I feel a troubling kind of opacity in my brain lately--as if reality were becoming illegible, as if language were a vessel with holes in the bottom and meaning was leaking all over the floor. I sometimes look up words after I write them: does "illegible" still mean too messy to read? The day after Donald Trump's second Inauguration, my verbal cognition kept glitching: I got an e-mail from the children's-clothing company Hanna Andersson and read the name as "Hamas"; on the street, I thought "hot yoga" was "hot dogs"; on the subway, a theatre poster advertising "Jan. Ticketing" said "Jia Tolentino" to me. Even the words that I might use to more precisely describe the sensation of "losing it" elude me.

The State Bar of California announced Friday that its embattled leader, who has faced growing pressure to resign over the botched February roll out of a new bar exam, will step down in July. Leah T. Wilson, the agency's executive director, informed the Board of Trustees she will not seek another term in the position she has held on and off since 2017. She also apologized for her role in the February bar exam chaos. "Accountability is a bedrock principle for any leader," Wilson said in a statement. "At the end of the day, I am responsible for everything that occurs within the organization. Despite our best intentions, the experiences of applicants for the February Bar Exam simply were unacceptable, and I fully recognize the frustration and stress this experience caused. While there are no words to assuage those emotions, I do sincerely apologize."

The NGO appeared to accuse Israel of being behind the incident and called for Israeli ambassadors to be summoned to answer for "violation of international law, including the ongoing blockade and the bombing of our civilian vessel". The Israeli military said it was looking into reports of the attack. The Freedom Flotilla Coalition uploaded a video showing a fire on one of its ships but did not indicate whether anyone had been hurt. It said the attack appeared to have targeted the generator, which left the ship without power and at risk of sinking. The ship was 17 nautical miles (31.5 kilometres) east of Malta when it was hit.

Identifying regulatory statements in legislation is useful for developing metrics to measure the regulatory density and strictness of legislation. A computational method is valuable for scaling the identification of such statements from a growing body of EU legislation, constituting approximately 180,000 published legal acts between 1952 and 2023. Past work on extraction of these statements varies in the permissiveness of their definitions for what constitutes a regulatory statement. In this work, we provide a specific definition for our purposes based on the institutional grammar tool. We develop and compare two contrasting approaches for automatically identifying such statements in EU legislation, one based on dependency parsing, and the other on a transformer-based machine learning model. We found both approaches performed similarly well with accuracies of 80% and 84% respectively and a K alpha of 0.58. The high accuracies and not exceedingly high agreement suggests potential for combining strengths of both approaches.

We extend the formal framework of classifier models used in the legal domain. While the existing classifier framework characterises cases solely through the facts involved, legal reasoning fundamentally relies on both facts and rules, particularly the ratio decidendi. This paper presents an initial approach to incorporating sets of rules within a classifier. Our work is built on the work of Canavotto et al. (2023), which has developed the rule-based reason model of precedential constraint within a hierarchy of factors. We demonstrate how decisions for new cases can be inferred using this enriched rule-based classifier framework. Additionally, we provide an example of how the time element and the hierarchy of courts can be used in the new classifier framework.

This paper proposed an approach to automatically discovering subject dimension, action dimension, object dimension and adverbial dimension from texts to efficiently operate texts and support query in natural language. The high quality of trees guarantees that all subjects, actions, objects and adverbials and their subclass relations within texts can be represented. The independency of trees ensures that there is no redundant representation between trees. The expressiveness of trees ensures that the majority of sentences can be accessed from each tree and the rest of sentences can be accessed from at least one tree so that the tree-based search mechanism can support querying in natural language. Experiments show that the average precision, recall and F1-score of the abstraction trees constructed by the subclass relations of subject, action, object and adverbial are all greater than 80%. The application of the proposed approach to supporting query in natural language demonstrates that different types of question patterns for querying subject or object have high coverage of texts, and searching multiple trees on subject, action, object and adverbial according to the question pattern can quickly reduce search space to locate target sentences, which can support precise operation on texts.

Our AU-ROC scores show that GPT-4o, OpenAI's more recent and capable model, demonstrates strong recognition of paywalled O'Reilly book content (AUROC = 82%), compared to OpenAI's earlier model GPT-3.5 Turbo. In contrast, GPT-3.5 Turbo shows greater relative recognition of publicly accessible O'Reilly book samples. GPT-4o Mini, as a much smaller model, shows no knowledge of public or non-public O'Reilly Media content when tested (AUROC 50%). Testing multiple models, with the same cutoff date, helps us account for potential language shifts over time that might bias our findings. These results highlight the urgent need for increased corporate transparency regarding pre-training data sources as a means to develop formal licensing frameworks for AI content training.

The proliferation of generative AI and deceptive synthetic media threatens the global information ecosystem, especially across the Global Majority. This report from WITNESS highlights the limitations of current AI detection tools, which often underperform in real-world scenarios due to challenges related to explainability, fairness, accessibility, and contextual relevance. In response, WITNESS introduces the Truly Innovative and Effective AI Detection (TRIED) Benchmark, a new framework for evaluating detection tools based on their real-world impact and capacity for innovation. Drawing on frontline experiences, deceptive AI cases, and global consultations, the report outlines how detection tools must evolve to become truly innovative and relevant by meeting diverse linguistic, cultural, and technological contexts. It offers practical guidance for developers, policy actors, and standards bodies to design accountable, transparent, and user-centered detection solutions, and incorporate sociotechnical considerations into future AI standards, procedures and evaluation frameworks. By adopting the TRIED Benchmark, stakeholders can drive innovation, safeguard public trust, strengthen AI literacy, and contribute to a more resilient global information credibility.

As large language models (LLMs) evolve into tool-using agents, the ability to browse the web in real-time has become a critical yardstick for measuring their reasoning and retrieval competence. Existing benchmarks such as BrowseComp concentrate on English and overlook the linguistic, infrastructural, and censorship-related complexities of other major information ecosystems -- most notably Chinese. To address this gap, we introduce BrowseComp-ZH, a high-difficulty benchmark purpose-built to comprehensively evaluate LLM agents on the Chinese web. BrowseComp-ZH consists of 289 multi-hop questions spanning 11 diverse domains. Each question is reverse-engineered from a short, objective, and easily verifiable answer (e.g., a date, number, or proper noun). A two-stage quality control protocol is applied to strive for high question difficulty and answer uniqueness. We benchmark over 20 state-of-the-art language models and agentic search systems on our proposed BrowseComp-ZH. Despite their strong conversational and retrieval capabilities, most models struggle severely: a large number achieve accuracy rates below 10%, and only a handful exceed 20%. Even the best-performing system, OpenAI's DeepResearch, reaches just 42.9%. These results demonstrate the considerable difficulty of BrowseComp-ZH, where success demands not only effective retrieval strategies, but also sophisticated reasoning and information reconciliation -- capabilities that current models still struggle to master. Our dataset, construction guidelines, and benchmark results have been publicly released at https://github.com/PALIN2018/BrowseComp-ZH.

US District Court Judge Vince Chhabria spent several hours grilling lawyers from both sides after they each filed motions for partial summary judgment, meaning they want Chhabria to rule on specific issues of the case rather than leaving each one to be decided at trial. The authors allege that Meta illegally used their work to build its generative AI tools, emphasizing that the company pirated their books through "shadow libraries" like LibGen. Kadrey v. Meta is one of the dozens of lawsuits filed against AI companies that are winding through the US legal system. While the authors were heavily focused on the piracy element of the case, Chhabria spoke emphatically about his belief that the big question is whether Meta's AI tools will hurt book sales and otherwise cause the authors to lose money. "If you are dramatically changing, you might even say obliterating, the market for that person's work, and you're saying that you don't even have to pay a license to that person to use their work to create the product that's destroying the market for their work--I just don't understand how that can be fair use," he told Meta lawyer Kannon Shanmugam.