Barron Trump's mystery words to Joe Biden after his father's fierce inauguration speech are finally revealed Republican representative blasts Trump's Epstein investigation as a'smokescreen' while survivors beg Congress to finally reveal the hidden files: Live updates This is the simple weight loss trick I'd recommend to any woman over 50 who wants to burn calories and tone up - without working out or eating less: KERRY POTTER She runs the anonymous real-life Gossip Girl account outing Hollywood scandals now we expose HER identity and the secret life she's desperate to hide Keith Urban breaks his silence following split with Nicole Kidman - after admitting he's been'completely lonely and miserable' Olivia Nuzzi says Robert F. Kennedy Jr. didn't have a'brain worm' after all I was blackmailed and forced to do the unthinkable. DEAR JANE, how do I hide it from my wife? Trump turns to Epstein's lawyer to prove he has'nothing to hide' as he orders GOP to vote on releasing ALL documents to avert MAGA mutiny Terrifying rise of'taboo cancer': Doctors reveal subtle signs ALL women must know... the most common cause... and a game-changer shot that could save your life Jeff Bezos's ex MacKenzie Scott contributes more than $700MILLION to'historically black colleges' So many single men are taking this new drug cocktail before dates. The results in the bedroom are startling... as I discovered during one marathon session: JANA HOCKING I felt so ill I thought I was dying... but it was a gut condition doctors often wrongly dismiss as IBS: It's so common and there's misinformation about how to treat it, says Nicola... but this is what really works Boy, 9, accused of raping and brutally attacking girl, 5, is allowed home with ankle monitor... despite victim's mom pleading with judge Meghan's ex-husband's reaction to finding out she was dating Prince Harry is revealed by his pal Bethenny Frankel Trump wades into the most explosive debate in right wing media: 'People must decide' My Montecito mole tells me why Me-Me-Meghan'threw a fit' after Kris Jenner's birthday party... this Kardashian drama just won't go away: KENNEDY If I had to start over, here's how I'd make millions again! KEVIN O'LEARY reveals best investments, the career with soaring salaries and worst mistake he made Nigerian gunmen abduct'dozens' of girls from boarding school after killing deputy head teacher in chilling echo of Chibok kidnappings Conor McGregor's friend Dillon Danis slapped with LIFETIME ban by UFC chief Dana White after being involved in ringside brawl READ MORE: Here's what happens in football fans' brains when their team loses Scientists have unveiled the most detailed map of the brain ever created.

Trump turns to Epstein's lawyer to prove he has'nothing to hide' as he orders GOP to vote on releasing ALL documents to avert MAGA mutiny If I had to start over, here's how I'd make millions again! KEVIN O'LEARY reveals best investments, the career with soaring salaries and worst mistake he made My Montecito mole tells me why Me-Me-Meghan'threw a fit' after Kris Jenner's birthday party... this Kardashian drama just won't go away: KENNEDY Jeff Bezos's ex MacKenzie Scott contributes more than $700MILLION to'historically black colleges' Boy, 9, accused of raping and brutally attacking girl, 5, is allowed home with ankle monitor...despite victim's mom pleading with judge Smiling girl, 14, who vanished without a trace is found dead in RV... as cops arrest her family member Marjorie Taylor Greene's lookalike daughter defends mom against'fake MAGA' attacks amid fallout with Trump Trump crashes Mar-a-Lago wedding to talk about getting into heaven... but MAGA Christians are left angry Terrifying rise of'taboo cancer': Doctors reveal subtle signs ALL women must know... the most common cause... and a game-changer shot that could save your life She runs the anonymous real-life Gossip Girl account outing Hollywood scandals now we expose HER identity and the secret life she's desperate to hide Ariana Grande and Cynthia Erivo cause a stir with ANOTHER'ridiculous' red carpet moment Residents of city dubbed the'Birthplace of Silicon Valley' that's home to Mark Zuckerberg are sick of sleepless nights Nigerian gunmen abduct'dozens' of girls from boarding school after killing deputy head teacher in chilling echo of Chibok kidnappings Emily Blunt's asymmetrical frock horror and Kate Hudson's drab dress lead worst dressed stars at 16th Governors Awards Timothee Chalamet reveals his'true feelings' about ex Kylie Jenner after avoiding the'Kardashian curse' that has destroyed the lives and careers of the sisters' famous exes Iconic O.J. Simpson witness looks VERY different 30 years after legendary murder trial... see him now Most parents try to get them over and done with as soon as possible - but woke scientists now claim that nappy changes should be used as an opportunity to teach babies consent. Dr Nicole Downs and Dr Katherine Bussey, lecturers in Early Childhood at Deakin University in Australia, maintain that parents should not wait until their kids are teenagers to talk about appropriate touching. Instead, consent should be a'normal, everyday part of life' that teaches babies what is acceptable when it comes to their bodies. Parents should take their children's views into account, according to the pair - even when it comes to dealing with a dreaded nappy disaster.

From 10th to 12th September 2025, Barcelona hosted an academic gathering at the Universitat Oberta de Catalunya: the first Hype Studies Conference, titled "(Don't) Believe the Hype!?" Organised by a transnational, collective research group of scholars and practitioners, the conference drew together researchers, activists, artists, journalists, and technology professionals to examine hype as a significant force shaping contemporary society. Hype Studies is an emerging academic field that analyses how and why excessive expectations form around technologies, ideas, or phenomena, and what effects those expectations have on society, culture, economics, and policy. As the playful brackets around "Don't" in the conference title suggest - both a warning and an invitation to question that warning - the aim of the conference wasn't to simply reject hype, but to understand it. The conference approached hype critically by examining it as a phenomenon with real power and consequences that needs to be understood and questioned. The purpose here was to build collective knowledge about hype, develop better and more concrete theories, share empirical findings, and create an interdisciplinary community whilst advancing the field's scholarship and knowledge.

Direct Preference Optimization (DPO) has emerged as a simple alternative to reinforcement learning from human feedback (RLHF) for aligning language models, but its reliance on hard pairwise labels makes it brittle under noise; our experiments show performance degrading by up to 93 percent in noisy settings. We introduce Anchored Direct Preference Optimization (ADPO), a unified framework that addresses this fragility through reference anchoring. By minimizing KL(q || softmax((l - l_ref) / tau_anc)), where l_ref are reference policy log probabilities, ADPO provides three key advantages: (1) it unifies major learning paradigms, including supervised fine-tuning, knowledge distillation, maximum-entropy reinforcement learning, and DPO, as special cases through different choices of target distribution q, anchor policy pi_ref, and temperature tau_anc; (2) it induces an implicit trust region governed by the softmax Fisher metric with curvature scaling as 1 / tau_anc^2, providing geometric regularization absent in standard methods; and (3) it enables flexible anchor strategies tailored to different learning contexts. Empirically, ADPO consistently outperforms standard DPO by 12 to 93 percent across twelve noisy scenarios, with listwise variants achieving top performance in eleven of twelve cases. In offline distillation, ADPO reduces student-teacher KL by 4 to 49 times while achieving superior returns (for example, 279.3 vs -309.0 for knowledge distillation on HalfCheetah). We further uncover a task-dependent tradeoff: dynamic anchors excel at online exploration in noisy environments (plus 5 to 11 percent), while fixed anchors enable stable offline distillation. Our work establishes anchoring as a general principle for robust policy optimization, with clear practical guidance for anchor selection across diverse learning scenarios.

There has been a prevalence of applying AI software in both high-stakes public-sector and industrial contexts. However, the lack of transparency has raised concerns about whether these data-informed AI software decisions secure fairness against people of all racial, gender, or age groups. Despite extensive research on emerging fairness-aware AI software, up to now most efforts to solve this issue have been dedicated to binary classification tasks. Fairness in regression is relatively underexplored. In this work, we adopted a mutual information-based metric to assess separation violations. The metric is also extended so that it can be directly applied to both classification and regression problems with both binary and continuous sensitive attributes. Inspired by the Reweighing algorithm in fair classification, we proposed a FairReweighing pre-processing algorithm based on density estimation to ensure that the learned model satisfies the separation criterion. Theoretically, we show that the proposed FairReweighing algorithm can guarantee separation in the training data under a data independence assumption. Empirically, on both synthetic and real-world data, we show that FairReweighing outperforms existing state-of-the-art regression fairness solutions in terms of improving separation while maintaining high accuracy.

Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge. Vision-language models such as CLIP offer strong transferable representations via multi-modal supervision, making them promising for CIL. However, applying CLIP to CIL poses two major challenges: (1) adapting to downstream tasks often requires additional learnable modules, increasing model complexity and susceptibility to forgetting; and (2) while multi-modal representations offer complementary strengths, existing methods have yet to fully realize their potential in effectively integrating visual and textual modalities. To address these issues, we propose BOFA (Bridge-layer Orthogonal Fusion for Adaptation), a novel framework for CIL. BOFA confines all model adaptation exclusively to CLIP's existing cross-modal bridge-layer, thereby adding no extra parameters or inference cost. To prevent forgetting within this layer, it leverages Orthogonal Low-Rank Fusion, a mechanism that constrains parameter updates to a low-rank ``safe subspace" mathematically constructed to be orthogonal to past task features. This ensures stable knowledge accumulation without data replay. Furthermore, BOFA employs a cross-modal hybrid prototype that synergizes stable textual prototypes with visual counterparts derived from our stably adapted bridge-layer, enhancing classification performance. Extensive experiments on standard benchmarks show that BOFA achieves superior accuracy and efficiency compared to existing methods.

Navigating human-populated environments without causing discomfort is a critical capability for socially-aware agents. While rule-based approaches offer interpretability through predefined psychological principles, they often lack gener-alizability and flexibility. Conversely, data-driven methods can learn complex behaviors from large-scale datasets, but are typically inefficient, opaque, and difficult to align with human intuitions. To bridge this gap, we propose RLSLM, a hybrid Reinforcement Learning framework that integrates a rule-based Social Locomotion Model, grounded in empirical behavioral experiments, into the reward function of a reinforcement learning framework. The social locomotion model generates an orientation-sensitive social comfort field that quantifies human comfort across space, enabling socially aligned navigation policies with minimal training. RL-SLM then jointly optimizes mechanical energy and social comfort, allowing agents to avoid intrusions into personal or group space. A human-agent interaction experiment using an immersive VR-based setup demonstrates that RLSLM outperforms state-of-the-art rule-based models in user experience. Ablation and sensitivity analyses further show the model's significantly improved interpretability over conventional data-driven methods. This work presents a scalable, human-centered methodology that effectively integrates cognitive science and machine learning for real-world social navigation.

Learning governing equations from data is central to understanding the behavior of physical systems across diverse scientific disciplines, including physics, biology, and engineering. The Sindy algorithm has proven effective in leveraging sparsity to identify concise models of nonlinear dynamical systems. In this paper, we extend sparsity-driven approaches to real-time learning by integrating a cornerstone algorithm from control theory -- the Kalman filter (KF). The resulting Sindy Kalman Filter (SKF) unifies both frameworks by treating unknown system parameters as state variables, enabling real-time inference of complex, time-varying nonlinear models unattainable by either method alone. Furthermore, SKF enhances KF parameter identification strategies, particularly via look-ahead error, significantly simplifying the estimation of sparsity levels, variance parameters, and switching instants. We validate SKF on a chaotic Lorenz system with drifting or switching parameters and demonstrate its effectiveness in the real-time identification of a sparse nonlinear aircraft model built from real flight data.

The advent of Intelligent Tutoring Systems (ITSs) has marked a paradigm shift in education, enabling highly personalized learning pathways. However, true personalization requires adapting to learners' complex knowledge states (multi-source) and diverse goals (multi-sink); existing ITSs often lack the necessary structural-reasoning capability and knowledge dynamism to generate genuinely effective learning paths, and they lack scientifically rigorous validation paradigms. In this paper we propose GraphMASAL (A Graph-based Multi-Agent System for Adaptive Learning), which integrates (i) a dynamic knowledge graph for persistent, stateful learner modeling; (ii) a LangGraph-orchestrated trio of agents (Diagnostician, Planner, Tutor); (iii) a knowledge-graph-grounded two-stage neural IR component (dual-encoder dense retrieval with cross-encoder listwise re-ranking and calibrated score fusion); and (iv) a multi-source multi-sink (MSMS) planning engine with a cognitively grounded cost and an approximation guarantee via greedy set cover. Under blinded automated evaluations with matched inputs and inference settings across diverse student profiles, GraphMASAL consistently outperforms LLM prompting and structured ablations in planning--achieving stronger structural/sequence alignment of learning paths, higher coverage of weak concepts, and lower learning cost--while also surpassing prompt-based baselines in cognitive diagnosis. Agreement with expert/LLM-proxy ratings further supports the validity of our evaluation protocol. These findings indicate that grounding LLM agents in a dynamic knowledge graph, coupled with optimization under educational constraints, yields reliable, interpretable, and pedagogically plausible learning plans, advancing personalized and goal-oriented education.

This paper explores the automatic classification of exam questions and learning outcomes according to Bloom's Taxonomy. A small dataset of 600 sentences labeled with six cognitive categories - Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation - was processed using traditional machine learning (ML) models (Naive Bayes, Logistic Regression, Support Vector Machines), recurrent neural network architectures (LSTM, BiLSTM, GRU, BiGRU), transformer-based models (BERT and RoBERTa), and large language models (OpenAI, Gemini, Ollama, Anthropic). Each model was evaluated under different preprocessing and augmentation strategies (for example, synonym replacement, word embeddings, etc.). Among traditional ML approaches, Support Vector Machines (SVM) with data augmentation achieved the best overall performance, reaching 94 percent accuracy, recall, and F1 scores with minimal overfitting. In contrast, the RNN models and BERT suffered from severe overfitting, while RoBERTa initially overcame it but began to show signs as training progressed. Finally, zero-shot evaluations of large language models (LLMs) indicated that OpenAI and Gemini performed best among the tested LLMs, achieving approximately 0.72-0.73 accuracy and comparable F1 scores. These findings highlight the challenges of training complex deep models on limited data and underscore the value of careful data augmentation and simpler algorithms (such as augmented SVM) for Bloom's Taxonomy classification.