Topic modeling is a key method in text analysis, but existing approaches are limited by assuming one topic per document or fail to scale efficiently for large, noisy datasets of short texts. We introduce Semantic Component Analysis (SCA), a novel topic modeling technique that overcomes these limitations by discovering multiple, nuanced semantic components beyond a single topic in short texts which we accomplish by introducing a decomposition step to the clustering-based topic modeling framework. We evaluate SCA on Twitter datasets in English, Hausa and Chinese. It achieves competetive coherence and diversity compared to BERTopic, while uncovering at least double the semantic components and maintaining a noise rate close to zero. Furthermore, SCA is scalable and effective across languages, including an underrepresented one.

The frequent discovery of security vulnerabilities in both open-source and proprietary software underscores the urgent need for earlier detection during the development lifecycle. Initiatives such as DARPA's Artificial Intelligence Cyber Challenge (AIxCC) aim to accelerate Automated Vulnerability Detection (AVD), seeking to address this challenge by autonomously analyzing source code to identify vulnerabilities. This paper addresses two primary research questions: (RQ1) How is current AVD research distributed across its core components? (RQ2) What key areas should future research target to bridge the gap in the practical applicability of AVD throughout software development? To answer these questions, we conduct a systematization over 79 AVD articles and 17 empirical studies, analyzing them across five core components: task formulation and granularity, input programming languages and representations, detection approaches and key solutions, evaluation metrics and datasets, and reported performance. Our systematization reveals that the narrow focus of AVD research-mainly on specific tasks and programming languages-limits its practical impact and overlooks broader areas crucial for effective, real-world vulnerability detection. We identify significant challenges, including the need for diversified problem formulations, varied detection granularities, broader language support, better dataset quality, enhanced reproducibility, and increased practical impact. Based on these findings we identify research directions that will enhance the effectiveness and applicability of AVD solutions in software security.

In a context where the Brazilian judiciary system, the largest in the world, faces a crisis due to the slow processing of millions of cases, it becomes imperative to develop efficient methods for analyzing legal texts. We introduce uBERT, a hybrid model that combines Transformer and Recurrent Neural Network architectures to effectively handle long legal texts. Our approach processes the full text regardless of its length while maintaining reasonable computational overhead. Our experiments demonstrate that uBERT achieves superior performance compared to BERT+LSTM when overlapping input is used and is significantly faster than ULMFiT for processing long legal documents.

Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights. However, it is challenging for an off-the-shelf Large Language Model (LLM) to ingest these contracts due to the lengthy unstructured streams of text, limited LLM context windows, and complex legal jargon. To address these challenges, we introduce LAW (Legal Agentic Workflows for Custody and Fund Services Contracts). LAW features a modular design that responds to user queries by orchestrating a suite of domain-specific tools and text agents. Our experiments demonstrate that LAW, by integrating multiple specialized agents and tools, significantly outperforms the baseline. LAW excels particularly in complex tasks such as calculating a contract's termination date, surpassing the baseline by 92.9% points. Furthermore, LAW offers a cost-effective alternative to traditional fine-tuned legal LLMs by leveraging reusable, domain-specific tools.

Event-keyed summarization (EKS) requires summarizing a specific event described in a document given the document text and an event representation extracted from it. In this work, we extend EKS to the cross-document setting (CDEKS), in which summaries must synthesize information from accounts of the same event as given by multiple sources. We introduce SEAMUS (Summaries of Events Across Multiple Sources), a high-quality dataset for CDEKS based on an expert reannotation of the FAMUS dataset for cross-document argument extraction. We present a suite of baselines on SEAMUS -- covering both smaller, fine-tuned models, as well as zero- and few-shot prompted LLMs -- along with detailed ablations and a human evaluation study, showing SEAMUS to be a valuable benchmark for this new task.

Meta sent a letter to California's attorney general on Thursday urging him to stop OpenAI from converting to a for-profit company, a move that Meta says would be "wrong" and "could lead to a proliferation of similar start-up ventures that are notionally charitable until they are potentially profitable." The letter from Meta Platforms to Attorney General Rob Bonta, first reported on by The Wall Street Journal, comes on the heels of an injunction filed by Elon Musk at the end of November that also asked for OpenAI's conversion to be blocked. Meta argues in its letter, which The Verge has published in full, that OpenAI was able to raise billions of dollars from investors under its original nonprofit mission and now "wants to change its status while retaining all of the benefits that enabled it to reach the point it has today." It goes on to say, "OpenAI should not be allowed to flout the law by taking and reappropriating assets it built as a charity and using them for potentially enormous private gains." The letter also calls upon the attorney general to look into OpenAI's past practices as a nonprofit.

OpenAI says its models are "trained on publicly available data". Mr Balaji left the company in August, telling the New York Times he had since been working on personal projects. He grew up in Cupertino, California, before going to study computer science at the University of California, Berkeley. A spokesperson for OpenAI said in a statement cited by CNBC News that it was "devastated to learn of this incredibly sad news today and our hearts go out to Suchir's loved ones during this difficult time". US and Canadian news publishers, including the New York Times, and a group of best-selling writers, including John Grisham, have filed lawsuits claiming the company was illegally using news articles to train its software.

That was the score Mary Louis was given by an AI-powered tenant screening tool. The software, SafeRent, didn't explain in its 11-page report how the score was calculated or how it weighed various factors. It didn't say what the score actually signified. It just displayed Louis's number and determined it was too low. Louis, who works as a security guard, had applied for an apartment in an eastern Massachusetts suburb.

Artificial intelligence is being used to power the personalization of popular sports betting apps to tailor experiences to users' preferences. SUITS MOUNTING: Two Texas parents filed a lawsuit this week against the makers of Character.AI, claiming the artificial intelligence chatbot is a "clear and present danger to minors," with one plaintiff alleging it encouraged their teen to kill his parents. GENERATION AT RISK: Senate lawmakers unanimously passed the bipartisan-led Take It Down Act that would force social media companies to speedily remove sexually explicit deepfakes, prevent them from being posted and criminalize the act. 'WHAT WILL BE LEFT?': Lisa Kudrow fears an uncertain future as artificial intelligence becomes more and more prevalent in Hollywood. FUTURISTIC ROBOCOP: Footage from the streets of China captured a scene straight from a science fiction novel – spherical drones alongside patrolling law enforcement.

Abstract: Timely detection and geolocation of events based on social data can provide critical information for applications such as crisis response and resource allocation. However, most existing methods are greatly affected by event detection errors, leading to insufficient geolocation accuracy. To this end, this paper proposes a novel end-to-end event geolocation method (GTOP) leveraging Hyperbolic space and toponym hierarchies. Specifically, the proposed method contains one event detection module and one geolocation module. The event detection module constructs a heterogeneous information networks based on social data, and then constructs a homogeneous message graph and combines it with the text and time feature of the message to learning initial features of nodes. Node features are updated in Hyperbolic space and then fed into a classifier for event detection. To reduce the geolocation error, this paper proposes a noise toponym filtering algorithm (HIST) based on the hierarchical structure of toponyms. HIST analyzes the hierarchical structure of toponyms mentioned in the event cluster, taking the highly frequent city-level locations as the coarsegrained locations for events. To further improve the geolocation accuracy, we propose a fine-grained pseudo toponyms generation algorithm (FIT) based on the output of HIST, and combine generated pseudo toponyms with filtered toponyms to locate events based on the geographic center points of the combined toponyms. Extensive experiments are conducted on the Chinese dataset constructed in this paper and another public English dataset. The experimental results show that the proposed method is superior to the state-of-the-art baselines.