Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series Google's Pixel 11 lineup brings bold new camera features and a surprising approach to its camera hardware. Watch: CNET's Patrick Holland tests the Trump Phone and answers your questions live 36:59 This speaker is where it starts. Google Gemini is making its way into your car. Google's Pixel 11 lineup brings Magic Capture, Instant Night Sight, and one genuinely surprising choice: the cheapest and priciest models share a main camera. We tried Magic Capture ourselves and asked Google why the Fold's camera is different.

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series D23 2026: Every movie and series announced and shown at this year's fan event From'X-Men' bombshells to Pixar sequels, here's what you need to know. 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. 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's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series Marvel drops a special look'Avengers: Doomsday' trailer at D23 Chance Townsend is the General Assignments Editor at Mashable, covering tech, video games, dating apps, digital culture, and whatever else comes his way. He has a Master's in Journalism from the University of North Texas and is a proud orange cat father. His writing has also appeared in PC Mag and . A24's'Onslaught' trailer is chilling stuff'The Odyssey' trailer gives us our best look yet at Zendaya's Athena Marvel surprised the crowd at its D23 panel Friday night with new footage from, offering fans an exclusive look at the upcoming film. In the clip, Sue Storm of the Fantastic Four tells the assembled Avengers that Victor Von Doom (Robert Downey Jr.) wasn't always the villain they're facing now, hinting that the character's turn followed the loss of everything he held dear, presumably including his wife and child.

Proprietary giants are increasingly dominating the race for ever-larger language models. Can open-source, smaller models remain competitive across a broad range of tasks? In this paper, we present the Avengers -- a simple recipe that leverages the collective intelligence of these smaller models. The Avengers builds upon four lightweight operations: (i) embedding: encode queries using a text embedding model; (ii) clustering: group queries based on their semantic similarity; (iii) scoring: scores each model's performance within each cluster; and (iv) voting: improve outputs via repeated sampling and voting. At inference time, each query is embedded and assigned to its nearest cluster. The top-performing model(s) within that cluster are selected to generate the response with repeated sampling. Remarkably, with 10 open-source models (~7B parameters each), the Avengers surpasses GPT-4o, 4.1, and 4.5 in average performance across 15 diverse datasets spanning mathematics, coding, logical reasoning, general knowledge, and affective tasks. In particular, it surpasses GPT-4.1 on mathematics tasks by 18.21% and on code tasks by 7.46%. Furthermore, the Avengers delivers superior out-of-distribution generalization, and remains robust across various embedding models, clustering algorithms, ensemble strategies, and values of its sole parameter -- the number of clusters.

When he got his first glimpse of a movie studio, Orson Welles excitedly proclaimed it "the biggest electric train set any boy ever had." But with a reported budget of more than 300 million, Joe and Anthony Russo's The Electric State makes Welles' train set look like a busted caboose. The most expensive movie in Netflix's history, it's also among the costliest of all time, joining a list that includes the brothers' own Avengers: Infinity War and Avengers: Endgame. If the Russos are the most profligate creators in history--their Amazon series Citadel is also one of the most expensive TV shows ever made--they're among the most successful too. And yet for all the money they're making, and all that they're allowed to spend, they don't seem to be enjoying themselves very much.

Conversational Recommender Systems (CRSs) have emerged as a transformative paradigm for offering personalized recommendations through natural language dialogue. However, they face challenges with knowledge sparsity, as users often provide brief, incomplete preference statements. While recent methods have integrated external knowledge sources to mitigate this, they still struggle with semantic understanding and complex preference reasoning. Recent Large Language Models (LLMs) demonstrate promising capabilities in natural language understanding and reasoning, showing significant potential for CRSs. Nevertheless, due to the lack of domain knowledge, existing LLM-based CRSs either produce hallucinated recommendations or demand expensive domain-specific training, which largely limits their applicability. In this work, we present G-CRS (Graph Retrieval-Augmented Large Language Model for Conversational Recommender Systems), a novel training-free framework that combines graph retrieval-augmented generation and in-context learning to enhance LLMs' recommendation capabilities. Specifically, G-CRS employs a two-stage retrieve-and-recommend architecture, where a GNN-based graph reasoner first identifies candidate items, followed by Personalized PageRank exploration to jointly discover potential items and similar user interactions. These retrieved contexts are then transformed into structured prompts for LLM reasoning, enabling contextually grounded recommendations without task-specific training. Extensive experiments on two public datasets show that G-CRS achieves superior recommendation performance compared to existing methods without requiring task-specific training.

Retrieval-augmented generation (RAG) has recently become a very popular task for Large Language Models (LLMs). Evaluating them on multi-turn RAG conversations, where the system is asked to generate a response to a question in the context of a preceding conversation is an important and often overlooked task with several additional challenges. We present MTRAG: an end-to-end human-generated multi-turn RAG benchmark that reflects several real-world properties across diverse dimensions for evaluating the full RAG pipeline. MTRAG contains 110 conversations averaging 7.7 turns each across four domains for a total of 842 tasks. We also explore automation paths via synthetic data and LLM-as-a-Judge evaluation. Our human and automatic evaluations show that even state-of-the-art LLM RAG systems struggle on MTRAG. We demonstrate the need for strong retrieval and generation systems that can handle later turns, unanswerable questions, non-standalone questions, and multiple domains. MTRAG is available at https://github.com/ibm/mt-rag-benchmark.

Robert Downey Jr. praised Jon Favreau for being ambitious in his filmmaking, shouting out many films he has directed, including'The Lion King' and'The Jungle Book.' Robert Downey Jr. might be devoid of iron, but he's sure got some steel. The Academy Award-winning actor, 59, is speaking out about rapid technological advancements and how he plans to fight back if his name and likeness are manipulated by artificial intelligence. "I intend to sue," he told the "On with Kara Swisher" podcast. HOLLYWOOD EXECS WARN AI STEALS JOBS BUT CAN'T DO JOB OF TRUE ARTISTS: 'I WANT TO WORK WITH HUMAN BEINGS' Robert Downey Jr. says he plans to sue if someone manipulates his likeness through artificial intelligence. It all comes back to Downey Jr.'s alter ego, Tony Stark, whose own alter ego is Iron Man.

I wake up and immediately open Bumble. I swipe until I match with a woman who writes in her profile that the "Mamma Mia!" movies are better than the "Avengers" films. I promptly send her a message letting her know that she is wrong. The "Avengers" franchise is worth $14.3 billion, and the childish "Mamma Mia!" movies raked in a measly $1.1 billion. I tell her she can thank me for this information by getting a drink with me tonight.

The rise of AI programs like ChatGPT has triggered a tidal wave of ethical handwringing, most prominently from within the industries that it threatens to destroy. After all, just because you can get a robot to instantly write code or write contracts or provide customer support for free, should you? Well, the answer from the Writers Guild of America is a qualified yes. This week, the Writers Guild of America proposed that ChatGPT would absolutely be allowed to write scripts in the future, provided that the credit (and the money) goes to the human writer who came up with the prompts in the first place. The proposal paints a scary picture of the future; a future in which even the most human of arts are crushed under the wheels of an unthinking technology.