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Curcumin, Depression, and Seed Oils: A Surprising Connection

Tucker Goodrich

Substack·2026

Sometimes a model is a thing of beauty, sometimes it just makes you sad. This one is both.

★ 3.0 (1)

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Emily Wilson · An Uncomplicated Man

Emily Wilson

London Review of Books·2026

As they say at the awards shows, I was humbled to learn that Christopher Nolan has read at least the first line of my...

★ 5.0 (1)

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Feminist Approaches to Communication Policy

Leslie Regan Shade, Tamara Shepherd

Oxford Research Encyclopedia of Communication·2026

Summary Communication policy is broadly construed as the principles, processes, and procedures of various legal actions (legislation, court orders, or policy directives) that govern the diverse uses of communication resources at the global, national, or community level. Policy can be public (governmental-national or global) or private (corporate) and can involve formal or informal mechanisms. Policy is an increasingly important site of deliberation and contestation as digital technologies have become essential infrastructures in people’s everyday lives. Policy is also important to examine as a site where social values get debated and codified. Given this normative function of policy, feminist approaches offer perspectives through which to ask whether and how communication policy is serving people equally well. Important policy moments over the past several decades have influenced the ways that gender is framed in communication policy. The emergence of global digital policy issues provided the backdrop for these influential initiatives, including the New World Information and Communication Order, the Beijing Declaration and Platform for Action, and the World Summit on the Information Society. In addition, feminist work has been burgeoning in the realm of artificial intelligence policy, an area that urgently needs the attention to rights, equality, and justice that are the hallmarks of critical intersectional feminist critique.

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What Is Lost When We Make 'The Odyssey' Modern

Derek Thompson

Substack·2026

Christopher Nolan's 'Odyssey' is an extraordinary, modern epic. Homer's Odyssey is an even more extraordinarily strange one.

★ 5.0 (1)

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Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI

Joshua A. Kroll, Andrew Smart, R. Stuart Geiger, Abigail Z. Jacobs

arXiv·2026

As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology's capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems. Sociotechnical analysis of risks in highly complex systems provides clear lessons for the design and evaluation of AI systems, transcending a technical focus on reliable or "responsibly designed" components to understand risks at a systems level. Human-made catastrophes have been studied for decades because of the severity of these events: consider Chernobyl, Three Mile Island, Fukushima-Daiichi, Bhopal, the Challenger disaster. A common misconception is that these kinds of events are freak accidents, resulting from the inherently unforeseeable interactions in complex systems. Closer examination reveals that the risks and hazards were well-known beforehand but not acted upon due to social structural, political and economic factors. We outline several areas where the development and use of AI can benefit from learning these unlearned lessons: improved risk perception, communication, and analysis at the organizational level; traceability of requirements and responsibilities; and holistic approaches to responsibility and safety that include social and organizational dynamics as first-order engineering concerns. For each area, we offer concrete unlearned lessons and exemplify how they led to failure in prior accidents as well as examples of how these lessons remain unlearned for modern computing systems, particularly AI.

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Optimal Top-$k$ Identification from Pairwise Comparisons

Motti Goldberger, Nils Rudi

arXiv·2026

We study the active learning problem of fixed-confidence top-$k$ identification from noisy pairwise comparisons. In this problem, an algorithm sequentially chooses pairs of items to compare, observes the outcomes, and stops when it can return the set of top-$k$ items with error probability at most $δ$. The objective is to design such a $δ$-correct procedure that minimizes the expected number of comparisons (the sample complexity). This problem falls within the broader literature on fixed-confidence pure exploration in bandit models, where a common target is asymptotic optimality: the algorithm's expected sample complexity matches the information theoretic lower bound as $δ\to 0$. Asymptotically optimal procedures have been developed for a range of fixed-confidence pure-exploration problems, however to the best of our knowledge, for top-$1$, or more generally top-$k$ identification from pairwise comparisons under latent utility models an asymptotically optimal algorithm has not been established. In this setting, we develop such an algorithm. We characterize the structure of the lower bound and formulate it as a saddle-point problem. This structure enables a computationally efficient primal-dual procedure that learns the asymptotically optimal comparison allocation online. We then construct an adaptive comparison-allocation algorithm that tracks the allocation learned by the primal-dual procedure and prove it is asymptotically optimal.

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Validating LLMs in social science: Epistemic threats and emerging norms

Meera Desai, Dallas Card, Abigail Z. Jacobs

arXiv·2026

Large language models (LLMs) are reshaping social science methodology. Researchers increasingly prompt language models to generate quantitative measurements of social concepts, for example labeling data or simulating survey responses. Yet LLMs pose methodological challenges including bias, hallucination, and brittleness across contexts, with unclear threats to validity. Standard practices and norms for addressing these challenges are still emerging. We collect and systematically analyze validation practices in a comprehensive corpus of papers from eight flagship social science journals that use LLMs as measurement instruments. We find that LLM-generated measurements frequently play a central role in empirical analyses, yet validation practices are inconsistent and limited. We outline complementary strategies for more robust validation, pointing toward better norms and standards around the use of LLMs in social science.

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Your personalresearch library

All Papers

Curcumin, Depression, and Seed Oils: A Surprising Connection

Emily Wilson · An Uncomplicated Man

Feminist Approaches to Communication Policy

What Is Lost When We Make 'The Odyssey' Modern

Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI

Inkling: Our Open-Weights Model - Thinking Machines Lab

Optimal Top-$k$ Identification from Pairwise Comparisons

Validating LLMs in social science: Epistemic threats and emerging norms

Your Book Review: The Book Of Abraham

Minutes of the Federal Open Market Committee, June 16–17, 2026

N+1

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