Many current state-of-the-art models for sequential recommendations are based on transformer architectures. Interpretation and explanation of such black box models is an important research question, as a better understanding of their internals can help understand, influence, and control their behavior, which is very important in a variety of real-world applications. Recently sparse autoencoders (SAE) have been shown to be a promising unsupervised approach for extracting interpretable features from language models. These autoencoders learn to reconstruct hidden states of the transformer's internal layers from sparse linear combinations of directions in their activation space. This paper is focused on the application of SAE to the sequential recommendation domain. We show that this approach can be successfully applied to the transformer trained on a sequential recommendation task: learned directions turn out to be more interpretable and monosemantic than the original hidden state dimensions. Moreover, we demonstrate that the features learned by SAE can be used to effectively and flexibly control the model's behavior, providing end-users with a straightforward method to adjust their recommendations to different custom scenarios and contexts.

Conversational recommender systems (CRSs) often suffer from an extreme long-tail distribution of dialogue data, causing a strong bias toward head-frequency blockbusters that sacrifices diversity and exacerbates the cold-start problem. An empirical analysis of DCRS and statistics on the REDIAL corpus show that only 10% of head movies account for nearly half of all mentions, whereas about 70% of tail movies receive merely 26% of the attention. This imbalance gives rise to three critical challenges: head over-fitting, body representation drift, and tail sparsity. To address these issues, we propose LumiCRS, an end-to-end framework that mitigates long-tail imbalance through three mutually reinforcing layers: (i) an Adaptive Comprehensive Focal Loss (ACFL) that dynamically adjusts class weights and focusing factors to curb head over-fitting and reduce popularity bias; (ii) Prototype Learning for Long-Tail Recommendation, which selects semantic, affective, and contextual prototypes to guide clustering and stabilize body and tail representations; and (iii) a GPT-4o-driven prototype-guided dialogue augmentation module that automatically generates diverse long-tail conversational snippets to alleviate tail sparsity and distribution shift. Together, these strategies enable LumiCRS to markedly improve recommendation accuracy, diversity, and fairness: on the REDIAL and INSPIRED benchmarks, LumiCRS boosts Recall@10 and Tail-Recall@10 by 7-15% over fifteen strong baselines, while human evaluations confirm superior fluency, informativeness, and long-tail relevance. These results demonstrate the effectiveness of multi-layer collaboration in building an efficient and fair long-tail conversational recommender.

Manus AI is a general-purpose AI agent introduced in early 2025, marking a significant advancement in autonomous artificial intelligence. Developed by the Chinese startup Monica.im, Manus is designed to bridge the gap between "mind" and "hand" - combining the reasoning and planning capabilities of large language models with the ability to execute complex, end-to-end tasks that produce tangible outcomes. This paper presents a comprehensive overview of Manus AI, exploring its core technical architecture, diverse applications across sectors such as healthcare, finance, manufacturing, robotics, and gaming, as well as its key strengths, current limitations, and future potential. Positioned as a preview of what lies ahead, Manus AI represents a shift toward intelligent agents that can translate high-level intentions into real-world actions, heralding a new era of human-AI collaboration.

In Ari Aster's movies, the price of understanding how the world really works is your sanity, if not your life. His first three movies--Hereditary, Midsommar, and Beau Is Afraid--center on characters whose feeling that there's something sinister going on beneath the surface of their existence is eventually proved to be correct, but it's as if their bodies aren't equipped to contain that knowledge. One way or another, their minds are gone. The people in Aster's polarizing fourth movie, Eddington, a Western-inflected psychodrama set during the early days of the COVID-19 pandemic, don't get off so easy. The stress test of a rapidly spreading virus with no known treatment exposes innumerable cracks in society's facade: the gap between remote workers and people forced to risk their lives in order to earn a living; between people who breathe a sigh of relief when they see a police car approaching and people who have to be sure to keep their hands in plain sight.

The CyberGuy Kurt Knutsson joins'Fox & Friends' to discuss the U.S.-Saudi investment summit and the debate over regulation as artificial intelligence continues to advance. What happens when one of the world's richest companies decides to go all-in on artificial intelligence? If you're Meta Platforms CEO Mark Zuckerberg, it means launching superclusters so large they could rival the footprint of Manhattan. Recently, Zuckerberg unveiled plans to invest "hundreds of billions of dollars" into next-generation AI infrastructure, including some of the largest compute clusters the world has ever seen. Meta's first supercluster, called Prometheus, is slated to go live in 2026.

OpenAI's incoming CEO of applications, Fidji Simo, sent her first note to staff on Monday, telling employees the tools they're developing "will unlock more opportunities for more people than any other technology in history." "If we get this right, AI can give everyone more power than ever," Simo wrote, striking a hyper-optimistic tone, according to a copy of the memo viewed by WIRED. "But I also realize those opportunities won't magically appear on their own." Simo previously worked as the CEO of Instacart. Before that, she spent a decade at Meta, where she went from being a product manager on the company's news feed to the head of product for the Facebook app.

The arrival of AI chatbots marks a historical dividing line after which online material can't be completely trusted to be human-created, but how will people look back on this change? While some are urgently working to archive "uncontaminated" data from the pre-AI era, others say it is the AI outputs themselves that we need to record, so future historians can study how chatbots have evolved. Rajiv Pant, an entrepreneur and former chief technology officer at both The New York Times and The Wall Street Journal, says he sees AI as a risk to information such as news stories that form part of the historical record. "I've been thinking about this'digital archaeology' problem since ChatGPT launched, and it's becoming more urgent every month," says Pant. "Right now, there's no reliable way to distinguish human-authored content from AI-generated material at scale. For John Graham-Cumming at cybersecurity firm Cloudflare, information produced before the end of 2022, when ChatGPT launched, is akin to low-background steel. This metal, smelted before the Trinity nuclear bomb test on 16 July 1945, is prized for use in delicate scientific and medical instruments because it doesn't contain faint radioactive contamination from the atomic weapon era that creates noise in readings. Graham-Cumming has created a website called lowbackgroundsteel.ai to archive sources of data that haven't been contaminated by AI, such as a full download of Wikipedia from August 2022. Studies have already shown that Wikipedia today shows signs of huge AI input. "There's a point at which we we did everything ourselves, and then at some point we started to get augmented significantly by these chat systems," he says. "So the idea was to say – you can see it as contamination, or you can see it as a sort of a vault – you know, humans, we got to here.

Kevin Czinger, Divergent Technologies Executive Chairman, joined The Brian Kilmeade Show to discuss how his 3-D company can shorten manufacturing time from 12 days to 18 hours and deliver Predator and Reaper drones and reduce the costs by over 50%. Qatar is taking bold steps to transform its educational infrastructure. To lead this change, the country has launched one of the world's largest 3D-printed construction projects. UCC Holding and the Public Works Authority (Ashghal) are heading the effort. As part of the plan, Qatar will build 14 public schools.

In todayś digital world, fake news is spreading with immense speed. Its a significant concern to address. In this work, we addressed that challenge using novel graph based approach. We took dataset from Kaggle that contains real and fake news articles. To test our approach we incorporated recent covid-19 related news articles that contains both genuine and fake news that are relevant to this problem. This further enhances the dataset as well instead of relying completely on the original dataset. We propose a contextual graph-based approach to detect fake news articles. We need to convert news articles into appropriate schema, so we leverage Natural Language Processing (NLP) techniques to transform news articles into contextual graph structures. We then apply the Minimum Description Length (MDL)-based Graph-Based Anomaly Detection (GBAD) algorithm for graph mining. Graph-based methods are particularly effective for handling rich contextual data, as they enable the discovery of complex patterns that traditional query-based or statistical techniques might overlook. Our proposed approach identifies normative patterns within the dataset and subsequently uncovers anomalous patterns that deviate from these established norms.

The modern recommender systems are facing an increasing challenge of modelling and predicting the dynamic and context-rich user preferences. Traditional collaborative filtering and content-based methods often struggle to capture the temporal patternings and evolving user intentions. While Large Language Models (LLMs) have gained gradual attention in recent years, by their strong semantic understanding and reasoning abilities, they are not inherently designed to model chronologically evolving user preference and intentions. On the other hand, for sequential models like LSTM (Long-Short-Term-Memory) which is good at capturing the temporal dynamics of user behaviour and evolving user preference over time, but still lacks a rich semantic understanding for comprehensive recommendation generation. In this study, we propose DUALRec (Dynamic User-Aware Language-based Recommender), a novel recommender that leverages the complementary strength of both models, which combines the temporal modelling abilities of LSTM networks with semantic reasoning power of the fine-tuned Large Language Models. The LSTM component will capture users evolving preference through their viewing history, while the fine-tuned LLM variants will leverage these temporal user insights to generate next movies that users might enjoy. Experimental results on MovieLens-1M dataset shows that the DUALRec model outperforms a wide range of baseline models, with comprehensive evaluation matrices of Hit Rate (HR@k), Normalized Discounted Cumulative Gain (NDCG@k), and genre similarity metrics. This research proposes a novel architecture that bridges the gap between temporal sequence modeling and semantic reasoning, and offers a promising direction for developing more intelligent and context-aware recommenders.