Usage Application The model is intended for open-vocabulary object detection.Known Caveats

We present MAFA (Multi-Agent Framework for Annotation), a production-deployed system that transforms enterprise-scale annotation workflows through configurable multi-agent collaboration. Addressing the critical challenge of annotation backlogs in financial services, where millions of customer utterances require accurate categorization, MAFA combines specialized agents with structured reasoning and a judge-based consensus mechanism. Our framework uniquely supports dynamic task adaptation, allowing organizations to define custom annotation types (FAQs, intents, entities, or domain-specific categories) through configuration rather than code changes. Deployed at JP Morgan Chase, MAFA has eliminated a 1 million utterance backlog while achieving, on average, 86% agreement with human annotators, annually saving over 5,000 hours of manual annotation work. The system processes utterances with annotation confidence classifications, which are typically 85% high, 10% medium, and 5% low across all datasets we tested. This enables human annotators to focus exclusively on ambiguous and low-coverage cases. We demonstrate MAFA's effectiveness across multiple datasets and languages, showing consistent improvements over traditional and single-agent annotation baselines: 13.8% higher Top-1 accuracy, 15.1% improvement in Top-5 accuracy, and 16.9% better F1 in our internal intent classification dataset and similar gains on public benchmarks.

The stochastic block model (SBM) is a fundamental model for studying graph clustering or community detection in networks. It has received great attention in the last decade and the balanced case, i.e., assuming all clusters have large

This study explores the capacity of large language models (LLMs) for explicit learning, a process involving the assimilation of metalinguistic explanations to carry out language tasks. Using constructed languages generated by cryptographic means as controlled test environments, we designed experiments to assess an LLM's ability to explicitly learn and apply grammar rules. Our results demonstrate that while LLMs possess a measurable capacity for explicit learning, this ability diminishes as the complexity of the linguistic phenomena at hand increases. Supervised fine-tuning on chains of thought significantly enhances LLM performance but struggles to generalize to typologically novel or more complex linguistic features. These findings point to the need for more diverse training sets and alternative fine-tuning strategies to further improve explicit learning by LLMs.

Recent debates raised concerns that language models may favor certain viewpoints. But what if the solution is not to aim for a 'view from nowhere' but rather to leverage different viewpoints? We introduce Plurals, a system and Python library for pluralistic AI deliberation. Plurals consists of Agents (LLMs, optionally with personas) which deliberate within customizable Structures, with Moderators overseeing deliberation. Plurals is a generator of simulated social ensembles. Plurals integrates with government datasets to create nationally representative personas, includes deliberation templates inspired by deliberative democracy, and allows users to customize both information-sharing structures and deliberation behavior within Structures. Six case studies demonstrate fidelity to theoretical constructs and efficacy. Three randomized experiments show simulated focus groups produced output resonant with an online sample of the relevant audiences (chosen over zero-shot generation in 75% of trials). Plurals is both a paradigm and a concrete system for pluralistic AI. The Plurals library is available at https://github.com/josh-ashkinaze/plurals and will be continually updated.

In this paper, we show that the multiple-stem approach to nouns with a broken plural pattern allows for greater generalizations to be stated in the morphological system. Such an approach dispenses with truncating/deleting rules and other complex rules that are required to account for the highly allomorphic broken plural system. The generation of inflected sound nouns necessitates a pre-specification of the affixes denoting the sound plural masculine and the sound plural feminine, namely uwna and aAt, in the lexicon. The first subsection of section one provides an evaluation of some of the previous analyses of the Arabic broken plural. We provide both linguistic and statistical evidence against deriving broken plurals from the singular or the root. In subsection two, we propose a multiple stem approach to the Arabic Noun Plural System within the Lexeme-based Morphology framework. In section two, we look at the noun inflection of Arabic. Section three provides an implementation of the Arabic Noun system in MORPHE. In this context, we show how the generalizations discussed in the linguistic analysis section are captured in Morphe using the equivalencing nodes.

Modern Standard Arabic (MSA) nominals present many morphological and lexical modeling challenges that have not been consistently addressed previously. This paper attempts to define the space of such challenges, and leverage a recently proposed morphological framework to build a comprehensive and extensible model for MSA nominals. Our model design addresses the nominals' intricate morphotactics, as well as their paradigmatic irregularities. Our implementation showcases enhanced accuracy and consistency compared to a commonly used MSA morphological analyzer and generator. We make our models publicly available.

Since Scrabble adopted an official lexicon in 1978, one thing has been constant: People have never stopped arguing about what is or isn't a word. Players have defended the game by noting that its letter strings--from AA (a kind of Hawaiian lava) to ZZZ (an interjection for sleep)--could be found in a bunch of standard North American dictionaries, books that have been used through the years to compile and revise Scrabble's tournament word list. But after an update last month introduced dozens of suspect words, riling up the community of competitive players, that's becoming harder to do. The linguistic tumult began in September, when the organization that maintains the word list used in club and tournament Scrabble, NASPA Games, published a draft of its update. The NASPA list includes all of the words in the Official Scrabble Players Dictionary, the go-to source for living-room and app players in North America, plus a lot more.