For those who don't know, wood therapy involves rolling hand-held wooden tools over the body and is meant to reduce cellulite, help break down fat and promote blood circulation as well as smoother skin ... Furthermore, she says she gets "holistic body work" as well as wood therapy. Even me releasing the weight has affected people and I take that seriously. I wanted to win.” She also expressed her desire to be intentional about her language because “there’s young people who are watching me and they’re experiencing what I’m putting into the world.” Across 14 LLM judges, we observe clear susceptibility to framing, with model families showing distinct tendencies toward agreement or rejection. We design symmetric prompts using predicate-positive and predicate-negative constructions and demonstrate that such framing induces significant discrepancies in model outputs. Drawing inspiration from the framing effect in psychology, we systematically investigate how deliberate prompt framing skews model judgments across four high-stakes evaluation tasks. Evaluated on six diverse hate speech datasets, extsfHateXScore is intended as a diagnostic complement to reveal interpretability failures and annotation inconsistencies that are invisible to standard metrics like Accuracy or F1. In particular, our 70B DPO model achieved the best score on 4/6 reasoning tasks, among baselines and a 141B SOTA legal LLM, demonstrating the effectiveness of our KG for enhancing LLMs’ legal reasoning capability.zh Our post-trained models obtained better average performance on 4/5 diverse legal benchmarks (14 tasks) than baselines. We then produce training data using our IRAC KG, and conduct both Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) with three state-of-the-art (SOTA) LLMs (30B, 49B and 70B), varying architecture and base model family. This has caused models to struggle dealing with complex reasoning tasks, especially for high-stakes professional domains. Hence, future models may only require prosody, providing privacy and potential performance benefits. Then, we propose Semantic-aware Feature Regularization (SAFR) that constrains feature learning to prevent overfitting to source domain characteristics. To overcome this challenge, we propose Debiased Source-free Object Detection (DSOD), a novel VFM-assisted SFOD framework that can effectively mitigate source bias with the help of powerful VFMs. Extensive real-world evaluations confirm that our framework delivers superior generalization and robustness in complex environments. The four-time Grammy winner’s posts come as she’s kicking off a new musical era, dropping singles “Love in Real Life” and “Still Bad” in February and March, respectively. Lizzo has been open about her fitness journey after becoming known for her self-love-focused music and advocating for body positivity with her platform. The “About Damn Time” singer also stopped eating sugary foods in the morning, opting instead for a mix of sweet and savory breakfast choices, like “almond butter and toast.” “Everybody’s body is different. Lizzo is opening up about the steps she took to feel lighter both physically and mentally, with the star sharing her weight-loss and anxiety-beating strategies on an TikTok livestream Tuesday (April 22). The “Truth Hurts” singer said some people thought she was being “performative” and had “internalized fatphobia,” but she was tired of her “identity being overshadowed” by her weight. However, their evaluation is often limited to accuracy on medical multiple choice question (MCQ) benchmarks, and lacks evaluation of consistency, robustness, or reasoning behavior. Experiments across large-scale pre-training (English C4, Vietnamese VietVault) and fine-tuning (GLUE) demonstrate that AdaFRUGAL achieves a compelling trade-off. The FRUGAL framework mitigates this with gradient splitting, but its static hyperparameters – the subspace ratio ( ho ) and update frequency ( T ) – require costly manual tuning, limiting adaptability. The open-source datasets produced include over 28,000 QA pairs for PRSGPT and 154,282 for BioStarsGPT. In Law, reasoning requires deep understanding of the relations between various legal concepts, a key component missing in current LLM post-training. When either prosodic or lexical information is disrupted, the model exploits the other without further training, indicating they are encoded in S3Rs with limited interdependence. To mitigate this limitation, we curate a 7K-sample instruction-tuning dataset and propose an Omni-Modal Future Forecasting (OFF) training strategy. Evaluations on 13 omni-modal and 7 video-only models show that current systems struggle with audio-visual future prediction, particularly in speech-heavy scenarios, with the best accuracy of 64.8% achieved by Gemini 3 Flash. Related Posts To address this, we created the BirdsEye-RU dataset, a comprehensive collection of 2,978 images containing over eight thousand annotated faces. We conclude self-supervised eye movement reconstruction is an effective method for encoding the affective signal they carry.zh By combining adjoint operator initialization with semantic guidance, PISE improves classification accuracy by 2.57% and reduces variance by 9x at 5% sampling.zh These capabilities position our methods as promising tools for accelerating target triage, optimizing observation schedules, and maximizing scientific return for upcoming flagship missions such as HWO.zh Unlike existing PnP solvers, the proposed extttPnP-ProCay78 algorithm combines projection error minimization with an analytically eliminated reconstruction-error surrogate for translation, yielding a hybrid cost formulation that is both geometrically transparent and computationally efficient.zh Addressing the constant focus on her appearance, Lizzo firmly stated, "My body is nobody's business." A separate post featured her dressed modestly in a body-hugging black jumpsuit and leather jacket. To shut down doubters, she also shared evidence from her fitness app, showing she had lost 10.5 points on the Body Mass Index (BMI) scale and also reduced her body fat by 16%. "Even at the end of my weight-loss journey, I'm not going to be considered thin by any means," Lizzo added. The image also had the words "Need self love? Try Lizzo! Lose guilt, gain confidence," seemingly to inspire others who have been trolled for their weight.We introduce a quantitative evaluation framework that combines localization accuracy, perturbation-based faithfulness, and explanation consistency to assess Grad-CAM reliability across architectures.Under non-IID data, these uniformly weighted updates can be strongly misaligned across clients, causing client drift and degrading the global model.We propose a training-time interpretability view that tracks token-level attributions across finetuning epochs.This creates a vast and often opaque configuration space, making it challenging for developers to understand performance trade-offs and identify optimal designs."The idea of body positivity, it's moved away from the antiquated mainstream conception. It's evolved into body neutrality." While she admitted that she may not love her body all the time, she does take good care of it.The resulting ML-ready SolARED dataset is designed to support enhancements of predictive capabilities, enabling the development of operational forecasts for the emergence of active regions. Lizzo is no stranger to the spotlight, but this time, it’s not just about her musical accomplishments or public persona—it’s about how she took control of her physical and mental well-being, and the results have been stunning. Not only did Lizzo change her diet and fitness regimen to drop the weight, but she added that her struggles with anxiety have played a major factor in her health. She then opened up about her old diet, explaining that she used to drink between “two to three” large Starbucks drinks a day that sometimes added up to 1,200 calories. The “Good as Hell” singer added that she the most consistent way for her to lose weight is by being aware of the “calories in versus calories out.” Katie Price married for fourth time after whirlwind week-long romance with Lee Andrews Our experiments demonstrate that models enhanced with sentence context achieve superior performance over canonical decoder-only baselines. To address this gap, we propose PEARL, a reinforcement-learning framework that augments language agent with an external memory module and optimized round-wise reward design, enabling agent to progressively infer and adapt to user preferences on-the-fly. Evaluations on 13 cutting-edge RMs indicate a diminishing performance gap between open-source and proprietary models, with newer-generation models consistently outperforming their predecessors regardless of parameter count. Experiments on three large-scale datasets show our method matches or even surpasses the performance of retraining from scratch, reducing computational cost by up to 98%. We expect this benchmark to enable fair comparisons, facilitate the development of more robust detection models, and promote the safe and responsible deployment of VTON technologies in practice.zh To further advance detection, we design a multi-task framework that integrates auxiliary segmentation to enhance boundary-aware feature learning, achieving the best overall performance on VTONGuard. DExTeR achieves state-of-the-art performance across three datasets spanning different medical domains (endoscopy, chest X-rays, and endoscopic ultrasound) highlighting its potential to reduce annotation costs while maintaining high detection accuracy.zh Now, I guess it’s time to set new goals.” In a follow-up post, Lizzo revealed that she has dropped both 10.5 on her BMI and 16 percent body fat since January 16, 2023. Since starting her weight-loss journey in 2024, Lizzo has been vocal about how she dropped the fat. One of the images featured a stat that indicated Lizzo has shed 16% of her body fat. Lizzo Debuts New Hair Cut & Color in Social Media Transformation ARMARecon effectively models both local and global connectivity by leveraging 20-bin Fractional Anisotropy (FA) histogram features extracted from white-matter regions, while mitigating over-smoothing. Hence, we introduce ARMARecon, a unified graph learning framework that integrates Autoregressive Moving Average (ARMA) graph filtering with a reconstruction-driven objective to enhance feature representation and improve classification accuracy. To support effective modeling, we construct EmoSpace Set, a large-scale benchmark dataset comprising images with dense annotations on emotions, object semantics, and visual attributes. Building upon EmoLat, a cross-modal sentiment transfer framework is proposed to manipulate image sentiment via joint embedding of text and EmoLat features. Considering the data dilemma, we term the processing of synthetic data as domain adaptation problem where we propose a domain-invariant learning strategy to focus on foreground learning. Lizzo has been turning heads over the past few months with her incredible weight loss transformation. She has been seeing Lanini for lymphatic massages, and she showed off her striking before-and-after weight loss in a side-by-side picture comparison. Lizzo has made her commitment to her weight loss journey no secret online, and has also made it clear that it's purely for her own desires and that there's so much more involved than just losing weight. However, existing work has mainly focused on efficiency and performance, while the security implications of visual token compression remain largely unexplored. Our work provides a critical tool for improving the transparency and reliability of privacy evaluations, enabling safer use of synthetic data in health-related applications. A major challenge is the absence of accessible benchmark datasets for evaluating privacy risks, due to difficulties in acquiring sensitive data. Through a within-subjects study with 20 participants, we show that triadic collaboration enhances collaborative learning and social presence compared to the dyadic human-AI (HAI) baseline. In recent years, Lizzo has undergone a dramatic transformation and recently revealed that she has reached her goal weight. Your body will never be good enough for them because it’s not FOR them. “Let me be a reminder to everyone to NEVER let anyone shame you for what you choose to do with your body. “I’ve been methodical, losing weight very slowly. She continued, “Will my body fluctuate from this size? To address this gap, we introduce SciHorizon-GENE, a large-scale gene-centric benchmark constructed from authoritative biological databases. This study presents the first comprehensive evaluation of transformer-based architectures for detecting minority stress in online discourse. It should also be evaluated outside of commonly used benchmarks for LLMs, as these do not adequately capture the complexities of this kind of conversational system.zh Instead of relying on rule-based interactions, agents can now interact in flexible ways akin to humans. Experiments validate that CSyMR-Bench poses a non-trivial challenge across both community-sourced and exam-style questions, while our tool-augmented agent consistently outperforms all baselines, achieving 5-7% absolute accuracy gains.zh While recent benchmarks have begun to evaluate iterative self-correction, its quantitative limits and dominant reasoning bottlenecks remain poorly characterized.In a side-by-side photo shared on Instagram, fans can see that he's lost a considerable amount of weight.Baring her toned body at awards shows and in videos, the tiny girl with the big voice drew almost as much attention for her looks as her lyrics.Experiments on long-context QA benchmarks show that executing plans generated by PPA-Plan consistently outperforms existing plan-and-execute methods and direct prompting.zhOn Instagram, Lizzo hit us with some real talk and jaw-dropping before-and-after pics that’s got everybody buzzing.We also highlight the need for privacy metrics that fairly account for the probabilistic nature of machine learning models.Intermittent fasting usually involves consuming all of your daily calories within a certain window of time, typically eight hours, and then fasting for the remaining hours. Here we present LUMOS, a data-and-physics driven framework for inverse design of fluorescent molecules. Beyond neuroscience, we validate GeoDynamics on human action recognition benchmarks (UTKinect, Florence, HDM05), demonstrating its scalability and robustness in modeling complex spatiotemporal dynamics across diverse domains.zh By combining the flexibility of deep learning with the principled dynamical structure of SSMs, recent studies have achieved powerful fits to functional neuroimaging data. A two-month operational reforecast experiment demonstrates that the machine learning emulator achieves accuracy comparable to that of the traditional physical scheme, while accelerating the computation speed by approximately eightfold.zh Meanwhile, a LibTorch-based coupling method is utilized, which is more suitable for real-time operational computations. Lizzo flaunts unbelievable transformation after reaching her weight loss goal: ‘Haven’t seen the number since 2014’ “I think these are normal thoughts and feelings and they happen to everybody, they happen to the best of us,” she added. The artist’s relationship with body image has been complicated throughout her career, including moments of vulnerability she’s shared publicly. Over a few months, the singer has pushed back against critics who suggest her transformation contradicts her previous body-positivity messaging, firmly stating that her body remains nobody’s business but her own. The Grammy winner’s Twitch video followed a significant milestone announcement Lizzo made in January when she revealed reaching her “weight release goal” through an Instagram post. Two others said, “Omg she lost so much weight but why that’s makes you Lizzo, being a big girl,” and “OMG…. This setting is motivated by applications involving large, sparse panel datasets, where the number of rows far exceeds the number of columns. These results show that joint electronic health record and wearable pretraining yields more faithful representations of longitudinal health. The model uses modality specific encoders and a shared temporal backbone pretrained with self supervised and cross modal objectives. On the CERT r4.2 benchmark, our approach consistently outperforms existing baselines in precision, recall, and F1 score across various time granularities and scenarios. Through extensive experiments on segmentation, super-resolution, and denoising tasks, the proposed SSPFormer achieves state-of-the-art performance, fully verifying its ability to capture fine-grained MRI image fidelity and adapt to clinical application requirements.zh To tackle the domain gap and data scarcity, we introduce inverse frequency projection masking, which prioritizes the reconstruction of high-frequency anatomical regions to enforce structure-aware representation learning. We then construct a motion-labeled dataset to identify features that encode the strongest motion information, and inject them into a structurally identical video generation model. Evaluations on a dataset of 405,000 samples demonstrate that SKANet achieves an overall accuracy of 96.99%, exhibiting superior robustness for compound jamming classification, particularly under low Jamming-to-Noise Ratio (JNR) regimes.zh We propose the Selective Kernel and Asymmetric convolution Network(SKANet), a cognitive deep learning framework built upon a dual-stream architecture that integrates Time-Frequency Images (TFIs) and Power Spectral Density (PSD). Empirical results demonstrate that LIME-LLM establishes a new benchmark for black-box NLP explainability, achieving significant improvements in local explanation fidelity compared to both traditional perturbation-based methods and recent generative alternatives.zh These heuristic perturbations frequently generate semantically invalid, out-of-distribution inputs that weaken the fidelity of local surrogate models. The proposed framework provides a reproducible baseline for interpretable mental health screening across diverse online contexts.zh Using a substantial dataset of Reddit posts, we trained a logistic regression classifier on carefully curated subreddits for training, validation, and test splits. This work presents a transparent approach to social media-based anxiety detection through linguistically interpretable feature-grounded modeling and cross-domain validation. Utilizing distance-based similarity to these shapelets, we facilitate the user to selectively discard unreliable predictions and be informed of the model’s realistic capabilities. We learn shapelets using shift-invariant dictionary learning on the validation split of the target domain dataset. In this paper, we propose a selective forecasting framework to identify these critical segments of time series using shapelets. GeoDynamics embeds each connectivity matrix into a manifold-aware recurrent framework, learning smooth and geometry-respecting transitions that reveal task-driven state changes and early markers of Alzheimer’s disease, Parkinson’s disease, and autism. Experimental results show that the 11B small LLM fine-tuned with ScriptMind outperformed GPT-4o by 13%, achieving superior performance over commercial models in detection accuracy, false-positive reduction, scammer utterance prediction, and rationale quality. We propose ScriptMind, an integrated framework for LLM-based scam detection that bridges automated reasoning and human cognition. To ensure the stability of the hybrid model, the dataset is enhanced via experience replay, and additional output constraints based on physical significance are imposed. At runtime, it enforces per-step least-privilege tool access through adaptive filtering and status-aware validation of tool calls. Agents may retain unnecessary permissions (excessive agency) or fail to invoke required tools (insufficient agency), amplifying the attack surface and reducing performance. In the second stage, a Transformer encoder processes the resulting token sequence to model inter-patch temporal dependencies and generate per-patch forecasts. Token-level self-attention is subsequently applied during representation learning to refine these embeddings by enabling interactions across temporal patches. Crucially, this failure is governed by absolute position rather than the linear distance between facts (performance variance 3% ). This is in contrast with her statements on a TikTok Live chat, where she denied using Ozempic Or Mounjaro to lose weight. When Paytas asked if she took weight-loss medications like Ozempic, she casually admitted, 'I tried everything.' The 37-year-old 'Juice' singer revealed in January that she lost 16% of her body fat and reduced her body mass index (BMI) by 10.5. The American singer and rapper shared how GLP-1 medication helps her achieve her current weight in an interview with Trisha Paytas' Just Trish podcast. Lizzo has always been an advocate of body positivity, but this latest transformation made her the healthiest yet. Hannah Jiles didn't win any fans for the way she treated her fiance Nick Dorka during Season 7 of the Netflix series "Love Is Blind." So if she thought the public would sing her praises over her drastic weight loss transformation, she was mistaken. The singer explained to Men's Health that his weight loss wake up call came after the death of his pal and fellow rapper Big Pun who suffered a heart attack before he was even 30 years old. In fact, he openly acknowledged that a GLP-1 drug, which he began taking for diabetes, has been an integral part of his weight loss journey. We release model checkpoints under Apache 2.0, and publicly release the dataset and extbfLightOnOCR-bbox-bench evaluation under their respective licenses.zh Trained on a large-scale, high-quality distillation mix with strong coverage of scans, French documents, and scientific PDFs, LightOnOCR-2 achieves state-of-the-art results on OlmOCR-Bench while being 9 imes smaller and substantially faster than prior best-performing models. Beyond the novel training design, the model also learns an unprecedented compressed embedding space with outstanding performance for various visual tasks. The results establish sockpuppetting as an effective low-cost attack accessible to unsophisticated adversaries, highlighting the need for defences against output-prefix injection in open-weight models.zh Extensive evaluations demonstrate that with minimal data (20k samples), SRI-Coder enables Chat models to surpass the completion performance of their Base counterparts. This work demonstrates how LLMs can translate unstructured SDOH-related data into structured insights, offering a scalable approach to augment clinical risk models and decision-making.zh Across 4 datasets spanning mathematical, legal, and medical reasoning, we evaluate 11 models using personas representing four expertise levels per domain. We investigate whether language models exhibit systematic bias based on the perceived expertise of the provider of the endorsement. Experiments show that a 50-sample-per-class buffer restores performance to 78-82%, effectively preventing forgetting. LG-36 Bridging the Gap Between Estimated and True Regret Towards Reliable Regret Estimation in Deep Learning based Mechanism Design Numerical experiments on synthetic tensor sensing demonstrate that the proposed algorithm exhibits favorable convergence behavior in wall-clock time compared with representative stochastic tensor recovery baselines. It combines the strengths of both paradigms by dynamically shifting from LLM-driven to BO-driven proposals as data accumulates.The criticism intensified with another user stating, “Hey Lizzo, what happened to body positivity?Bielik 11B v3 not only advances AI capabilities for the Polish language but also establishes a new benchmark for developing resource-efficient, high-performance models for less-represented languages.About Wegovy® pill Wegovy® pill is the first oral GLP-1 medicine for obesity in the US, and is used with a reduced calorie diet and increased physical activity for adults with obesity, or with overweight who also have weight-related medical problems, to help them lose weight and keep it off.To mitigate the need for manual annotations, we leverage high-capacity “expert” models – such as Depth Anything V2 and OWLv2 – to synthesize dense, structured pseudo-labels at scale.To overcome these challenges, we introduce DExTeR (DETR with Experts), a transformer-based Point-to-Box regressor tailored for medical imaging.The proposed HiT-Prithvi model achieved 43 FPS on Jetson Orin Nano, a representative onboard hardware used in nanosats.The LLM Mode integrates these stages into a unified chain-of-thought for end-to-end inference, balancing efficiency and performance.Extensive experiments on the LERF and 3D-OVS datasets demonstrate that our approach significantly reduces transmission cost while preserving high rendering quality and strong segmentation performance.zh Variational inference is introduced to model the uncertainty in attention weights, thereby enhancing robustness to noise. The model combines sliding-window attention to capture short-term anomalies with a recurrent memory pathway that summarizes slower, progressive trends over time. The dimensionality of this latent representation scales linearly with the number of nodes, eliminating the quadratic bottleneck and making it feasible to train larger and more expressive models. But diet alone wasn’t the only thing that helped Lizzo reach her weight loss goals. So, how did she reconcile this with her weight loss journey? In this article, we take a deep dive into Lizzo’s weight loss story, exploring how she achieved her goals and the mindset behind it. Yet SHAP explanations can vary substantially across repeated runs even when the input, task, and trained model are held fixed. These results underscore a critical gap between theoretical medical knowledge and clinical practice ability, establishing MedConsultBench as a rigorous foundation for aligning medical AI with the nuanced requirements of real-world clinical care.zh To bridge this gap, we propose MedConsultBench, a comprehensive framework designed to evaluate the complete online consultation cycle by covering the entire clinical workflow from history taking and diagnosis to treatment planning and follow-up Q\A. We hypothesize that if a PINN accurately learns the underlying dynamics of a physical system, then the Fisher information landscape derived from the PINN’s learned equations of motion will closely match that of the original analytical model.Lizzo also added that she was trying to achieve "my ideal body type" and that her fans wouldn't have a say in the matter.Experiments on real-world data show that manipulating a one-day attack over 14 months can reliably mislead LLMs and reduce annual returns by up to 17.7 percentage points.Further, we demonstrate that EVO achieves a lower probability of constraint violations than expectation-based methods and exhibits lower variance than quantile regression methods.For the first time, we define metrics for this problem; and we evaluate our method on the Argoverse 2, MAN TruckScenes, and our proprietary datasets.(Notably, the not-yet-approved orforglipron pill can be taken with or without food, at any time of day.)This paper presents AdaNODEs, an innovative source-free TTA method tailored explicitly for time series forecasting.The Great British Bake Off star achieved her dramatic transformation by exercising with a personal trainer four times per week.Lizzo shared a major weight loss goal with fans, revealing she’d officially lost 16 percent of her body fat.Although traditional methods relied mainly on classical deterministic and probabilistic approaches, recent progress has benefited from supervised deep learning (DL) and deep reinforcement learning (DRL) to improve tract reconstruction. The framework reproduces theoretical resource boundaries for Werner-like and Bell-diagonal states with fidelities exceeding ~98%, establishing adversarial learning as a lightweight yet effective method for constraint-driven quantum-state discovery. We address this with a Model-Agnostic Meta-Learning (MAML) co-initialization that jointly sets the control filter and the secondary-path model for FxLMS-based ANC while keeping the runtime algorithm unchanged. We carry out the first study comparing state-of-the-art local (iETS, TweedieGP) and global models (D-Linear, DeepAR, Transformers) on intermittent time series. This formulation departs from prior prompt optimization methods that rely on per-sample supervision and provides a principled mechanism for automatic feature discovery from unstructured text.zh Instruction prompts are iteratively refined based on this structured feedback, enabling optimization over prompts that induce shared feature sets rather than per-example predictions. By grounding data instances onto this tree, a tree-aware metric then quantifies data quality and diversity, facilitating effective sampling. Subsequently, a hybrid deep learning model combining Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU) is adopted to extract spatial correlations and temporal dependencies for predicting future congestion risks. This work builds towards a unified understanding of the fairness-privacy-accuracy relationship and highlights its data-dependent nature.zh Finally, we propose a method for estimating Chernoff Information on data from unknown distributions and utilize this framework to examine the triad dynamic on real datasets. Motivated by this gap, we propose a contrastive causality metric that explicitly isolates inter-step causal dependencies, and demonstrate that it yields more faithful output selection than existing probability-based approaches.zh Surprisingly, across diverse model families and reasoning benchmarks, we find that selection accuracy degrades only marginally under these disruptions. Robust safety, therefore, requires flexible, runtime-enforceable policies and benchmarks that reflect local norms, harm scenarios, and cultural expectations. Compared to agent-only direct rewriting, the template-plus-search design significantly reduces the randomness of iterative optimization, making the process more interpretable and enabling a more systematic approach toward high-performance configurations. Although compiler optimizations and hand-written kernels can partially alleviate this issue, achieving near-hardware-limit performance still relies heavily on manual code refactoring and parameter tuning. Physics-Informed Neural Networks (PINNs) have emerged as a powerful solution that embeds physical laws in training by enforcing equation residuals.We open-source the entire FRoM-W1 framework and hope it will advance the development of humanoid intelligence.zhWe then provide a comprehensive analysis of methodologies, spanning training-free frameworks with their modular components to training-based techniques, including supervised fine-tuning and reinforcement learning.These results show the feasibility of LLM-driven STEP model generation from natural language, showing its potential to democratize CAD design for manufacturing.zhThe model outputs a portfolio of long/short positions that is rescaled to satisfy a fixed risk budget, and is optimized directly with a trading objective and risk-aware regularization.We introduce Reinforcement learning for Speculative Sampling (Re-SpS), the first reinforcement learning (RL)-based framework for draft tree hyperparameter optimization.Her Lizzo weight loss 2025 narrative is all about self-love and wellness, not societal pressure.Results show that MyGram outperforms baseline models, achieving a maximum improvement of 4.8% in Hits@1 on FBDB15K, 9.9% on FBYG15K, and 4.3% on DBP15K.zh Lizzo Shows Off Impressive Weight Loss While Posing on Red Carpet in Glam Gown at Met Gala 2025: Photos To address this, we present VTONGuard, a large-scale benchmark dataset containing over 775,000 real and synthetic try-on images. Finally, we implement a multi-point training strategy which promotes prediction consistency across different point placements, improving robustness to annotation variability. To overcome these challenges, we introduce DExTeR (DETR with Experts), a transformer-based Point-to-Box regressor tailored for medical imaging. A Point-to-Box teacher model, trained on a small box-labeled subset, converts these point annotations into pseudo-box labels to train a student detector. Weakly Semi-Supervised Object Detection (WSSOD) with point annotations proposes annotating each instance with a single point, minimizing annotation time while preserving localization signals. The proposed model is instantiated as an agentic perceive-predict xApp and evaluated on a bespoke O-RAN KPI time-series dataset comprising 59,441 sliding windows across 13 KPIs, using Reference Signal Received Power (RSRP) forecasting as a representative use case. We propose a quantum-inspired many-body state-space tensor network that replaces self-attention with stable structured state-space dynamics kernels, enabling linear-time sequence modeling. While Transformer-based models are effective for sequence modeling, their quadratic complexity limits scalability in Near-RT RAN Intelligent Controller (RIC) analytics. We propose client-side experience replay, where each client maintains a small buffer of past samples mixed with current data during local training. Together, these results establish a principled framework for deploying quantum machine-learning tools in precision hadronic physics. We then demonstrate the utility of this approach through an application to Compton form factor extraction from deeply virtual Compton scattering, where the quantum qualifier identifies kinematic regimes favorable to quantum models. Using controlled classification and regression studies, we show how relative model performance follows systematic trends in complexity, noise, and dimensionality, and how these trends can be distilled into a predictive criterion. It enables synchronized data collection, and realtime streaming inference with user PyTorch models, on commodity computing devices. In this work, we study the interpretability of autoencoder-based models for audio anomaly detection, by comparing a standard autoencoder (AE) with a mask autoencoder (MAE) in terms of detection performance and interpretability. By employing a PDE residual-based technique to adaptively refine the distribution of hidden neurons during the training process, the PIRBFNN facilitates accurate and efficient handling of multidimensional option pricing models featuring non-smooth payoff conditions. A wide range of techniques (from classical statistical models to neural network-based approaches such as Long Short-Term Memory (LSTM)) have been employed to address time series forecasting challenges. Furthermore, for learning our network, we also propose a large-scale dataset, which comprises 312 diverse glass scenarios with a total of 19,268 frames. These results highlight the effectiveness of structured, multimodal reasoning for comprehensive and context-consistent understanding of long-form videos.zh Notably, it achieves outstanding performance in the challenging reasoning category, reaching 80.1%. We present HAVEN, a unified framework for long-video understanding that enables coherent and comprehensive reasoning by integrating audiovisual entity cohesion and hierarchical video indexing with agentic search. Ablation studies show that constraining the model’s receptive field to the facial surface acts as a strong structural prior, and that surface-aligned, 3D-aware node features are critical for robustly encoding facial surface color. ReSearch integrates lexical search, semantic embeddings, abbreviation expansion, and large language model reranking within a unified architecture that explicitly separates recall and precision objectives. Our results show that reranking consistently improves retrieval and end-to-end accuracy, and that moderate reranking often yields larger gains than increasing search-time reasoning, achieving comparable accuracy at substantially lower cost. Using the BrowseComp-Plus benchmark, we analyze tradeoffs between model scale, reasoning effort, reranking depth, and total token cost via a novel effective token cost (ETC) metric. Through progressive multi-agent coordination, XR iteratively refines retrieval to meet both semantic and visual query constraints, achieving up to a 38% gain over strong training-free and training-based baselines on FashionIQ, CIRR, and CIRCO, while ablations show each agent is essential. While embedding-based CIR methods have achieved progress, they remain narrow in perspective, capturing limited cross-modal cues and lacking semantic reasoning. “I’ve been methodical, losing weight very slowly,” she told The New York Times in March 2024. "Today when I stepped on my scale, I reached my weight release goal. I haven’t seen this number since 2014! Let this be a reminder you can do anything you put your mind to. Time for new goals!" "Sometimes when you want to give up, it's really just because you've been pushing yourself too hard. Don't give up, just take it easier on yourself." "I'm trying to remind myself that my body needs that nourishment and if my body deserves comfort then my brain deserves comfort too." Taken together, these findings firmly establish system attention as a key factor in VLM hallucination and highlight its potential as a lever for mitigation.zh We show that this framework offers a useful empirical perspective on the yes-bias, a common form of hallucination in which VLMs indiscriminately respond ‘yes’. In this study, we evaluate a more holistic, system-mediated account, which attributes these imbalances to functionally redundant system weights that reduce attention to image and textual inputs. The framework successfully defends against attribute inference attacks, maintaining adversarial success probability below 0.05 across all tested configurations. Experiments on three long-context reasoning benchmarks show that our approach substantially outperforms RLVR baselines and matches frontier LLMs while using far fewer parameters. 10 Photos Flaunting Lizzo's Fitness Journey: Rapper Shares Secrets to Achieving Better Health This suggests that reduced connectivity simplifies the search space for formal solvers and that the optimal pruning ratio varies significantly between datasets. We also present efficient algorithms for the soft-margin SVM problem and for nearest neighbor-based classification in the Hilbert metric. This is a significant improvement on previous works, which either provide no theoretical guarantees on running time, or suffer from exponential runtime. The Hilbert metric is a generalization of the Cayley-Klein hyperbolic distance to arbitrary convex bodies and has a diverse range of applications in machine learning and convex geometry. The feats your body pulls off every day to function is iconic honestly 💁🏾♀️ love u.” “I love every stage my body fluctuates to. “I’ve been methodical, losing weight very slowly,” she added. “The bottom line is, the way you feel about your body changes every single day. In March, the “Truth Hurts” singer – real name Melissa Jefferson – admitted she was tired of “being the butt of the joke every single time because of how I look.” The “About Damn Time” singer shared the clip from Jay’s podcast to her Instagram, writing in the caption, “This is your body. Lizzo continued, “I saw how the media treated people who gained and lost weight and how that affected my brain chemistry.” The Grammy winner hoped that her choosing to call what she underwent as a “release” inspires her fans in a positive way with their own body goals. Lizzo credited boyfriend Myke Wright with helping her find the new way to describe the changes to her body. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. We present a controllable text-to-speech (TTS) system capable of synthesizing Lombard speech for any speaker without requiring explicit Lombard data during training. This underscores that external knowledge alone cannot bridge the reasoning gap without domain-adaptive pre-training. Mapping “Guideline Adherence” versus “Decision Quality” reveals a prevalent “High Efficacy, Low Safety” risk in general models. Our analysis shows that dLLMs are effective in non-causal roles (e.g., memory summarization and tool selection) but require the incorporation of causal, precise, and logically grounded reasoning mechanisms into the denoising process to be viable for agentic tasks.zh In the offline preprocessing stage, we embed information from other related text chunks into each chunk, while in the online reprocessing stage, we recompute the KV cache for tokens that the model focuses on. We propose FusionRAG, a novel inference framework that optimizes both the preprocessing and reprocessing stages of RAG. We propose Gdwm (Gated Differentiable Working Memory), a framework that introduces a write controller to gate the consolidation process. We validate our approach using real-world driving datasets and demonstrate that our approach outperforms existing baselines by effectively integrating road characteristics, real-time traffic conditions, and historical speed patterns in a task-aware manner. This architecture enables efficient and accurate ETA estimation while keeping the model lightweight and scalable. Simulation results demonstrate that the proposed algorithm can effectively balance training efficiency and model accuracy for FL in SAGIN. Lizzo's ultimate goal is to be happier and healthier This has led to a substantial rise in prescriptions for weight loss among patients without diabetes. There has been a marked increase in the use of these medications since they were first approved for weight loss in 2014, particularly following the approval of Wegovy in 2021 and Zepbound in 2022. Whether public and private insurance plans should cover GLP-1 agonists when prescribed primarily for weight loss has been a significant topic of interest and debate. But if it’s not, that’s some massive weight loss from The Rock.” Others quickly reminded fans that the transformation was for a role, with one comment reading, “He lost 60lbs for a movie folks, it’s called acting.. The Rock later explained his motivations behind the weight loss during the festival’s press conference. She lost 60 pounds, going from 308 pounds to 248 pounds, and has reduced her body fat percentage by 16% and lowered her body mass index (BMI) by 10.5 points. In a recent social media post (June 2025), Lizzo shared with her fans that she had reached her “goal weight” and that she had not been this weight since 2014. Lizzo shared in an interview with People Magazine that what she calls her “weight release journey,” which began in early 2023, was not intended to make her thin but to improve her mental health. She became a great source of inspiration to many through her public display of body positivity, through her music, in interviews, and on her social media platforms. However, current crop segmentation models mostly learn from limited data due to expensive pixel-level labelling cost, often performing well only under specific crop types or controlled environment. These models have demonstrated remarkable real world generalization due to significant model capacity and largescale datasets. In the era of foundation models, a number of generic large language and vision models have been developed. The environment is monitored via the digital twin through the shared messages which update the information of the spawned ego vehicle and detected objects based on the real-time localization and perception data. Firstly, the proposed upper bounds indicate that the degree of distribution shift directly affects the prediction ability of the learned models. Specifically, we derive several theoretical and empirical findings demonstrating that distribution shift plays a crucial role in model learning and benefits learning invariant prediction. We also implement a built-in composition mechanism that models how services interact under varied Internet of Things conditions. Extensive experiments on real-world datasets demonstrate that the proposed framework effectively mitigates cross-reality location privacy leakage for AVs while maintaining strong user immersion within 6G-enabled vehicular metaverse scenarios. To address this challenge, we design a cross-reality location privacy protection framework based on hybrid actions, including continuous location perturbation in reality and discrete privacy-aware AI agent migration in virtuality. Between the release of her new ’80s-inspired single, “Love in Real Life,” on February 28 and her revamped body, the singer seems ready for her comeback era. The longtime couple keeps their relationship mostly private. “Even at the end of my weight-loss journey, I’m not going to be considered thin by any means. “I am actually on an intentional weight-loss journey right now,” Lizzo explained. Lizzo dropped a new single and a lot of weight before she attended an Oscars after-party hosted by Vanity Fair on Sunday night. We present a methodology to characterize multiplicity in feature-attribution explanations and to disentangle sources due to model training/selection from stochasticity intrinsic to the explanation pipeline. RLERR leverages the high-quality reflections initialized by SCFT to construct reward signals, guiding the model to internalize the self-correction process via reinforcement learning. In this paper, a graph neural network anomaly detection backbone network incorporating spatio-temporal correlation features and a multi-task self-supervised training strategy of “pre-training - graph prompting - fine-tuning” are designed for the characteristics of WSN graph structure data. Our method ensures efficient erythrocyte classification, maintaining or improving accuracy over shape space models while significantly reducing computational costs. Since classification depends solely on cell shape, a suitable approach models erythrocytes as closed planar curves in shape space. While the effectiveness of these models can be theoretically explained using differential equations related to the sampling process, previous work by Song and Ermon (2020) demonstrated that neural networks using multiplicative noise conditioning can still generate satisfactory samples. We then reformulate PINN training as a multi-task learning problem and introduce a conflict-resolved gradient update strategy to alleviate gradient interference, leading to the Gradient-Conflict-Resolved PINN (GC-PINN). DFPO can be viewed as a lightweight form of hierarchical reinforcement learning, aimed at narrowing the `knowing-doing’ gap in LLMs. SCULPT scores and prunes actions using a combination of symbolic checks (dimensional consistency, type compatibility, magnitude sanity, depth control, and diversity) and structural pattern guidance, thereby steering the search toward plausible reasoning paths. Overall, our results delineate when EFX is computationally costly versus structurally aligned with welfare maximization in the setting with few surplus items.zh Instead of treating cases as isolated reports, we model how hotspot formation in one area is influenced by epidemic dynamics in neighboring regions. Finally, the paper discusses current research gaps, emphasizing the need for standardized benchmarking of system-level metrics and improved robustness under realistic communication conditions to enhance the real-world applicability of these approaches.zh The 37-year-old singer told Trisha Paytas’ Just Trish podcast that she began to take GLP-1 medication at the outset of her weight loss journey in 2023, after shutting down rumours of her taking the medication. Set small goals, like exercising a few times a week or adding more vegetables to your meals, instead of just focusing on the final weight you want to reach. Staying motivated during your weight loss journey can be tough, but there are simple ways to help you stay focused. Things may or may not be a smooth sail during your weight loss journey. This design reduces inter-task interference, mitigates negative transfer, and allows a single model to handle multiple paralinguistic tasks such as emotion, gender, age, and language classification. Unlike existing methods that rely on single-task models or cross-modal alignment, ParaMETA learns disentangled, task-specific embeddings by projecting speech into dedicated subspaces for each type of style. In this work, we introduce ParaMETA, a unified and flexible framework for learning and controlling speaking styles directly from speech. If it is large, the models’ ability can increase, approximating invariant prediction models that make stable predictions under arbitrary known or unseen domains; and vice versa. Our rigorous evaluation with proper train/validation/test splits demonstrates that these improvements generalize to held-out data, with GRADE-STE showing the best generalization characteristics among all methods tested.You typically need to have a certain BMI and participate in a weight loss coaching program.Our evaluation across multiple frontier LLMs on MedCounterFact reveals that in the presence of counterfactual evidence, existing models overwhelmingly accept such “evidence” at face value even when it is dangerous or implausible, and provide confident and uncaveated answers.This results in a more robust video representation, leading to new state-of-the-art performance on challenging benchmarks including MSRVTT, DiDeMo, and ActivityNet.In this study, we introduce a novel blood flow-aware network, named ASBA (A-line ROI State space model and B-line phase Attention), to reconstruct ODT images from highly sparsely sampled raw A-scans.This lack of transparency fosters blind trust, even as models produce unstable or repetitive outputs.We survey the spectrum of machine learning approaches, from traditional algorithms to advanced deep learning architectures, including CNNs, GNNs, and Transformers, alongside emerging generative AI and probabilistic models such as Gaussian Processes for uncertainty quantification. 4) Personal data enhances performance but due to limited data quality population-based models are preferred. We further evaluate our framework through comparison studies based on tabular and image data sets, showing the superiority of our framework which preserves consistent trade-offs among multiple downstream models compared to recent competing models. Several Machine learning based classification models using an AutoML library, “FLAML”, as well as 6 manually programmed models were trained on this dataset , which were then trained on 50 randomized samples of data, cross validated and evaluated. This paper proposes a supervised learning framework via a neural network model for approximating stationary performance measures of (s,S) inventory systems with general distributions for the interarrival time between demands and lead times under lost sales. She began documenting her gym sessions, which included heavy weightlifting and high-intensity cardio. Over the last two years, her narrative shifted from strict body acceptance to a focus on health optimization. However, her relationship with her weight was not without its private struggles. “I don’t want to use any negative terms,” Lizzo said, explaining her decision to shift the language around her health journey. “People aren’t going to understand this right now,” she admitted, “but it’s the most body-positive way to experience what I’m going through.” The 36-year-old artist recognizes that not everyone will immediately connect with the language she's using to describe her journey. “The weight that is no longer on me is not just fat or physical,” she said. As she continues to embrace her body at every stage, Lizzo remains a shining example of what it means to prioritize health, happiness, and self-acceptance. In this framework, a new privacy metric, termed cross-reality location entropy, is proposed to effectively quantify the privacy levels of AVs.At the output level, the global topic-word distribution, defined over the combined vocabulary, structurally synchronizes topic meanings across languages.However, existing LLM-based optimizers generalize poorly to the high-dimensional, structured CASH space.As with any big-time celebrity who has been documented on social media and in the press as their face and body have narrowed, Lizzo has seen fans, trolls, and neutral observers offer up opinions about what she looks like relative to her breakout years.Today, when I stepped on my scale, I reached my weight release goal.This paper presents a novel comparative framework for evaluating multilingual semantic robustness by systematically measuring how models handle polysemous words across languages.We also performed a trustworthy self-assessment procedure following the ALTAI checklist, in which it scored 4.40+/-0.27 (out of 5) across seven dimensions of the AI Trustworthiness Assessment List.zhExperiments demonstrate that ReSearch consistently improves recall and ranking performance over baseline methods, particularly for task-based queries expressing abstract scientific goals. Because constructing a proper training dataset for image smoothing is challenging, we propose DIP- ll_0 , a deep image prior framework that incorporates the ll_0 gradient regularizer. Recent state-of-the-art methods leverage deep learning, but they require a carefully curated training dataset. Using a dataset of 500 images labeled by 14 clinicians on the Leighton-Rex scale (Poor, Fair, Good, and Excellent), a ResNet-18 model was trained for classification, leveraging stratified K-fold cross-validation to ensure robust performance. To address these limitations, we propose an attention-based Multi-Objective Reinforcement Learning (MORL) architecture that explicitly handles the trade-off between data collection and energy consumption in urban environments, even without prior knowledge of wireless channel conditions. Overall, TPBS models exhibit greater robustness to overfitting and consistently benefit from regularization, while neural networks are more sensitive to overfitting and less effective in leveraging regularization. Leveraging pretrained TPBS models, we also introduce two estimators for inference from incomplete samples. To address this limitation, we propose a novel regularization strategy based on local Dirichlet energies defined on small hypercubes centered at the training points. We seek to unify these two directions with a first-of-its-kind model that learns representations which are simultaneously useful for recognition and generation. We systematically evaluate a wide range of state-of-the-art general-purpose and biomedical LLMs, revealing substantial heterogeneity in gene-level reasoning capabilities and persistent challenges in generating faithful, complete, and literature-grounded functional interpretations. The benchmark integrates curated knowledge for over 190K human genes and comprises more than 540K questions covering diverse gene-to-function reasoning scenarios relevant to cell type annotation, functional interpretation, and mechanism-oriented analysis. In our experiments, we gather four types of treasury futures data covering the period from 2015 to 2025, and define data synthesis tasks with durations ranging from one week to four months. This work emphasizes the characteristics of treasury futures data, including its low volume, market dependencies, and the grouped correlations among multivariables. Specifically, we develop a modality diffusion learning module to capture deep structural contextual information within modalities and enable fine-grained multi-modal fusion. The evaluation combines functional correctness, similarity-based metrics, and LLM-judge assessments focused on usefulness and contextual relevance. It includes 1,800 evaluation instances across six programming languages and six task categories derived from real developer telemetry, such as API usage and code purpose understanding. By reducing modality redundancy and enhancing inter-modal interactions, our method preserves speech semantic integrity to achieve robust recognition performance. In this work, we propose an end-to-end noise-robust AVSR framework coupled with speech enhancement, eliminating the need for explicit noise mask generation. To mitigate this, recent AVSR methods often adopt mask-based strategies to filter audio noise during feature interaction and fusion, yet such methods risk discarding semantically relevant information alongside noise. This review provides a comprehensive and structured overview of the current landscape, synthesizing recent advancements and methodologies for materials scientists seeking to effectively leverage these data-driven techniques. Driven by the accelerating pace of algorithmic advancements and increasing data availability, AI is becoming an essential competency for materials researchers. According to the screenshots, Lizzo lost 10.5 points in her BMI and 16% of her body fat. 'Today when I stepped on my scale, I reached my weight release goal. We understand that there is not a one-size-fits-all solution to creating a calorie deficit to lose weight, so we concentrate on finding the right plan just for you and your circumstances. She has lost a great deal of weight by focusing on eating whole foods, staying active, and being deliberate in creating a calorie deficit through healthy lifestyle habits, and treating her health as a long-term journey that is ongoing. Despite criticism from nay-sayers who said described her weight loss journey as shunning body-positivity - she says she feels great. Years of speculating about Lizzo’s body have culminated in the star admitting she “tried everything” to achieve the dramatic weight loss that has dumfounded her fans. The ‘Truth Hurts’ singer has recently reached a significant milestone in her weight loss journey, achieving her "weight release goal" for the first time since 2014. "I released so much to get to this point and I think people can see that and I don't want to describe anything as loss. People aren't going to understand this right now, but it's the most body-positive way to experience what I'm going through. I don't want to use any negative terms." "The weight that is no longer on me is not just fat or physical," she told Shetty. In fact, she shared in a January TikTok that she had lost 16 percent of her body fat and felt good as hell about it. And Lizzo acknowledged that she had been on “an intentional weight release journey” for about a year and a half, initially because she'd been suffering from weight-related back pain. "I've gained a lifestyle that I actually really love and I'm like, 'I can maintain this.' I've gained new perspective on nutrition and the science behind cardio and weight-lifting." To solve the problem, we first gamify it; that is, we model it as a game where charging blocks are placed within temporal and capacity constraints on a grid. In this problem, a central authority must decide, in real time, when to charge dynamically arriving electric vehicles (EVs), subject to capacity limits, with the objective of balancing load across a finite planning horizon. Instead, we propose replacing the softmax with an alternative family of policy parameterizations based on the generalized f-softargmax. Its application on the closed-loop intelligent prosthesis use case illustrates the process of suitable AI model development from the generated constraints and trade-offs. Lizzo explained that she works out three times a week, does cardio and sauna sessions daily, and recently brought animal protein back into her diet. Ozempic is a type 2 diabetes injectable medication that has become popular due to its weight-loss effects. Lizzo poked some fun at the allegations that she was using Ozempic to lose weight in her post. According to the Cleveland Clinic, lymphatic massage can help reduce swelling and move waste toward the body’s lymph nodes. Lizzo’s recent post featured a before-and-after photo of her weight-loss journey. MultiST employs graph-based gene encoders with adversarial alignment to learn robust spatial representations, while integrating color-normalized histological features to capture molecular-morphological dependencies and refine domain boundaries. Experimental results confirm that this integrated approach significantly enhances clutter mitigation, detection robustness, and overall system resilience in dust-laden mining environments.zh We also present a new 4D mmWave radar dataset, augmented by camera and LiDAR, illustrating how dust particles and reflective surfaces jointly impact the sensing functionality. Together, these results suggest that sampling-based optimization provides a powerful way to extract latent capabilities from language models without retraining.zh A recent theoretical breakthrough has shown that quantum kernel models can achieve similar performance with less training data than classical models. We compare different architectures, from fully connected neural networks to transformers, and we test different design/training modalities, identifying sequence-to-sequence, attention-based models as the most effective on our datasets. To address these limitations, we introduce a framework for Positioning Objects Consistently and Interactively (POCI-Diff), a novel formulation for jointly enforcing 3D geometric constraints and instance-level semantic binding within a unified diffusion process. Extensive experiments on three public datasets (e.g., CIRR, CIRCO, and FashionIQ) demonstrate that CVSI significantly outperforms existing state-of-the-art methods. (3) Complementary Information Retrieval, which integrates information extracted from both the query and database images to retrieve the target image, enabling the system to efficiently handle retrieval queries in a variety of situations. (2) Semantic Information Extraction, which involves using a pre-trained captioning model to generate multiple captions for the reference image, followed by leveraging an LLM to generate the modified captions and the objects most likely to be added. We perform in-depth evaluations on three standard video action recognition datasets, including UCF101, HMDB51, and Something-to-Something V2, by ignoring the labels from the supervised setting. Our method proposes (1) to employ a fixed parameterization based on the major axis of each cell to compute distances and (2) to align each cell with two templates using this parameterization before computing distances. While previous methods exploiting shape space distances had achieved high accuracy, we refined the model by considering the geometric characteristics of healthy and sickled erythrocytes. This structure limits the model’s ability to represent a more general relationship between the spatial variable and the noise, indicating that it cannot fully learn the correct score. These results highlight the effectiveness of architecture-optimization co-design for improving the robustness and accuracy of PINN-based solvers. In practice, however, their performance is often hindered by limited representational capacity and optimization difficulties caused by competing physical constraints and conflicting gradients. As a result, STAT produces length-adaptive 1D visual tokens that are naturally compatible with causal 1D autoregressive (AR) visual generative models. We further extend the output format to predict normalized bounding boxes for embedded images, introducing localization during pretraining via a resume strategy and refining it with RLVR using IoU-based rewards. While recent advances have significantly improved 2D, video, and 3D content generation, 4D synthesis remains difficult due to limited training data and the inherent ambiguity of recovering geometry and motion from a monocular viewpoint. "And I'm NOT only talking about my body if yall only KNEWWWW what I've done for my mental & emotional health in the last year..." "I wasn't gonna post this on IG but 2021 me would be soooo proud of 2024 me," she wrote in the caption of the post, which showed her modeling two different skin-tight outfits. Since fans and haters alike tended to focus on her changing body, she made it clear in an August Instagram post that the changes that were occurring on the inside were even more important to her.