Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Say More Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook In My Bag AI at School Safety Net Versus Trending Now All Series Belen Edwards is an Entertainment Reporter at Mashable. She covers movies and TV with a focus on fantasy and science fiction, adaptations, animation, and more nerdy goodness. She is a member of the Critics Choice Association and the Television Critics Association, as well as a Tomatometer-approved critic. Dakota Johnson keeps a straight face while promoting third'SNL' hosting appearance'Werwulf' trailer: Willem Dafoe hunts Aaron Taylor-Johnson in Robert Eggers' latest A little over a week after releasing, the singer-songwriter made an appearance on to support host Dakota Johnson. SEE ALSO: 'Verity' ending explainer: What should audiences believe?

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects In My Bag Look Up Creator Playbook Say More AI at School Safety Net Versus Trending Now Back to School Good Connection: Uplifting stories for a digital age All Series He's determined to make Super Intelligence happen. Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Rosie O'Donnell calls out'Mango Mussolini' Trump on'Kimmel' Jimmy Kimmel recaps Trump's summer with a montage of the weirdest things he's said Trump had a meeting with tech leaders recently, where he once again banged on about changing the name of artificial intelligence to super intelligence -- with Jimmy Kimmel playing a clip of him saying everybody likes it. Yes, I'm sure the guy whose company's name is OpenAI is very excited about this development, says Kimmel in the monologue above.

Mashable Selects Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more In My Bag Look Up Creator Playbook Say More AI at School Safety Net Versus Trending Now Back to School Good Connection: Uplifting stories for a digital age All Series'Digger' review: Tom Cruise swings big for Oscar gold, and dear God, does he miss Alejandro G. Iñárritu's political satire is sure to have people talking, at least? Kristy Puchko is the Entertainment Editor at Mashable. Based in New York City, she's an established film critic and entertainment reporter who has traveled the world on assignment, covered a variety of film festivals, co-hosted movie-focused podcasts, and interviewed a wide array of performers and filmmakers. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission.

Mashable Selects Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more In My Bag Look Up Creator Playbook Say More AI at School Safety Net Versus Trending Now Back to School Good Connection: Uplifting stories for a digital age All Series Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Rosie O'Donnell calls out'Mango Mussolini' Trump on'Kimmel' Rosie O'Donnell has another'Kimmel' monologue dig at Trump Jimmy Kimmel's rivalry with Matt Damon is a joke that's been going for decades, and it appears it won't be stopping anytime soon. During his Monday night monologue in New York, Kimmel is interrupted when Damon pops up in the crowd to cheer for the Boston Red Sox -- alongside Kimmel's own mother and sister. Kimmel wastes no time in throwing Damon out, but isn't too happy when he leaves with the host's family in tow.

Code Large Language Models (Code LLMs) have excelled at tasks like code completion but often miss deeper semantics such as execution effects and dynamic states. This paper aims to bridge the gap between Code LLMs' reliance on static text data

Full-duplex dialog models aim to listen and speak simultaneously, delivering rapid responses to dynamic user input. Among different solutions to full duplexity, a native solution merges multiple channels in each time step, achieving the lowest latency. However, prevailing designs break down the textual monologue sentences for word-level alignment with audio streams, which degrades language modeling abilities. To help address this issue, we introduce natural monologues, which are composed by continuous sentences and waiting intervals, mimicking humanoid cognitive behavior in dialogs. We find a proper training paradigm to be critical for semantically aligning natural monologues with audio. To this end, we develop a dual training paradigm that alternates the position of the monologues, either leading or trailing the audio, across different training stages. A combination of our natural monologue and dual training strategy is applied in developing FLM-Audio, our 7B spoken dialog chatbot with native full-duplexity. As confirmed by experimental results, FLM-Audio achieves superior response qualities and chatting experiences while requiring significantly less training data.

Earnings calls represent a uniquely rich and semi-structured source of financial communication, blending scripted managerial commentary with unscripted analyst dialogue. Although recent advances in financial sentiment analysis have integrated multi-modal signals, such as textual content and vocal tone, most systems rely on flat document-level or sentence-level models, failing to capture the layered discourse structure of these interactions. This paper introduces a novel multi-modal framework designed to generate semantically rich and structurally aware embeddings of earnings calls, by encoding them as hierarchical discourse trees. Each node, comprising either a monologue or a question-answer pair, is enriched with emotional signals derived from text, audio, and video, as well as structured metadata including coherence scores, topic labels, and answer coverage assessments. A two-stage transformer architecture is proposed: the first encodes multi-modal content and discourse metadata at the node level using contrastive learning, while the second synthesizes a global embedding for the entire conference. Experimental results reveal that the resulting embeddings form stable, semantically meaningful representations that reflect affective tone, structural logic, and thematic alignment. Beyond financial reporting, the proposed system generalizes to other high-stakes unscripted communicative domains such as tele-medicine, education, and political discourse, offering a robust and explainable approach to multi-modal discourse representation. This approach offers practical utility for downstream tasks such as financial forecasting and discourse evaluation, while also providing a generalizable method applicable to other domains involving high-stakes communication.

Movie dubbing has advanced significantly, yet assessing the real-world effectiveness of these models remains challenging. A comprehensive evaluation benchmark is crucial for two key reasons: 1) Existing metrics fail to fully capture the complexities of dialogue, narration, monologue, and actor adaptability in movie dubbing. 2) A practical evaluation system should offer valuable insights to improve movie dubbing quality and advancement in film production. To this end, we introduce Talking Adaptive Dubbing Benchmarks (TA-Dubbing), designed to improve film production by adapting to dialogue, narration, monologue, and actors in movie dubbing. TA-Dubbing offers several key advantages: 1) Comprehensive Dimensions: TA-Dubbing covers a variety of dimensions of movie dubbing, incorporating metric evaluations for both movie understanding and speech generation. 2) Versatile Benchmarking: TA-Dubbing is designed to evaluate state-of-the-art movie dubbing models and advanced multi-modal large language models. 3) Full Open-Sourcing: We fully open-source TA-Dubbing at https://github.com/woka- 0a/DeepDubber- V1 including all video suits, evaluation methods, annotations. We also continuously integrate new movie dubbing models into the TA-Dubbing leaderboard at https://github.com/woka- 0a/DeepDubber-V1 to drive forward the field of movie dubbing.

Large language models (LLMs) have demonstrated enhanced performance through the \textit{Thinking then Responding} paradigm, where models generate internal thoughts before final responses (aka, System 2 thinking). However, existing research lacks a systematic understanding of the mechanisms underlying how thinking patterns affect performance across model sizes. In this work, we conduct a comprehensive analysis of the impact of various thinking types on model performance and introduce ThinkPatterns-21k, a curated dataset comprising 21k instruction-response pairs (QA) collected from existing instruction-following datasets with five thinking types. For each pair, we augment it with five distinct internal thinking patterns: one unstructured thinking (monologue) and four structured variants (decomposition, self-ask, self-debate and self-critic), while maintaining the same instruction and response. Through extensive evaluation across different model sizes (3B-32B parameters), we have two key findings: (1) smaller models (<30B parameters) can benefit from most of structured thinking patterns, while larger models (32B) with structured thinking like decomposition would degrade performance and (2) unstructured monologue demonstrates broad effectiveness across different model sizes. Finally, we released all of our datasets, checkpoints, training logs of diverse thinking patterns to reproducibility, aiming to facilitate further research in this direction.