This paper presents Project Riley, a novel multimodal and multi-model conversational AI architecture oriented towards the simulation of reasoning influenced by emotional states. Drawing inspiration from Pixar's Inside Out, the system comprises five distinct emotional agents - Joy, Sadness, Fear, Anger, and Disgust - that engage in structured multi-round dialogues to generate, criticise, and iteratively refine responses. A final reasoning mechanism synthesises the contributions of these agents into a coherent output that either reflects the dominant emotion or integrates multiple perspectives. The architecture incorporates both textual and visual large language models (LLMs), alongside advanced reasoning and self-refinement processes. A functional prototype was deployed locally in an offline environment, optimised for emotional expressiveness and computational efficiency. From this initial prototype, another one emerged, called Armando, which was developed for use in emergency contexts, delivering emotionally calibrated and factually accurate information through the integration of Retrieval-Augmented Generation (RAG) and cumulative context tracking. The Project Riley prototype was evaluated through user testing, in which participants interacted with the chatbot and completed a structured questionnaire assessing three dimensions: Emotional Appropriateness, Clarity and Utility, and Naturalness and Human-likeness. The results indicate strong performance in structured scenarios, particularly with respect to emotional alignment and communicative clarity.

Podcasts have become daily companions for half a billion users. Given the enormous amount of podcast content available, highlights provide a valuable signal that helps viewers get the gist of an episode and decide if they want to invest in listening to it in its entirety. However, identifying highlights automatically is challenging due to the unstructured and long-form nature of the content. We introduce Rhapsody, a dataset of 13K podcast episodes paired with segment-level highlight scores derived from YouTube's 'most replayed' feature. We frame the podcast highlight detection as a segment-level binary classification task. We explore various baseline approaches, including zero-shot prompting of language models and lightweight fine-tuned language models using segment-level classification heads. Our experimental results indicate that even state-of-the-art language models like GPT-4o and Gemini struggle with this task, while models fine-tuned with in-domain data significantly outperform their zero-shot performance. The fine-tuned model benefits from leveraging both speech signal features and transcripts. These findings highlight the challenges for fine-grained information access in long-form spoken media.

Graph Neural Networks (GNNs) suffer from Oversquashing, which occurs when tasks require long-range interactions. The problem arises from the presence of bottlenecks that limit the propagation of messages among distant nodes. Recently, graph rewiring methods modify edge connectivity and are expected to perform well on long-range tasks. Yet, graph rewiring compromises the inductive bias, incurring significant information loss in solving the downstream task. Furthermore, increasing channel capacity may overcome information bottlenecks but enhance the parameter complexity of the model. To alleviate these shortcomings, we propose an efficient model-agnostic framework that asynchronously updates node features, unlike traditional synchronous message passing GNNs. Our framework creates node batches in every layer based on the node centrality values. The features of the nodes belonging to these batches will only get updated. Asynchronous message updates process information sequentially across layers, avoiding simultaneous compression into fixed-capacity channels. We also theoretically establish that our proposed framework maintains higher feature sensitivity bounds compared to standard synchronous approaches. Our framework is applied to six standard graph datasets and two long-range datasets to perform graph classification and achieves impressive performances with a $5\%$ and $4\%$ improvements on REDDIT-BINARY and Peptides-struct, respectively.

Intelligent vehicle cockpits present unique challenges for API Agents, requiring coordination across tightly-coupled subsystems that exceed typical task environments' complexity. Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery. We introduce VehicleWorld, the first comprehensive environment for the automotive domain, featuring 30 modules, 250 APIs, and 680 properties with fully executable implementations that provide real-time state information during agent execution. This environment enables precise evaluation of vehicle agent behaviors across diverse, challenging scenarios. Through systematic analysis, we discovered that direct state prediction outperforms function calling for environmental control. Building on this insight, we propose State-based Function Call (SFC), a novel approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions. Experimental results demonstrate that SFC significantly outperforms traditional FC approaches, achieving superior execution accuracy and reduced latency. We have made all implementation code publicly available on Github https://github.com/OpenMOSS/VehicleWorld.

Large Language Models (LLMs) are increasingly used for recommendation tasks due to their general-purpose capabilities. While LLMs perform well in rich-context settings, their behavior in cold-start scenarios, where only limited signals such as age, gender, or language are available, raises fairness concerns because they may rely on societal biases encoded during pretraining. We introduce a benchmark specifically designed to evaluate fairness in zero-context recommendation. Our modular pipeline supports configurable recommendation domains and sensitive attributes, enabling systematic and flexible audits of any open-source LLM. Through evaluations of state-of-the-art models (Gemma 3 and Llama 3.2), we uncover consistent biases across recommendation domains (music, movies, and colleges) including gendered and cultural stereotypes. We also reveal a non-linear relationship between model size and fairness, highlighting the need for nuanced analysis.

Trump's Epstein crisis explodes as lewd birthday letter showing president's signature is revealed Judge's'promise' let career criminal walk free to butcher Ukrainian refugee after his MOM said he should be locked up'She was so f***ed up': Carolyn Bessette's friends tell MAUREEN CALLAHAN of her secret Daddy issue, JFK Jr's murder brag that drove her mad... and why everything we know about her is a lie The chaos behind when Meghan Markle was told not to be at Queen Elizabeth II's deathbed They were locked in a dungeon inside a house of horrors. But incredible footage shows five kids' daring acts while their parents were out... and it left neighbors speechless Turn back the clock with the K-beauty retinol cream Amazon shoppers say leaves their skin'silky smooth' - and it's now $10 Scientists crack the ultimate answer to the meaning of life... and it's hidden among 38M obituaries CBS News hires a CONSERVATIVE to police interviews after Trump and Noem'deceptive' editing fury Scientist claims life on Earth was not random... but engineered Supreme Court LIFTS restrictions on Trump's immigration raids despite claims agents targeted people by race I was 52 with a collapsed'turkey neck'. Here's how I turned back the clock 10 years Plastic surgeons weigh in on Jessica Simpson's dramatic new look at VMAs as fans declare her'unrecognizable' Billionaire turns his back on Trump as he blasts President's'risky' financial move that could cost Americans their savings Trump loses appeal and must pay $83 million to E. Jean Carroll AMANDA PLATELL: Harry is'desperate' to come back to Britain and reclaim his royal role - but this fresh snub from William makes it clear why it will never happen... and why he'll never forgive his brother Scientists crack the ultimate answer to the meaning of life... and it's hidden among 38million obituaries Scientists on a mission to uncover what constitutes a life well lived found the answer after analyzing 38 million obituaries from the US spanning 30 years. Using automated text analysis tools, the team found that the most commonly celebrated values were tradition and benevolence. Nearly 80 percent of obituaries highlighted respect for customs or religion, while 76 percent emphasized caring, reliability and trustworthiness.

The effective altruism movement, led by RAND CEO Jason Matheny, may challenge Trump's plans for American artificial intelligence dominance through regulatory advocacy.

Technology Engineering The'Star Trek' technology that came to real life Breakthroughs, discoveries, and DIY tips sent every weekday. To celebrate Star Trek Day on September 8, the European Space Agency (ESA) released a video of the Star Trek technology that's made it real-life space. So while we still don't have teleporters or deflector shields, ISS astronauts kind of have tricorders like the one used by Captain Christopher Pike in the first episode of the original series. We've also seen the development of technology that resembles Replicators, VISOR, and PADDs. The original premiered on network television in the United States on September 8, 1966.

The Great Canadian Treasure Hunt's first clue is a 13-stanza poem. Breakthroughs, discoveries, and DIY tips sent every weekday. An actual treasure chest filled with around $1 million in gold coins is hidden somewhere in Canada. However, the mystery isn't tied to a centuries' old pirate bounty or unsolved bank heist, however. These riches were instead intentionally hidden by a mining consortium to celebrate the country's "rich mining heritage and spirit of adventure."

Breakthroughs, discoveries, and DIY tips sent every weekday. The smelly, brown seaweed can put a damper on a day at the beach at best and hinder baby turtles on their way to the ocean at worst. Only about one in 1,000 sea turtle hatchlings survive to adulthood, and might be added to their already long list of challenges . The new findings detailed in a study published in the explores the role that this brown seaweed plays on vulnerable sea turtle populations. "For sea turtle hatchlings, reaching the ocean is already a race against time - and survival. Now, increasingly large mats of sargassum are adding new challenges to this critical journey," study co-author and Florida Atlantic University biologist Sarah Milton, said in a statement .