document governs where this specification and the Defensible AI Framework Registry describe the same relationship.
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.