Large Language Models (LLMs) have made it possible for recommendation systems to interact with users in open-ended conversational interfaces. In order to personalize LLM responses, it is crucial to elicit user preferences, especially when there is limited user history. One way to get more information is to present clarifying questions to the user. However, generating effective sequential clarifying questions across various domains remains a challenge. To address this, we introduce a novel approach for training LLMs to ask sequential questions that reveal user preferences. Our method follows a two-stage process inspired by diffusion models. Starting from a user profile, the forward process generates clarifying questions to obtain answers and then removes those answers step by step, serving as a way to add ``noise'' to the user profile. The reverse process involves training a model to ``denoise'' the user profile by learning to ask effective clarifying questions. Our results show that our method significantly improves the LLM's proficiency in asking funnel questions and eliciting user preferences effectively.

Recent advances in the audio language modeling (ALM) domain tackle audio understanding and text-to-audio generation as separate tasks. V ery few studies attempt to unify these tasks - an essential step toward advanced multimodal reasoning. This paper introduces Unified Audio Language Model (UALM), which aims to unify audio understanding, text-to-audio generation, and multimodal reasoning in a single model. To achieve this goal, we first present UALM-Gen, a text-to-audio language model that directly predicts audio tokens and is comparable to state-of-the-art diffusion-based models. We then demonstrate, using proper data blending, training recipes, and inference techniques, that our single UALM model matches the quality of state-of-the-art specialized models in audio understanding, text-to-audio generation, and text reasoning. Furthermore, we present UALM-Reason, a multimodal reasoning model that utilizes both text and audio in the intermediate thinking steps to facilitate complex generation tasks. To our knowledge, this is the first demonstration in audio research of cross-modal generative reasoning, with its effectiveness confirmed by subjective evaluations. Figure 1: Humans need understanding, generation, and reasoning to handle complex tasks, like composing music. Human auditory intelligence is characterized by two fundamental capabilities: perception (understanding) and production (generation). This duality is not merely conceptual; neuro-scientific evidence reveals a profound synergy between these functions, where impairment in one often corresponds to a deficit in the other (Liberman et al., 1967; Hickok & Poeppel, 2007; Rizzolatti & Craighero, 2004). Furthermore, resolving complex acoustic challenges requires a sophisticated reasoning process that is inherently multimodal (McGurk & MacDonald, 1976; Leman, 2007; Denes & Pinson, 1993; Liberman & Mattingly, 1985).

Social media has become an essential part of the digital age, serving as a platform for communication, interaction, and information sharing. Celebrities are among the most active users and often reveal aspects of their personal and professional lives through online posts. Platforms such as Twitter provide an opportunity to analyze language and behavior for understanding demographic and social patterns. Since followers frequently share linguistic traits and interests with the celebrities they follow, textual data from followers can be used to predict celebrity demographics. However, most existing research in this field has focused on English and other high-resource languages, leaving Urdu largely unexplored. This study applies modern machine learning and deep learning techniques to the problem of celebrity profiling in Urdu. A dataset of short Urdu tweets from followers of subcontinent celebrities was collected and preprocessed. Multiple algorithms were trained and compared, including Logistic Regression, Support Vector Machines, Random Forests, Convolutional Neural Networks, and Long Short-Term Memory networks. The models were evaluated using accuracy, precision, recall, F1-score, and cumulative rank (cRank). The best performance was achieved for gender prediction with a cRank of 0.65 and an accuracy of 0.65, followed by moderate results for age, profession, and fame prediction. These results demonstrate that follower-based linguistic features can be effectively leveraged using machine learning and neural approaches for demographic prediction in Urdu, a low-resource language.

Existing Persian speech datasets are typically smaller than their English counterparts, which creates a key limitation for developing Persian speech technologies. We address this gap by introducing ParsVoice, the largest Persian speech corpus designed specifically for text-to-speech(TTS) applications. We created an automated pipeline that transforms raw audiobook content into TTS-ready data, incorporating components such as a BERT-based sentence completion detector, a binary search boundary optimization method for precise audio-text alignment, and audio-text quality assessment frameworks tailored to Persian. The pipeline processes 2,000 audiobooks, yielding 3,526 hours of clean speech, which was further filtered into a 1,804-hour high-quality subset suitable for TTS, featuring more than 470 speakers. To validate the dataset, we fine-tuned XTTS for Persian, achieving a naturalness Mean Opinion Score (MOS) of 3.6/5 and a Speaker Similarity Mean Opinion Score (SMOS) of 4.0/5 demonstrating ParsVoice's effectiveness for training multi-speaker TTS systems. ParsVoice is the largest high-quality Persian speech dataset, offering speaker diversity and audio quality comparable to major English corpora. The complete dataset has been made publicly available to accelerate the development of Persian speech technologies. The ParsVoice dataset is publicly available at: https://huggingface.co/datasets/MohammadJRanjbar/ParsVoice.

Guitar tablature transcription consists in deducing the string and the fret number on which each note should be played to reproduce the actual musical part. This assignment should lead to playable string-fret combinations throughout the entire track and, in general, preserve parsimonious motion between successive combinations. Throughout the history of guitar playing, specific chord fingerings have been developed across different musical styles that facilitate common idiomatic voicing combinations and motion between them. This paper presents a method for assigning guitar tablature notation to a given MIDI-based musical part (possibly consisting of multiple polyphonic tracks), i.e. no information about guitar-idiomatic expressional characteristics is involved (e.g. bending etc.) The current strategy is based on machine learning and requires a basic assumption about how much fingers can stretch on a fretboard; only standard 6-string guitar tuning is examined. The proposed method also examines the transcription of music pieces that was not meant to be played or could not possibly be played by a guitar (e.g. potentially a symphonic orchestra part), employing a rudimentary method for augmenting musical information and training/testing the system with artificial data. The results present interesting aspects about what the system can achieve when trained on the initial and augmented dataset, showing that the training with augmented data improves the performance even in simple, e.g. monophonic, cases. Results also indicate weaknesses and lead to useful conclusions about possible improvements.

Large language models (LLMs) excel at complex reasoning but can still exhibit harmful behaviors. Current alignment strategies typically embed safety into model weights, making these controls implicit, static, and difficult to modify. This paper introduces Cognition-of-Thought (CooT), a novel decoding-time framework that equips LLMs with an explicit cognitive self-monitoring loop. CooT couples a standard text Generator with a cognitive Perceiver that continuously monitors the unfolding sequence. The Perceiver uses a structured, precedence-based hierarchy of principles (e.g., safety over obedience) to detect potential misalignments as they arise. When violations are flagged, CooT intervenes by rolling back the generation to the point of error and regenerating under injected guidance that combines universal social priors with context-specific warnings. CooT thus transforms alignment from a fixed property into an explicit, dynamic, and auditable process active during inference, allowing for flexible policy updates without retraining the model. Extensive experiments across multiple benchmarks and model families confirm that CooT consistently improves safety and social reasoning performance.

Understanding and solving complex reasoning tasks is vital for addressing the information needs of a user. Although dense neural models learn contextualised embeddings, they still underperform on queries containing negation. To understand this phenomenon, we study negation in both traditional neural information retrieval and LLM-based models. We (1) introduce a taxonomy of negation that derives from philosophical, linguistic, and logical definitions; (2) generate two benchmark datasets that can be used to evaluate the performance of neural information retrieval models and to fine-tune models for a more robust performance on negation; and (3) propose a logic-based classification mechanism that can be used to analyze the performance of retrieval models on existing datasets. Our taxonomy produces a balanced data distribution over negation types, providing a better training setup that leads to faster convergence on the NevIR dataset. Moreover, we propose a classification schema that reveals the coverage of negation types in existing datasets, offering insights into the factors that might affect the generalization of fine-tuned models on negation.

Instagram Is Introducing New Restrictions for Teen Users. In this photo illustration a 13-year-old boy looks at an iPhone screen display on May 21, 2025 in Bath, England. In this photo illustration a 13-year-old boy looks at an iPhone screen display on May 21, 2025 in Bath, England. Instagram announced new restrictions for teen accounts on Tuesday amid mounting controversy over safety guidelines for younger users on the social media platform. The photo-sharing app will soon limit content for teens using guidelines similar to those in the film industry for PG-13-rated movies.

Plus: California's AI safety bill has passed into law Wrinkles and gray hairs aside, it can be difficult to know how well--or poorly--someone's body is truly aging. A person who develops age-related diseases earlier in life, or has other biological changes associated with aging, might be considered "biologically older" than a similar-age person who doesn't have those changes. Some 80-year-olds will be weak and frail, while others are fit and active. Over the past decade, scientists have been uncovering new methods of looking at the hidden ways our bodies are aging. And what they've found is changing our understanding of aging itself. Can we repair the internet?

Hamas executes'collaborators' in Gaza as it clings to power amid fears Trump's peace deal is already at risk Internet star who demanded free seats for fat fliers vanished without trace... now the Daily Mail has learned the heartbreaking reason why Donald Trump tells crowds there are world leaders he'doesn't like at ALL' as he teases who they are How Diane Keaton's closest friend helped her to achieve her'lifelong ambition' just months before she died - and the poignant legacy it leaves Kate and Wills' fresh start at their'forever home': Why they have fast-tracked their move to house they will never leave - even when he becomes King'It's Meghan Markle 3.0': Why the duchess has set tongues wagging that she's plotting another Sussex relaunch'as she holds cosy meeting with new editor of US Vogue' Trump's ominous warning to Macron at Egypt summit: 'You will see what is about to happen' Neil Diamond, 84, sang Sweet Caroline and worked with Cher as well as Barbra Streisand... see him now Insiders reveal how reluctant Katy Perry finally gave in to'persistent' Justin Trudeau... as sexy yacht photos get spicy response from his ex-wife Awkward moment Donald Trump asks Giorgia Meloni'You won't be offended if I say you're beautiful, right? Horrors endured by Israel's last 20 hostages: Chained, tortured, and starved. Lindsey Halligan removes senior DOJ official after taking over Virginia US attorney's office Gorgeous Bay Area enclave filled with hippies becomes America's ANGRIEST town over plans for huge affordable housing project MLB fans hail'greatest play in baseball HISTORY' after Dodgers thought they hit grand slam in Brewers game Father launches campaign to become sheriff as he faces murder trial for killing teenage daughter's abuser Spot the difference: Apple has rebranded its TV service as part of a'vibrant new identity' - so, can you see what has changed? But Apple TV+ is no more - as Apple has quietly rebranded its streaming service. 'Apple TV+ is now simply Apple TV, with a vibrant new identity,' the tech giant explained in the bottom of a press release on the streaming debut of its film, 'F1 The Movie'.