The training sets varied in size, number of unique sentence types, and proportion of rule-following versus exception exemplars. To address this, we propose Plan-Verify-Fill (PVF), a training-free paradigm that grounds planning via quantitative validation. These findings highlight key challenges and opportunities for deploying LLMs in practical software engineering workflows.zh To enable realistic evaluation, we introduce VersiBCB, a benchmark that is multi-package, execution-verified, and deprecation-aware, capturing complex and evolving environments that prior datasets often overlook. TikTok lizzo weight loss surgery is most likely paid in the center of this range if she underwent weight loss surgery. Her most devoted followers believed it betrayed the strong declarations of self-acceptance that she had come to represent, fitness lizzo weight loss surgery. Lizzo, the Grammy-winning champion of body image and self-love, surprised her massive fan base by admitting Lizzo weight loss surgery, which sparked new, heated debates. In Section 2.1, we characterize the goal of LLM unlearning as ensuring that “the unlearned model should no longer memorize information from the unlearn set while preserving all other knowledge.”However, two key issues remain ambiguous, leading to divergent definitions of unlearning across the literature and, consequently, to inconsistent and imprecise evaluation practices. A known limitation of direct probability usage is the extreme variance in conditional probabilities across tokens, which can adversely affect metric stability.A straightforward mitigation is to utilize token ranks instead of raw probabilities.Sorting tokens by their probability in descending order and using the rank as the score results offer a more uniform score distribution (Ashuach et al., 2025).This rank-based paradigm is also employed by several metrics adapted for unlearning evaluation (Wu et al., 2023; Baluta et al., 2024; Qiu et al., 2024).For instance, Exposure (Carlini et al., 2019), a key metric in memorization analysis, can be viewed as a rank-based variant of the Truth Ratio, substituting likelihood comparison with rank comparison.Similarly, the Mean Reciprocal Rank (MRR), prevalent in entity retrieval tasks (Lacroix et al., 2018), calculates the reciprocal average of the ranks of target tokens. The speculation surrounding Lizzo’s weight loss has led to a myriad of questions, one of which is the potential cost of weight loss surgery for celebrities. After her transformation, her weight is now estimated to be around 240 pounds, representing a significant weight loss of approximately 70 pounds. As Lizzo continues to captivate audiences with her music and charismatic stage presence, her recent weight loss has sparked a flurry of speculation and curiosity. Meanwhile, to validate this exploration, a hardware-realistic fault injection experimental platform is established, and its simulation model is built and open-sourced, both fully replicating the real SPS. Therefore, to adapt the health management in the SMC era, this work proposes a principle of aligning underlying capabilities (AUC principle) and develops SpaceHMchat, an open-source Human-AI collaboration (HAIC) framework for all-in-loop health management (AIL HM). This work offers a robust solution for enhancing intelligent judicial systems, with publicly code available.zh Motivated by these findings, we investigate whether supervised training can more fully harness the tracking capability of video diffusion models. Extensive experiments demonstrate the effectiveness of our method, achieving high-quality 3D full-head modeling as well as real-time animation, thereby improving the realism of 3D talking avatars.zh Our framework achieves state-of-the-art performance, reaching 61.1% balanced accuracy across target-present and no-target settings, and demonstrates generalization to real-world egocentric data. "Can yall go one post without mentioning how 'skinny she's getting' or how she's 'using ozempic,”' one fan commented in her defense. "She's been documenting her fitness and healthy eating journey this whole time." She wasn't necessarily saying that TikTok's lion diet is actually good for you, but at that time, a diet higher in animal protein was helping her hit her goals and feel her best. At the beginning of October, she took to TikTok to reveal that she ate more than she normally would the day before, and she was "feeling really bad about it." In the caption, she noted, "I'm trying to remind myself that my body needed that nourishment." “I do call it a weight release because when it started, I got snatched here first. The singer said she reached her "weight release goal" in January. The “About Damn Time” singer shared the clip from Jay’s podcast to her Instagram, writing in the caption, “This is your body. The establishment of benchmarks like SWE-bench revealed this task as profoundly difficult for large language models, thereby significantly accelerating the evolution of autonomous coding agents. After comparing several indexing strategies for retrieval, we fine-tune a large language model to make optimal use of research context and to encourage the generation of evidence-based question. 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 The model achieved strong performance while maintaining high accuracy even after sentiment removal or keyword masking. Her weight-release experience is not a miracle pill or a secret remedy; it is one that relates to careful nourishment, regularity, and attitude. But if you can just do that on your own… It’s the same.” The singer called it a “weight release” journey, highlighting a slow and steady approach rather than crash dieting. But amid the buzz, rumours swirled that she’d used weight-loss drugs like Ozempic to shed pounds. Well, for the singer, metaphorically, it is her ‘weight release’ as she did not have to ‘lose’ in the transformation. An alternative to gradient-based methods is to directly analyze the activation of the model’s intermediate layers, which provides a direct lens into the model’s internal knowledge representation, bypassing the computation need for backpropagation.The method of selective pruning (Pochinkov and Schoots, 2024) calculates an importance score for each neuron based on four statistics of its activations when processing unlearn versus retain data.In addition to the activation strength, REVS (Ashuach et al., 2025) also considers the rank of a target token when projecting the neuron to the vocabulary space by unembedding matrix.A lower rank value indicates a stronger association between the target token and the neuron.They show that the combination outperforms methods based solely on activations, token associations, and gradients.Another approach, FALCON (Hu et al., 2025), uses mutual information of activations of the unlearn and retain set, to identify layers where the hidden representations of forget and retain knowledge are least entangled, targeting these specific layers for modification. Since training a model of comparable size to the original is computationally intensive, two key refinements have been proposed.First, fine-tuning can be performed using parameter-efficient fine-tuning (PEFT) techniques, where unlearning is achieved by applying negation operations to relevant parameter-efficient modules (PEMs) (Zhang et al., 2023b).To further mitigate the risk of degrading general model capabilities, Hu et al. (2024) combine an “expert” PEM with an “anti-expert” PEM and derive a general capability vector for preservation.Second, as an alternative to fine-tuning, approximate negative models can be derived via subspace decomposition and projection techniques, such as the Gram–Schmidt orthogonalization used in UNLEARN (Lizzo and Heck, 2025) and the singular value decomposition (SVD) applied in Ethos (Gao et al., 2024). A small portion of post-training unlearning methods do not update the parameters through optimization.We review several notable approaches here as a complementary perspective, broadly categorizing them into (1) parameter arithmetic operations and (2) SAE-based methods. The results highlight the potential of quantum kernel machine learning methods for accelerating materials discovery and suggest complex x-ray diffraction data is a candidate for robust quantum kernel model advantage. Lizzo Responds to Ozempic Allegations After Debuting Weight Loss Transformation Several users' comments about Lizzo's weight loss were not good as hell. She dressed up as a package of the fictional weight loss drug Lizzo, which was referenced on the series' special The End of Obesity. The Grammy winner, who has also shifted away from her previous vegan diet amid her journey, later poked fun at the weight loss drug use speculation by spoofed her own South Park parody this past Halloween. "When you finally get ozempic allegations," she wrote on Instagram, days after debuting her new look, "after 5 months of weight training and calorie deficit." "I am actually on an intentional weight loss journey right now," she added on TikTok. Qualitative examples further show the uncertainty maps based on quantile regression capture the magnitude and spatial distribution of reconstruction errors across acceleration factors, with regions of elevated uncertainty aligning with pathologies and artifacts.Moreover, we propose a novel framework for the OSLD task, which integrates multiple stages to continuously discover and learn new classes.We show that incorporating synthetic training data significantly improves pose estimation accuracy on real scans.We introduce RubRIX (Rubric-based Risk Index), a theory-driven, clinician-validated framework for evaluating risks in LLM caregiving responses.Instead, she clarified that for the past five months, she's been focusing on "weight training and calorie deficit."To benchmark performance, we solve a broad set of problems, including nonlinear oscillators across various damping regimes, the equilibrium-centered Lotka-Volterra system, the KPP-Fisher and the Wave equation.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.In this work, we introduce ParaMETA, a unified and flexible framework for learning and controlling speaking styles directly from speech. This makes it a promising approach for achieving low-latency and robust data delivery in highly dynamic LEO networks. Our results show that the proposed surrogate model achieves accurate and efficient predictions across a range of transient brain deformation scenarios, scaling to meshes with up to 150,000 nodes. Building on Universal Physics Transformers, our approach operates directly on large-scale mesh data and is trained on an extensive dataset generated from nonlinear finite element simulations, covering a broad spectrum of temporal instrument-tissue interaction scenarios. These results highlight the effectiveness of our framework for advancing open-source LLMs in this domain. Specifically, the evaluation framework designed for D-MT fails to yield consistent evaluation results when applied to ND-MT. First, we identify key tokens in prepared long context based on loss gaps between long and short forward contexts and find most revant preceding paragraphs, then summarize them using an LLM. Using this data, TRACE constructs a textual blueprint of inexpensive audio signals and prompts an LLM to render dimension-wise judgments, fusing them into an overall rating via a deterministic policy. In this work, we propose TRACE (Textual Reasoning over Audio Cues for Evaluation), a novel framework that enables LLM judges to reason over audio cues to achieve cost-efficient and human-aligned S2S evaluation. This leaves current automatic Speech-to-Speech (S2S) evaluation methods reliant on opaque and expensive Audio Language Models (ALMs). We evaluate our method on instructional and activity datasets, using reference summaries for instructional videos. The final evaluation results for the proposed model include an accuracy of 98.5%, sensitivity of 97.8%, specificity of 96.3%, F1-score of 98.2%, and overall precision of 97.9%. This thesis presents an innovative framework for detecting malignant masses in mammographic images by integrating the Pyramid Adaptive Atrous Convolution (PAAC) and Transformer architectures. To support systematic evaluation, we construct the largest cross-domain 3D WiFi pose estimation dataset to date, comprising 21 subjects, 5 scenes, 18 actions, and 7 device layouts. This design forces the network to disentangle human motion from deployment layouts, enabling robust and, for the first time, layout-invariant WiFi pose estimation. She admits that her feelings about her body fluctuate daily. While she was a proponent for 'body positivity', she now says that 'body neutrality' is more reflective of her current stance. She has gone on record to say that she moves her body every day, believing that 'there is never a day when I regret taking a walk or doing some Pilates.' Throughout her journey, Lizzo has been clear that she did not use Ozempic or other weight-loss medications. To mitigate this issue, we propose (1) Clinical Diagnostic Reasoning Data (CDRD) structure to capture abstract clinical reasoning logic, and a pipeline for its construction, and (2) the Dr. Assistant, a clinical diagnostic model equipped with clinical reasoning and inquiry skills. Robustness analyses further demonstrate that optical tokens preserve essential structural information under visual perturbations.zh Moreover, GRADFILTERING-selected subsets converge faster than competitive filters under the same compute budget, reflecting the benefit of uncertainty-aware scoring.zh Unlike previous SDG studies, we use an asynchronous multi-agent framework which better simulates realistic social contexts. RTCE surfaces several new and previously unmeasured insights that are not captured by existing I/O-prediction, execution-reasoning, or round-trip natural-language benchmarks. Team dialogue is analyzed using structured prompts derived from the Non-Technical Skills for Surgeons (NOTSS) framework, enabling automated classification of behaviors and generation of directed interaction graphs that quantify communication structure and hierarchy. In this paper, we propose a unified framework called TrustEnergy for accurate and reliable user-level energy usage prediction. Although a plethora of deep learning approaches have been proposed to perform this task, most of them either overlook the essential spatial correlations across households or fail to scale to individualized prediction, making them less effective for accurate fine-grained user-level prediction. Lizzo has been updating fans on her weight-loss process for a number of years at this point. If I said the number, I don’t think people could do the math.” From there, Lizzo complained about people's fixation with finding an exact weight. Lizzo explained why she won't tell her fans exactly how much weight she's lost during a new interview with Jason Lee of Hollywood Unlocked. How Did Lizzo Lose Weight? The Singer Credits A Shift For Mental Health 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. However, these studies often position AI as a replacement for human collaboration and overlook the social and learning-oriented aspects that emerge in collaborative programming. In this paper, we introduce DriveSafe, a hierarchical, four-level risk taxonomy designed to systematically characterize safety-critical failure modes of LLM-based driving assistants. Despite growing interest in LLM safety, existing taxonomies and evaluation frameworks remain largely general-purpose and fail to capture the domain-specific risks inherent to real-world driving scenarios. However, these methods often lack informed heuristics to provide a guided search for temporal goals. However, most methods only utilize the sparsity prior assumption for anomalies and rarely expand on this hypothesis. We believe this framework provides a practical and efficient baseline for future research in semantic correspondence. We propose a lightweight upsample decoder that progressively recovers spatial detail by upsampling deep features to 1/4 resolution, and a multi-scale supervised loss that ensures the upsampled features retain discriminative features across different spatial scales. We present MIRACLE, a deep learning architecture for prediction of risk of postoperative complications in lung cancer surgery by integrating preoperative clinical and radiological data. Experiments demonstrate that our method can maintain superior generation quality across multiple datasets.zh We introduce a unified training strategy that enables joint optimization of the ViT-based geometric backbone and the diffusion-based refinement module. Specifically, we propose an A-line ROI state space model to extract sparsely distributed flow features along the A-line, and a B-line phase attention to capture long-range flow signals along each B-line based on phase difference. Fans React to Lizzo’s Transformation By enabling text-aligned 3D model generation along with precise, real-time parametric edits, Proc3D facilitates highly accurate text-based image editing applications.zh To address this, we propose CytoCLIP, a suite of vision-language models derived from pre-trained Contrastive Language-Image Pre-Training (CLIP) frameworks to learn joint visual-text representations of brain cytoarchitecture. Extensive experiments across 11 image classification tasks show PCBM-ReD achieves state-of-the-art accuracy, narrows the performance gap with end-to-end models, and exhibits better interpretability.zh PCBM-ReD automatically extracts visual concepts from a pre-trained encoder, employs multimodal large language models (MLLMs) to label and filter concepts based on visual identifiability and task relevance, and selects an independent subset via reconstruction-guided optimization. Traditional forecasting methods require significant expert knowledge and struggle to generalize across diverse deployment scenarios. For example, a video labeled “train” might also contain motorcycle audio and visual, because “motorcycle” is not the chosen annotation; standard methods treat these co-occurrences as negatives to true motorcycle anchors elsewhere, creating false negatives and missing true cross-modal dependencies. Most contrastive and triplet-loss methods use sparse annotated labels per clip and treat any co-occurrence as semantic similarity. The Truth About Lizzo’s Weight Loss Journey Our theory, supported by preliminary experiments, explains why feedback-free self-improvement works and predicts when it should succeed or fail.zh Methods such as debate, bootstrap, and internal coherence maximization achieve this surprising feat, even matching golden finetuning performance. We introduce SUDO, a novel dataset for implicit comparative opinion mining from same-user reviews, allowing reliable inference of user preferences even without explicit comparative cues. It also holds potential for integration with diverse sensor inputs and global glacier monitoring activities.zh The vision component, RiskFlow, of IceWatch deals with Sentinel-2 multispectral satellite imagery using a CNN-based classifier and predicts GLOF events based on the spatial patterns of snow, ice, and meltwater. Experimental results show that ppRAG achieves efficient processing throughput, high retrieval accuracy, strong privacy guarantees, making it a practical solution for resource-constrained users seeking secure cloud-augmented LLMs.zh CAPRISE preserves only the relative distance ordering between the encrypted query and each encrypted database embedding, without exposing inter-database distances, thereby enhancing both privacy and efficiency. We propose Conditional Approximate Distance-Comparison-Preserving Symmetric Encryption (CAPRISE) that encrypts embeddings while still allowing the cloud to compute similarity between an encrypted query and the encrypted database embeddings. Traditional supervised models typically require large annotated datasets, which is resource-intensive and not generalizable to novel artifact types. In this paper, we introduce Proc3D, a system designed to generate editable 3D models while enabling real-time modifications. Prior work has progressed from using basic visual features like color, motion, and structural changes to using pre-trained vision-language models that can better understand what’s happening in the video (semantics) and capture temporal flow, resulting in more context-aware video summarization. She makes choices about her body and health based on what will make her feel good. Lizzo is cutting straight through the chatter about her recent weight loss. Lizzo has become a symbol of body positivity through her weight-loss journey, openly sharing her triumphs and challenges along the way. Lizzo began her weight loss journey in 2023 and has since shared noticeable progress along the way. I’m working out to have my ideal body type,” she told fans in a 2020 TikTok exercise video. Lizzo completely transformed her body in 2025 after focusing on her fitness and changing her diet. "I lead a very healthy lifestyle—mentally, spiritually, I try to keep everything I put in my body super clean," she told Vanity Fair. After all, she's been criticized for gaining and losing weight over the years. I feel very lucky because I don’t feel that weight gain is bad anymore. While speculation about Lizzo’s weight loss surgery continues, it’s essential to differentiate between verified information and unfounded rumors. Lizzo has been vocal about her journey towards a healthier lifestyle, but she has not directly confirmed undergoing weight loss surgery. As a prominent figure in the entertainment industry, Lizzo’s transformation has been under scrutiny, with fans and critics speculating about the reasons behind her weight loss. We first construct a logistics topology graph by using the discrete GPS data using spatial clustering methods. 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. This mechanism dynamically modulates input embeddings based on mask characteristics, balancing background fidelity with generative flexibility. This bridge formulation effectively leverages the input video as a strong structural prior, guiding the model to perform precise removal while ensuring that the filled regions are logically consistent with the surrounding environment. In this paper, we reformulate video object removal as a video-to-video translation task via a stochastic bridge model. Consequently, such methods often lack sufficient guidance, leading to incomplete object erasure or the synthesis of implausible content that conflicts with the scene’s physical logic. As AD and FTD propagate along white-matter regions in a global, graph-dependent manner, graph-based neural networks are well suited to capture these patterns. "I have been on an intentional weight release journey ... I was, like, very clear that this time it's intentional," she explained. Indeed, the “Juice” singer recently detailed her weight loss journey on social media, noting that she picked up a daily habit. Speaking with the New York Times back in March 2024, she described her effort as "methodical." "I'm taking the time every day to put some love into my body," she told the outlet. ‘I’m taking the time every day to put some love into my body,’ she said. Lizzo has stunned her fans by revealing her new look following weight loss in a candid post. “The weight that is no longer on me is not just fat or physical. She refused to refer to her journey as a weight “loss” because she believes it does not suit to describe the positive benefits that the transformation has brought with it. The rapper also shed light on why she chose to call it a “weight release”. Today, when I stepped on my scale, I reached my weight release goal. Here’s everything to know about Lizzo’s “weight release” journey and transformation. With the improved framework, we show that our LNNs can learn Lagrangians representing geodesic motion in both non-relativistic and general relativistic settings. We systematically evaluate these techniques alongside previously proposed methods for improving stability. In both cases finding the optimal split can be done in O(d) time, where d is the number of features. A standard training strategy involves augmenting the current tree by changing a leaf node into a split. Experiments across multiple EV arrival patterns confirm that gamified learning enhances load balancing. “The Wizard Of Oz-empic”: Lizzo Reveals Her Weight Loss Methods After Rejecting Ozempic Rumors While model weights may vary significantly across training datasets, model responses to specific inputs are much lower dimensional and more stable. Comparative experiments with neural networks demonstrate that TPBS models outperform neural networks in the overfitting regime for most datasets, and maintain competitive performance otherwise. We hypothesize that neuroimaging data could open a window into elements of human cognition that are not accessible through observable actions, and argue that this additional knowledge could be used, alongside classical training data, to overcome some of the current limitations of foundation models. We estimate this measure by comparing the prediction error of a fixed LLM in a given domain to that of flexible machine learning models trained on increasing samples of domain-specific data. Our method consistently achieves reliable lineage verification across a broad spectrum of model types, including classifiers, diffusion models, and large language models.zh 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. We provide a theoretical analysis that establishes DFPO’s favorable optimization properties, supporting a stable and reliable training process. Drawing inspiration from cognitive science, we propose PaperCompass, a framework that mitigates these issues by separating high-level planning from fine-grained execution. Typical filtering methods use similarity metrics to locate relevant article sections from one article, leading to information selection errors at the article and intra-article levels. Existing discovery systems are primarily retrieval-centric and struggle to bridge the gap between high-level scientific intent and heterogeneous metadata at scale. 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. However, VFM performance remains inconsistent across other spatial tasks, raising the question of whether these models truly have spatial awareness or overfit to specific 3D objectives. We employed two unsupervised image translators (CycleGAN and an AdaIN-based model) using only annotated data from the source domain and non-annotated data from the target domain. Although promising, there is still room for improvements to close the performance gap toward the upper-bound (when training with the target data). So, everybody’s body is different. Lizzo has finally opened up about how she lost 16 percent of her body fat — and it wasn’t due to Ozempic or Mounjari, as the Good As Hell singer has claimed. "The only way to really alleviate that is surgery or releasing a little bit of weight. So I was like, I want to be Tina Turner. I want to be doing stadium shows when I’m 70. If my back is on this track right now, there’s no way I’m going to be able to do that without serious surgery. So that was my decision," she said. Top NAACP Image Awards Winners of All Time We preserve and extend properties of classical methods through feature space constraints and locality weighting. This allows our weights to be computed for high resolution meshes in under a minute, in contrast to potentially hours for both classical and neural methods. We propose a technique that fuses the semantic prior of data with the precise control and speed of traditional frameworks. While our empirical analysis focuses on climate communication, the proposed framework is designed to support comparative narrative analysis across heterogeneous communication environments.zh We evaluate the quality of the induced themes against traditional topic modeling baselines using both human judgments and an LLM-based evaluator, and further validate their semantic coherence through downstream stance prediction and theme-guided retrieval tasks. We also propose a mechanism to reduce tokenization premiums in pre-trained models, by post-hoc additions to the token vocabulary that coalesce multi-token characters into single tokens. Lizzo has always been about self-love, and it doesn’t seem to be slowing down with her weight goal reached. “Even me releasing the weight has affected people and I take that seriously,” Lizzo expressed. Lizzo is also very conscious about how she talks about her body, especially to discourage negativity in her younger fans. But she slammed the constant obsession over her weight as it changed over her decades-long career.“When you’re a teenager, you have a very different body than when you’re in your 20s,” she told Glamour last August. Here are some stars who have admitted to taking the medication for weight loss and others who have vehemently denied taking it. A number of celebrities have recently shared their experiences with taking Ozempic injections—or other GLP-1 medication such as Mounjaro or Wegovy—normally prescribed to maintain healthy blood sugar levels but now infamous for its weight loss effects. So it is no wonder in a celebrity culture obsessed with weight and size, that a drug that purports to induce weight loss would become the talk of the town. This Turing-completeness implies that determining any non-trivial property concerning the relationship between the inputs and the computed outputs is undecidable for GA and, by extension, for the general set of SLS methods (although not necessarily for each particular method). The key idea is to adopt a unified FP8 precision flow for both training and rollout, thereby minimizing numerical discrepancies and eliminating the need for inefficient inter-step calibration. Our analysis shows that these failures stem from the off-policy nature of the approach, which introduces substantial numerical mismatch between training and inference. In this work, we present the first comprehensive study of FP8 RL training and demonstrate that the widely used BF16-training + FP8-rollout strategy suffers from severe training instability and catastrophic accuracy collapse under long-horizon rollouts and challenging tasks. Fans Can Already Shop Bad Bunny’s NFL “Concho” Merch Ahead of His Super Bowl Halftime Show We introduce extbfReSearch, a multi-stage, reasoning-enhanced search framework that formulates Earth Science data discovery as an iterative process of intent interpretation, high-recall retrieval, and context-aware ranking. 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. To address these limitations, we introduce XR, a training-free multi-agent framework that reframes retrieval as a progressively coordinated reasoning process. While embedding-based CIR methods have achieved progress, they remain narrow in perspective, capturing limited cross-modal cues and lacking semantic reasoning. In addition to unifying shower generation for multiple particle types, AllShowers surpasses the fidelity of previous single-particle-type models for hadronic showers. Although the concept of granularity holds significant value for business applications by providing deeper insights, the capability of topic modeling methods to produce granular topics has not been thoroughly explored.The model is fine-tuned through the “graph prompting-fine-tuning” mechanism to guide the pre-trained self-supervised learning model to complete the parameter fine-tuning, thereby reducing the training cost and enhancing the detection generalization performance.The 44-year-old brought Lizzo out during her DJ set at Diplo's Honky Tonk and the singer again displayed the results of her journey as she slipped into a very revealing sheer bodysuit which she completed with a netted maxi skirt.Then, we introduce semantic-aware hierarchical CL as auxiliary training objective to guide models in improving their discriminative capabilities and achieving sufficient semantic learning, considering both local level and global level CL.Our results on the evaluation on order of training (fine-tuning on synthetic aerial data vs. real ground data) shows that fine-tuning on real ground data but differ in how they transition from synthetic to real.Experiments on multiple benchmarks demonstrate that DistilTS achieves forecasting performance comparable to full-sized TSFMs, while reducing parameters by up to 1/150 and accelerating inference by up to 6000x.At the beginning of October, she took to TikTok to reveal that she ate more than she normally would the day before, and she was "feeling really bad about it." In the caption, she noted, "I'm trying to remind myself that my body needed that nourishment." "The weight that is no longer on me is not just fat or physical," she explained on the April 7 episode of Jay Shetty’s On Purpose podcast. News that the drug is not FDA-approved for chronic weight management. She admitted to becoming "immediately invested" in Ozempic in 2022, but explained that it was not "livable" for her to take the Type 2 diabetes drug as it hindered her ability to spend time with her son Gene. We propose BiCoLoR, the first algorithm to combine local training with bidirectional compression using arbitrary unbiased compressors. We compare a multilayer perceptron, a windowed multilayer perceptron, and a convolutional neural network (CNN) on three-phase air-water-oil flow data from 342 experiments. Combining machine learning (ML) algorithms with accurate single-phase flowmeters has therefore received extensive research attention in recent years. Experiments on the toric code demonstrate that QuantumSMoE outperforms state-of-the-art machine learning decoders as well as widely used classical baselines. Typically operating in a black-box manner, it employs an encoder-decoder framework for watermark embedding and extraction. In this context, this study presents the first exact Constraint Programming formulation for the SAEOS-ISP, considering flexible observation windows, multiple pointing directions and sequence-dependent transition times across multiple satellites. Our work demonstrates that context-aware scheduling is the key to unlocking complex multi-modal AI on cost-effective edge hardware, making intelligent perception more accessible and privacy-preserving.zh The selected pages alone are fed to a frozen LVLM for answer generation, eliminating the need for model fine-tuning. This approach effectively mitigates bias and safety deterioration without costly retraining or alignment, maintaining trustworthiness while retaining efficiency.zh PUMA utilizes a parameter-efficient adapter to bridge the semantic gap, combined with a group-based user selection strategy to significantly reduce training costs. However, these prompts become obsolete when the foundation model is upgraded, necessitating costly, full-scale retraining. These results highlight segmentation as a consequential design choice that should be optimized for downstream objectives rather than a single performance score.zh Both soft and hard voting ensemble approaches combining top-performing models achieved 98% accuracy, demonstrating superior robustness and generalization. We analyze these changes in multiple, large-scale datasets with 2.1M preprints, 28K peer review reports, and 246M online accesses to scientific documents. For the minority classes that have smaller samples, synthetic samples were generated and merged with the original dataset. Our approach synthesizes high-fidelity minority-class samples from the CIC-IDS2017 dataset through iterative denoising processes. Class imbalance in network intrusion detection using Tabular Denoising Diffusion Probability Models (TabDDPM) for data augmentation is ad- dressed in this paper. However, the same LLMs achieve near-perfect deal closure rates ( \geq 95%) under turn-based limits, revealing the failure is in temporal tracking rather than strategic reasoning. We introduce SDF-HOLO (Systemic Dual-stream Fusion Holo Model), a multimodal foundation model for holistic total-body PET/CT, pre-trained on more than 10,000 patients. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79,cm RMSE (about 2.6 pixels at this GSD) and outperforms a state-of-the-art LiDAR baseline (7.91,cm RMSE), demonstrating that densified splat-based geometry can enable accurate, low-cost aerial DBH measurement.zh Our findings aim to inspire a more cautious and rigorous adoption of visual explanation tools in medical AI, urging the community to rethink what it truly means to “trust” a model’s explanation.zh Unlike prior methods, SL-CBM produces faithful saliency maps inherently tied to the model’s internal reasoning, facilitating more effective debugging and intervention. This approach unifies natural gradient descent with orthogonal gradient methods within an information-geometric framework. We propose the Fisher-Orthogonal Projected Natural Gradient Descent (FOPNG) optimizer, which enforces Fisher-orthogonal constraints on parameter updates to preserve old task performance while learning new tasks. By shifting from simply reporting dengue cases to mining and validating hidden spreading dynamics, this work transforms open web-based case data into a predictive and explanatory resource. No matter what the truth may be, Lizzo’s message of self-love, body positivity, and self-acceptance resonates deeply with her audience, serving as a powerful reminder that beauty and worth extend far beyond physical appearance. While the singer has always been open about her struggles with body image and self-acceptance, she has neither confirmed nor denied undergoing any surgical procedures to achieve her current physique. Lizzo, the talented singer, rapper, and body positivity advocate, has been making waves in the music industry with her chart-topping hits and unapologetic attitude towards her body image. Her authenticity and dedication have not only inspired her fans but also sparked important conversations about body positivity and the true meaning of wellness. Lizzo’s fitness routine includes Pilates, walking, and a focus on strengthening her body while maintaining a healthy, balanced lifestyle. We operationalize this perspective through controlled perturbation auditing of training trajectories, probing how learning dynamics respond to structured disturbances without modifying learning algorithms. Training unfolds as a high-dimensional dynamical system in which small perturbations to optimization, data, parameters, or learning signals can induce abrupt and irreversible collapse, undermining reproducibility and scalability. The augmented training data enables an ANN classifier to achieve near-perfect recall on previously underrepresented attack classes. Evaluated on the TREC NeuCLIR 2024 collection, our Crucible system substantially outperforms Ginger, a recent nugget-based RAG system, in nugget recall, density, and citation grounding.zh Nugget-based approaches in particular are now embedded not only in evaluation frameworks but also in the architectures of RAG systems themselves. To achieve this goal, we propose CLASP (CLIP-guided Adaptable Self-suPervised learning), a novel framework designed for unsupervised pre-training in human-centric visual tasks. With the emergence of large-scale unlabeled human image datasets, there is an increasing need for a general unsupervised pre-training model capable of supporting diverse human-centric downstream tasks. By treating embeddings as first-class geospatial datasets, we decouple downstream analysis from model-specific engineering, providing a roadmap for more transparent and accessible Earth observation workflows.zh With the emergence of multi-modal large language models (MLLMs), recent studies have explored their applications in geo-localization, benefiting from improved accuracy and interpretability. Our evaluations show that explicitly optimising for object-based attention not only improves oSIM performance but also leads to an improved model performance on common metrics. However, existing approaches predominantly treat teacher models as simple binary annotators, failing to fully exploit the rich knowledge and capabilities for RM distillation. Despite their success, AR models are inherently constrained by a causal bottleneck that limits global structural foresight and iterative refinement. DermaBench is released as a metadata-only dataset to respect upstream licensing and is publicly available at Harvard Dataverse.zh Visual question answering (VQA) benchmarks are required to evaluate how models interpret dermatological images, reason over fine-grained morphology, and generate clinically meaningful descriptions. However, existing methods use a single policy to produce both inference responses and training optimization trajectories. Therefore, leveraging reward models (RMs) to automatically and reliably evaluate memory quality is critical. The model leverages contrastive learning, guided by soft labels derived from LLM-generated credibility scores, to enhance detection robustness. In this paper, we present a multi-agent, LLM-based framework for prescriptive decision support, which transforms large scale review corpora into actionable business advice. To validate our approach’s generality, we further set up a new amodal grounding setting that requires the model to predict both the visible and occluded parts of the objects. Small VLMs fall behind larger VLMs in grounding because of the difference in language understanding capability rather than visual information handling. Our experimental results show that applying QUB loss to the existing methods yields significant improvement of robustness. Through quantitative experiments we demonstrate the effectiveness of our approach, showing that it achieves SOTA performance on two task formulation of face-voice association. We developed a dataset by capturing images of vegetables from their fresh state until they were completely spoiled. The dataset varies weather conditions (five types) and traffic density levels (spanning Level-of-Service A-E) in a structured manner, resulting in 25 controlled scenarios. We present CARLA-Round, a systematically designed simulation dataset for roundabout trajectory prediction. Developing accurate prediction algorithms relies on reliable, multimodal, and realistic datasets; however, such datasets for roundabout scenarios are scarce, as real-world data collection is often limited by incomplete observations and entangled factors that are difficult to isolate. In this paper, we introduce Segment And Matte Anything (SAMA), a lightweight extension of SAM that delivers high-quality interactive image segmentation and matting with minimal extra parameters. My face was clenching up, my whole body was tense.” After taking a dosage meant for those roughly 220 pounds or more, the model was struggling to keep any food or water down and landed in the emergency room. In December 2024, the model said she had once taken Ozempic but stopped after she started competing on Dancing With the Stars the previous fall. Ultimately, said the Playboy model, she wasn't interested in hopping into any potential health risks. Finally, CARPE is designed to be effectively integrated with most open-source LVLMs that consist of a vision encoder and a language model, ensuring its adaptability across diverse architectures.zh This design enhances the model’s ability to adaptively weight visual and textual modalities and enables the model to capture various aspects of image representations, leading to consistent improvements in generalization across classification and vision-language benchmarks. Experiments show our method can consistently and significantly improve the vanilla grounding and amodal grounding capabilities of small models to be on par with or outperform the larger models, thereby improving the efficiency for visual grounding.zh However, we notice that the sizes of visual encoders are nearly the same for small and large VLMs and the major difference is the sizes of the language models. Previous state-of-the-art grounding visual language models usually have large model sizes, making them heavy for deployment and slow for inference. Looks more like eating tiny portions, or just looks in the mirror and sees herself as overweight? “Having Mason move in with him full-time has been a complete game changer,” the source added. Kelly has previously said she lost 85 pounds after gastric bypass surgery in 2020 and also lost weight after becoming a mother to her son, Sidney, in 2022. Finally, we investigate whether language bias is in fact caused by low-perplexity bias, a previously identified bias of LLM-as-a-judge, and we find that while perplexity is slightly correlated with language bias, language bias cannot be fully explained by perplexity only.zh We find that for same-language judging, there exist significant performance disparities across language families, with European languages consistently outperforming African languages, and this bias is more pronounced in culturally-related subjects. One of the identified biases is language bias, which indicates that the decision of LLM-as-a-judge can differ based on the language of the judged texts. “I wanted to change how I felt in my body,” she added. “And even sometimes a super hero suit to protect me through life. “After talking to a few therapists I discovered that my weight had been a protective shield, a joyful comfort zone,” she explained. Using a large-scale dataset, we correlate model performance with traditional, human-centric complexity metrics, such as lexical size, control-flow complexity, and abstract syntax tree structure. We introduce a diagnostic framework that reframes code understanding as a binary input-output consistency task, enabling the evaluation of classification and generative models. Through time and frequency domain analysis of the S4D kernel, we show that the long-range modeling capability of S4D varies significantly under different model architectures, affecting model performance. Lizzo’s path after her weight loss surgery was not an easy one. Ultimately, Lizzo’s weight loss operation was an embrace of the next phase in her own personal path toward growth and self-acceptance. She remained steadfast in her commitment despite the disapproval of several others regarding her choice to have weight loss surgery. It’s called weight loss surgery. No one knows exactly how much Lizzo has paid for weight loss surgery. She understood that her journey towards weight loss wasn’t solely about aesthetics but also about improving her overall health. At the heart of Lizzo’s decision to embark on her weight loss journey was a profound commitment to her own well-being. In this section, we’ll delve into the factors that inspired Lizzo to commit to her weight loss journey and understand the depth of her motivation. Lizzo’s journey towards her stunning 2024 weight loss transformation was fueled by a powerful motivation that extended beyond the desire for physical change. We adapt this framework to logic synthesis, adding column-sign modulation to enable Boolean negation – a capability absent in standard doubly stochastic routing. Our approach draws on recent insights from Manifold-Constrained Hyper-Connections (mHC), which demonstrated that projecting routing matrices onto the Birkhoff polytope preserves identity mappings and stabilizes large-scale training. This observation indicates that loss-induced numerical ill-conditioning, rather than nonconvexity or model expressivity, can constitute a dominant practical bottleneck. To address this, we introduce Group-Invariant Skill Discovery (GISD), a framework that explicitly embeds group structure into the skill discovery objective. 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%. These results establish that privacy risks of fine-tuned language models are substantially greater than previously understood, with implications for both privacy auditing and deployment decisions. This study explores the effectiveness of large language models (LLMs) in classifying Bengali newspaper articles. Based on the preliminary results and evaluation, BERTopic shows stronger performance and is selected for further experimentation using three clinically oriented embedding models. The proposed approach is found to be more robust to shorter utterances and is shown to be easily adaptable for streaming, real-time applications, with minimal performance degradation.zh That’s the very approach we explore in our deep dive on microdosing weight loss, where intentional, measured steps replace crash diets and guilt-laden deprivation with sustainable habits and self-compassion. As she put it, “Every single time I’ve received backlash I use it as a growing and learning lesson.” In one post, some fans cheered on Lizzo’s noticeable weight loss. “I’m taking some time every day to put love into my body,” Lizzo told the New York Times in March 2024. In January 2022, Lizzo danced around wearing a brown bodysuit and tights, crowing about gaining weight and looking good. Lynn Edwards comments on weight loss journey, referencing her Instagram workouts and trainer dedication. Text message screenshot about Lizzo’s weight loss and appearance. Woman in a white outfit posing confidently indoors, related to The Wizard Of Oz-empic weight loss topic. Addressing the Ozempic Rumors To address these challenges, we introduce a comprehensive dataset spanning 7 domains, containing 155 tools and 9,377 question-answer pairs, which simulates realistic integration scenarios. However, existing methods for tool selection often focus on limited tool sets and struggle to generalize to novel tools encountered in practical deployments. By selecting and integrating appropriate tools, LLMs extend their capabilities beyond pure language understanding to perform specialized functions. The blockchain integration adds 400 ms per round, and the ledger size remains under 12 KB due to metadata-only on-chain storage. Being skinny can be a competition sometimes,” one Redditor suggested.“I believe the producers and directors wanted both women to be as slim as possible,” said another.In her book, Simply More, the Tony winner discussed the dangers of body-shaming, writing, “In today's society, there's a degree of ease involved in commenting on others.These prediction results are then integrated into a dynamic edge weight mechanism to perform path planning.IR-13 Facet-Aware Multi-Head Mixture-of-Experts Model with Text-Enhanced Pre-training for Sequential Recommendation WSDMEverybody’s body’s different!Kourtney Kardashian's former longtime partner/co-parent, "Keeping Up with the Kardashians" alum, and self-styled "lord" Scott Disick has undergone a significant transformation since he first caught the public eye via his old flame's family and reality TV stardom.Experimental results of both face hallucination and FLD demonstrate that our method surpasses state-of-the-art techniques.zhCV-132 A Generalist Foundation Model for Total-body PET/CT Enables Diagnostic Reporting and System-wide Metabolic ProfilingTo reduce the high computational cost, various fast machine learning surrogate models have been proposed.Motivated by these observations, we propose Jet-RL, an FP8 RL training framework that enables robust and stable RL optimization. The ‘Truth Hurts’ songstress had the timeline in shambles after she flexed her MAJOR weight loss glow-up, and fans are taking notes. After people began leaving harsh comments accusing her of hypocrisy over her weight loss, Meghan wrote on social media that she found it “a little disheartening” that her body was being discussed more than her music.“No, I don’t look like I did 10 years ago. Last month, the actress stunned fans with her slimmer physique when she hosted Saturday Night Live for the sixth time.“I’m really impressed by Melissa’s weight loss progress,” one viewer wrote. For Mo’Nique, weight loss came down to “putting in the work and not giving up.” In 2018, she celebrated weighing under 200 pounds for the first time since she was 17. She credits her weight loss to a disciplined regimen of calorie deficit, high-protein meals, and strength training. Lizzo Opens Up About Anxiety Amid Weight Loss Journey Lizzo’s story proves that weight loss is more than just a physical change—it’s a journey of self-love, health, and acceptance. Lizzo’s weight loss journey has been a gradual process that started in early 2023 and continued through 2024 and into 2025. No, Lizzo has been open about not undergoing any kind of surgery for her weight loss. With explicit visual cues of causality, COW enables models to ground their reasoning in physical reality rather than linguistic priors. To address this, we propose the Causal Object World model (COW), a framework that externalizes the simulation process by generating videos of hypothetical dynamics. MultiST yields spatial domains with clearer and more coherent boundaries than existing methods, leading to more stable pseudotime trajectories and more biologically interpretable cell-cell interaction patterns. Extensive experiments show that our approach substantially outperforms existing methods in geometric quality, textual fidelity, and inference efficiency.zh Critically, this canonical sphere can be seamlessly unwrapped into a 2D map, creating a perfect synergy with powerful 2D generative models. Our results demonstrate that MIRACLE outperforms various traditional machine learning models and contemporary large language models (LLM) variants alone, for personalized and explainable postoperative risk management.zh Experimental evaluations on three benchmark datasets demonstrate that our method consistently outperforms LLM prompting, fine-tuned smaller language models, and state-of-the-art clickbait detection baselines.zh Experiments across three service domains and multiple model families show that our framework consistently outperform single model baselines on actionability, specificity, and non-redundancy, with medium sized models approaching the performance of large model frameworks.zh The present study explored the minimal amount and quality of training data necessary for rules to be generalized by a transformer-based language model to test the predictions of the Tolerance Principle. While post-training methods can improve ToM performance, we show that strong ToM capability can be recovered directly from the base model without any additional weight updates or verifications. Experiments on CalConflictBench shows that PEARL achieves 0.76 error reduction rate, and 55% improvement in average error rate compared to the strongest baseline.zhNotably, our models trained 1-2 orders of magnitude faster and were 10 times smaller than competing transformer-based approaches.We present a controlled comparison of six fine-tuning objectives – Supervised Fine-Tuning, Direct Preference Optimization, Conditional Fine-Tuning, Inoculation Prompting, Odds Ratio Preference Optimization, and KL-regularized fine-tuning – holding data, domain, architecture, and optimization fixed.Using just 26% of the fine-tuning budget of baseline models, we reduce generative perplexity from 25.7 to 21.9, significantly narrowing the performance gap with autoregressive models.zhThe entertainment industry is known for its scrutiny of physical appearance, and celebrities often find themselves under intense pressure to conform to certain body standards.By resolving the intrinsic conflict between static computing and dynamic electromagnetic environments, the proposed framework significantly reduces computational overhead without performance degradation, offering a viable solution for resource-constrained cognitive receivers.zhOur analysis shows that the evaluated models often fail to appropriately refuse unsafe or non-compliant driving-related queries, underscoring the limitations of general-purpose safety alignment in driving contexts.zh We present the first systematic investigation of auditory EEG for BPR and evaluate cross-sensory training benefits. We approach this under PAC privacy, which provides instance-based privacy guarantees for arbitrary black-box functions by controlling mutual information (MI). Rigorous experiments across four datasets (CIFAR-10, MNIST, CINIC-10, and ImageNette), five backdoor attack scenarios, and seven alternative defenses confirm the effectiveness of SecureSplit under various challenging conditions. With this enhanced distinction, we develop an adaptive filtering approach that uses a majority-based voting scheme to remove contaminated embeddings while preserving clean ones. However, SL is susceptible to backdoor attacks, in which malicious clients subtly alter their embeddings to insert hidden triggers that compromise the final trained model. To address these challenges, we propose extbfSASA, a novel framework designed to enhance TC models via separated attention mechanism and semantic-aware contrastive learning~(CL). This novel evaluation paradigm is particularly vital for languages, where the data scarcity problem is magnified when creating flexible models for diverse target groups rather than a single, fixed simplification style. We propose CORE-T, a scalable, training-free framework that enriches tables with LLM-generated purpose metadata and pre-computes a lightweight table-compatibility cache. But on a night that should have been about her talent and contributions to the music industry, Trainor found herself fielding questions surrounding her obvious weight loss. "I didn't feel safe holding the baby, and at the same time I felt like my body was giving up on me." She immediately reached out to her doctor for help. Lake's individual weight loss totaled 35 pounds, and her success was sweeter to her because she did without the help of medication, despite her doctor telling her that she would not be able to do it on her own. While it may not be suitable for everyone, it has been shown to have a number of benefits in addition to weight loss including lowering cholesterol and blood pressure, reducing inflammation, and improving brain health. We will release the HCoT annotations and the TRACE framework to enable scalable and human-aligned S2S evaluation.zh Second, we demonstrate that models systematically overestimate salaries for AI-related jobs relative to closely matched non-AI jobs, with proprietary models overestimating AI salaries more by 10 percentage points. First, we show that LLMs disproportionately recommend AI-related options in response to diverse advice-seeking queries, with proprietary models doing so almost deterministically. She also admitted that her view of body positivity had shifted to something more attainable. In March, Lizzo decided to open up about her weight-loss journey to The New York Times, revealing that she had been losing weight "very slowly" by doing activities like walking and Pilates. "I really thought you cared about body positivity. And you just jump on the Ozempic train. This always happens." We further integrate the trained surrogate model into an interactive neurosurgical simulation environment, achieving runtimes below 10 ms per simulation step on consumer-grade inference hardware. Specifically, training consists of short stochastic rollouts in which the proportion of ground truth inputs is gradually decreased in favor of model-generated predictions. To reduce the accumulation of errors in autoregressive inference, we propose a stochastic teacher forcing strategy applied during model training. We introduce negation through text augmentation and a dissimilarity-based contrastive loss, designed to explicitly separate original and negated captions in the joint embedding space. We present ANCHOR, a modular framework that makes decoupling and robustness explicit system-level primitives. This progression establishes (a) that ternary polynomial threshold representations exist for all tested functions, and (b) that finding them requires methods beyond pure gradient descent as dimensionality grows. In 2011, Melissa revealed that she was once told by a manager that she would never achieve success if she didn’t lose weight. The Bridesmaids actress, who has struggled with her body image since she was a teen, reportedly lost an estimated 75 to 95 pounds (34 to 43 kg) gradually, over the course of five years. Last month, the actress stunned fans with her slimmer physique when she hosted Saturday Night Live for the sixth time. “I never looked up to anyone because of their weight. She also denied rumors that she followed a plant-based diet. In 2017, Jonah, who reportedly turned vegan as part of his transformation, said he asked his 21 Jump Street co-star Channing Tatum for weight-loss advice. She explained that she lost weight “not to look hot which does feel fun and temporary” but to “survive.” The comedian has spoken openly about shedding 50 pounds (22 kg) after undergoing liposuction and taking the weight-loss medication Mounjaro. Through extensive experiments on a variety of public and real-world business datasets, we demonstrate that TIDE’s topic modeling approach outperforms modern topic modeling methods, and our auxiliary components provide valuable support for dealing with industrial business scenarios. Experiments on benchmark datasets such as DetoxLLM and ParaDetox show that our method achieves better detoxification performance than state-of-the-art methods while preserving semantic fidelity. Experiments show that, compared to baseline models, our proposed method significantly improves the innovation of the generated problems while maintaining a high correctness rate.zh In recent years, the rapid development of large language models (LLMs) has enabled new technological approaches to problem-generation tasks. 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 Even severe interventions, such as applying hard attention masks that directly prevent the model from attending to prior reasoning steps, do not substantially reduce selection performance. WFMLoss also incorporates path- and horizon-weighted strategies to focus learning on more reliable paths and horizons. The experimental results across four widely used conversational benchmarks demonstrate the effectiveness of our methods by surpassing several existing strong baselines.zh We introduce a conversational agent that interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through reinforcement learning (RL) training with tailored rewards towards evolving user goals. The experimental results show that VC-LLM performs better than the existing mainstream models in both Chinese and English tests, verifying the effectiveness of the method. Using this framework, we develop extbfTVTheseus, a foundation model specialized for TV navigation. A common approach to handle missing data is called importance sampling, in which we reweigh old data from a base policy to estimate gradients for the current policy. In this paper, we present a novel agentic technique for bug localization – CogniGent – that overcomes the limitations above by leveraging multiple AI agents capable of causal reasoning, call-graph-based root cause analysis and context engineering. Traditional methods for bug localization often analyze the suspiciousness of code components (e.g., methods, documents) in isolation, overlooking their connections with other components in the codebase. Analysis of architectural constraints, including open access literature limitations and challenges inherent to automated novelty assessment, informs practical deployment considerations for AI-assisted scientific workflows.zh Evaluation on the BixBench computational biology benchmark demonstrated state-of-the-art performance, achieving 48.8% accuracy on open response and 64.5% on multiple-choice evaluation, exceeding existing baselines by 14 to 26 percentage points. We outline the attack model and objective evaluation metrics for assessing privacy protection (concealing speaker voice identity) and utility (content and emotional state preservation). The task was to develop a voice anonymization system for speech data that conceals a speaker’s voice identity while preserving linguistic content and emotional state. Building on ATOD, we propose ATOD-Eval, a holistic evaluation framework that translates these dimensions into fine-grained metrics and supports reproducible offline and online evaluation. Beyond empirical comparisons, we analyze embedding space, showing that metadata integration improves effectiveness by increasing intra-document cohesion, reducing inter-document confusion, and widening the separation between relevant and irrelevant chunks. Across multiple retrieval metrics and question types, we find that prefixing and unified embeddings consistently outperform plain-text baselines, with the unified at times exceeding prefixing while being easier to maintain. The Pitch Perfect star said she turns to weight-loss jabs when her schedule gets busy and she doesn’t have time to work out. Lana flaunted her weight loss in 2024 after revealing she had been working out at Taylor Swift’s go-to gym, Dogpound. In 2020, Adele posted a picture to Instagram highlighting her weight loss in a black minidress, sparking Google searches for the “Adele diet.” “I’m really impressed by Melissa’s weight loss progress,” one viewer wrote. The conversation around weight loss is far from new. We design heuristic policies, train learning agents with expert demonstrations, and improve them using Dataset Aggregation (DAgger). 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. Leveraging this structure, we establish the first explicit non-asymptotic last-iterate convergence guarantees for stochastic policy gradient methods for finite MDPs without any form of preconditioning. To bridge this divide, we present Habibi, a suite of specialized and unified text-to-speech models that harnesses existing open-source ASR corpora to support a wide range of high- to low-resource Arabic dialects through linguistically-informed curriculum learning. 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. Hence, future models may only require prosody, providing privacy and potential performance benefits. Our theory formalizes the preconditioning effect induced by spectral orthogonalization, offering insight into Muon’s effectiveness in these matrix optimization problems and potentially beyond.zh Experimental results show that APOLO consistently improves diagnostic accuracy and robustness across domain-specific and stratified benchmarks, demonstrating a scalable and generalizable paradigm for trustworthy LLM applications in mental healthcare.zh To address these challenges, we propose APOLO (Automated Prompt Optimization for Linguistic Emotion Diagnosis), a framework that systematically explores a broader and finer-grained prompt space to improve diagnostic efficiency and robustness. We utilise snapshot confidence remasking to identify the most critical tokens that require modification, and apply mix-scale training to expand the block diffusion model’s global capabilities. However, their strict unidirectional block dependencies introduce irreversibility and sacrifice the global planning capabilities for which diffusion models are renowned. This empirical evidence confirms that the proposed data-driven approach can effectively balance delivery efficiency and operational safety.zh We evaluated the framework on the Smart Logistics Dataset 2024, which contains real-world Internet of Things(IoT) sensor data. 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. The benchmark contains 200 prompts (50 object pairs times 4 relations) grouped into 100 counterfactual pairs obtained by swapping object roles. We additionally show that CADD, via its use of context and/or transcripts, is more robust to 5 adversarial evasion strategies, limiting performance degradation to an average of just -0.71% across all experiments. Our results establish that graph neural networks do not require supervised training nor explicit search to be effective. Focusing on the Travelling Salesman Problem, we show that encoding global structural constraints as an inductive bias enables a non-autoregressive model to generate solutions via direct forward passes, without search, supervision, or sequential decision-making. We introduce extbfReSearch, a multi-stage, reasoning-enhanced search framework that formulates Earth Science data discovery as an iterative process of intent interpretation, high-recall retrieval, and context-aware ranking.Lizzo has been very open and honest with fans about her changing body and hasn't shied away from sharing her transformation with the world.To address these limitations, we propose OmniTransfer, a unified framework for spatio-temporal video transfer.The two parameters vary with the prompt and the model; they can be interpreted in terms of an elementary noise rate, and the number of plausible erroneous tokens that can be predicted.Using MDG, we generate more than ten thousand MLaaS service instances and construct a large-scale benchmark dataset suitable for downstream evaluation. Experiments demonstrate that both methods, individually and combined, improve negation handling while largely preserving retrieval performance. By bridging nonlinear neural representations with reduced-order linear solvers at fixed linearization points, LSR provides a numerically grounded and broadly applicable refinement framework for supervised learning, operator learning, and scientific computing. In contrast, one-shot LSR systematically exposes accuracy levels not fully exploited by gradient-based training, frequently achieving order-of-magnitude error reductions. We introduce Linearized Subspace Refinement (LSR), a general and architecture-agnostic framework that exploits the Jacobian-induced linear residual model at a fixed trained network state. Several existing surveys touch upon LLM unlearning, some of which adopt a broader scope or concentrate on specialized aspects (Cooper et al., 2024; Zhou et al., 2024; Liu et al., 2024c; Xu, 2024; Barez et al., 2025; Zhang et al., 2025b; Si et al., 2023; Qu et al., 2025).Compared to surveys that also focus specifically on LLM unlearning (Geng et al., 2025a; Blanco-Justicia et al., 2025), this work offers a more systematic and comprehensive perspective, with several distinctive contributions in the following paragraph.A detailed comparison is summarized in Table 1. As a kid, she spent most of her time consuming as much knowledge as she could get her hands on and could always be found at the library. Shanilou has always loved reading and learning about the world we live in. In her book, Simply More, the Tony winner discussed the dangers of body-shaming, writing, “In today’s society, there’s a degree of ease involved in commenting on others. Being skinny can be a competition sometimes,” one Redditor suggested.