Our analysis involved a two-phase process.

Former U.S. Treasury Secretary Larry Summers says he will step back from all public commitments, adding the move is to allow him to rebuild trust and repair relationships with the people closest to me. Former U.S. Treasury Secretary Larry Summers is stepping down from a teaching post at Harvard University and as a director of one of its business and government schools, a spokesperson said on Wednesday, after Congress released documents showing Summers shared close ties with the late convicted sex offender Jeffrey Epstein. A spokesperson for Summers, Steven Goldberg, said Summers' co-teachers would complete the semester for three ongoing courses. Mr. Summers has decided it's in the best interest of the center for him to go on leave from his role as director as Harvard undertakes its review, he said. Summers, also a former president of Harvard University, is a director of the Mossavar-Rahmani Center for Business and Government at the Harvard Kennedy School. Summers has been under fire since the U.S. House Oversight Committee released documents detailing an ongoing personal correspondence between Summers and Epstein, who died by suicide in a Manhattan prison in 2019 as he faced sex-trafficking charges.

Evaluating Large Language Models' (LLMs) anthropomorphic capabilities has

The draft order, obtained by WIRED, instructs the US Justice Department to sue states that pass laws regulating AI. US President Donald Trump is considering signing an executive order that would seek to challenge state efforts to regulate artificial intelligence through lawsuits and the withholding federal funding, WIRED has learned. A draft of the order viewed by WIRED directs US Attorney General Pam Bondi to create an "AI Litigation Task Force," whose purpose is to sue states in court for passing AI regulations that allegedly violate federal laws governing things like free speech and interstate commerce. Trump could sign the order, which is currently titled "Eliminating State Law Obstruction of National AI Policy," as early as this week, according to four sources familiar with the matter. A White House spokesperson told WIRED that "discussion about potential executive orders is speculation."

Shapley values have emerged as a critical tool for explaining which features impact the decisions made by machine learning models. However, computing exact Shapley values is difficult, generally requiring an exponential (in the feature dimension) number of model evaluations. To address this, many model-agnostic randomized estimators have been developed, the most influential and widely used being the KernelSHAP method (Lundberg & Lee, 2017). While related estimators such as unbiased KernelSHAP (Covert & Lee, 2021) and LeverageSHAP (Musco & Witter, 2025) are known to satisfy theoretical guarantees, bounds for KernelSHAP have remained elusive. We describe a broad and unified framework that encompasses KernelSHAP and related estimators constructed using both with and without replacement sampling strategies. We then prove strong non-asymptotic theoretical guarantees that apply to all estimators from our framework. This provides, to the best of our knowledge, the first theoretical guarantees for KernelSHAP and sheds further light on tradeoffs between existing estimators. Through comprehensive benchmarking on small and medium dimensional datasets for Decision-Tree models, we validate our approach against exact Shapley values, consistently achieving low mean squared error with modest sample sizes. Furthermore, we make specific implementation improvements to enable scalability of our methods to high-dimensional datasets. Our methods, tested on datasets such MNIST and CIFAR10, provide consistently better results compared to the KernelSHAP library.

Aircraft Maintenance Technicians (AMTs) spend up to 30% of work time searching manuals, a documented efficiency bottleneck in MRO operations where every procedure must be traceable to certified sources. We present a compliance-preserving retrieval system that adapts LLM reranking and semantic search to aviation MRO environments by operating alongside, rather than replacing, certified legacy viewers. The system constructs revision-robust embeddings from ATA chapter hierarchies and uses vision-language parsing to structure certified content, allowing technicians to preview ranked tasks and access verified procedures in existing viewers. Evaluation on 49k synthetic queries achieves >90% retrieval accuracy, while bilingual controlled studies with 10 licensed AMTs demonstrate 90.9% top-10 success rate and 95% reduction in lookup time, from 6-15 minutes to 18 seconds per task. These gains provide concrete evidence that semantic retrieval can operate within strict regulatory constraints and meaningfully reduce operational workload in real-world multilingual MRO workflows.

The law draws a sharp distinction between objects and persons, and between two kinds of persons, the ''fictional'' kind (i.e. corporations), and the ''non-fictional'' kind (individual or ''natural'' persons). This paper will assess whether we maximize overall long-term legal coherence by (A) maintaining an object classification for all future AI systems, (B) creating fictional legal persons associated with suitably advanced, individuated AI systems (giving these fictional legal persons derogable rights and duties associated with certified groups of existing persons, potentially including free speech, contract rights, and standing to sue ''on behalf of'' the AI system), or (C) recognizing non-fictional legal personhood through legal identity for suitably advanced, individuated AI systems (recognizing them as entities meriting legal standing with non-derogable rights which for the human case include life, due process, habeas corpus, freedom from slavery, and freedom of conscience). We will clarify the meaning and implications of each option along the way, considering liability, copyright, family law, fundamental rights, civil rights, citizenship, and AI safety regulation. We will tentatively find that the non-fictional personhood approach may be best from a coherence perspective, for at least some advanced AI systems. An object approach may prove untenable for sufficiently humanoid advanced systems, though we suggest that it is adequate for currently existing systems as of 2025. While fictional personhood would resolve some coherence issues for future systems, it would create others and provide solutions that are neither durable nor fit for purpose. Finally, our review will suggest that ''hybrid'' approaches are likely to fail and lead to further incoherence: the choice between object, fictional person and non-fictional person is unavoidable.