Humanities & Social Sciences

The authors built MIST (the Misleading-Image Stress Test), 200 English sentences each containing a phrase readable either figuratively or literally and shown with an aligned image, a misleading image, or no image; across thirteen VLM judges an aligned image changed 20.5% of labels and a misleading one 19.4%, versus 11.6% when only the ignore-the-image instruction was deleted with the image left in place, and only 37% of the labels that differ between the two images moved toward the sense shown, while agreement with the human annotators was unchanged whether the image was absent, aligned, or misleading.

The work proposes SMART, a self-evolving multi-agent system for long-form subtitle translation: during test-time training it builds persistent series-level memory and translates a subset of sentences through a dynamic graph router and a Mixture-of-Agents layer with tools for terminology verification, subtitle constraint validation, and contextual retrieval, while a judge-refiner loop scores candidates and back-propagates textual critiques that refine agent prompts and the routing policy without retraining the underlying LLMs; during test-time inference the evolved configuration translates the remaining series, and the paper also introduces Subtitle Arena, covering 14 genres, 2–198 episodes per series, production years 1959–2023, and 15 target locales, together with SubMQM, a subtitle-adapt

In this guest post, Rachel Webb draws on her own mathematical research experience to argue that LLMs let her execute research faster and turn some of her lands of mathematical fantasy into worlds she can realistically start exploring, while her metric for mathematical interest stays unchanged and humans keep doing math for two humanistic reasons: math is interesting to us individually, and math creates communities.

In an NBER working paper, Harvard Kennedy School economists Doug Elmendorf and Karen Dynan with Brookings's Louise Sheiner lay out four scenarios for AI's economic impact, ranging from a moderate GDP boost with little reduction in worker numbers to much faster GDP growth with persistently high unemployment, and estimate that their AI scenario leaves about 3 million people, roughly 2 percent of the labor force, out of work at any given time; separately, Harvard Business School's Joseph Fuller, working with Accenture Research, developed an AI model finding that 41 percent of all work tasks can today be automated or augmented by AI, while only about one-third of firms' AI experiments succeed, which helps explain why mass layoffs have not yet appeared.

The authors define and formalize behavioural side-channel leakage in browser-use agents, introduce the AgentTell benchmark of 20 scenarios and 100 tasks, and evaluate six backbones across 9,760 sessions, finding that agents carrying a secret reveal it through task-directed action choices in 61.1% of sessions despite an explicit privacy instruction, that agents still leak in 56.7% of sessions where their own memory states the secret must not be shared, and that in 34.5% of leaking sessions their final responses falsely assure users that no information was disclosed.

The study introduces Hyperspherical Semantic Trajectory Analysis (HSTA), an unsupervised pipeline that encodes 20,000 arXiv preprints and 10,000 USPTO patent abstracts with Sentence-BERT, projects them onto a unit hypersphere, clusters them into eight sub-topics with Spherical K-Means alongside UMAP reduction, and defines two metrics, Semantic Centroid Vector Drift and Commercialization Offset, then links them to Epoch AI compute data through Vector Autoregressive Granger tests, finding that Large Language Models (drift 0.332) and Artificial Intelligence Systems (0.234) evolve fastest semantically while quarterly paper volume velocity alone does not Granger-cause frontier training compute surges at conventional significance levels.

In her new book 'Artificial Intimacy: Who We Become When We Talk to Machines,' MIT professor of the social studies of science and technology Sherry Turkle draws on surveyed evidence and new interview research to examine chatbots across the stages of human life, concluding that chatbot use, though often felt as a short-term salve, is broadly detrimental to human development and social connectivity, and calling for a pushback movement akin to those against phones in schools and youth social media use.

In Philosophical Perspectives, MIT's Brian Hedden and Manish Raghavan systematically evaluate major objections to algorithmic monoculture—where one algorithm makes all decisions in a domain—arguing that objections such as systematic exclusion do not hold, proving mathematically that monoculture tends to create information echo chambers that hinder exploration, and showing through a series of hiring simulations that bundling algorithms into an "ensemble" can sometimes overcome this limitation so that monoculture performs as well as or better than polyculture.

Using 16,905 different-speaker pairs from VoxSim, the study builds a human perceptual alignment metric and systematically compares seven training objectives across five model conditions, finding that verification accuracy (EER) does not track human similarity judgments while embedding effective dimensionality correlates with perceptual alignment at about -0.95 rank correlation, and that a dimensionality bottleneck on ECAPA-TDNN raises AM-Softmax alignment from 0.08 to 0.74.