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arXiv:2602.10706v2 Announce Type: replace Abstract: Estimating the expectation of a real-valued function of a random variable from sample data is a critical aspect of statistical...
arXiv:2602.10699v2 Announce Type: replace Abstract: Generative recommendation via autoregressive models has unified retrieval and ranking into a single conditional generation framework. However, fine-tuning these models...
arXiv:2602.10687v2 Announce Type: replace Abstract: Existing forgery detection methods are often limited to uni-modal or bi-modal settings, failing to handle the interleaved text, images, and...
arXiv:2602.10633v2 Announce Type: replace Abstract: With the growing deployment of sequential recommender systems in e-commerce and other fields, their black-box interfaces raise security concerns: models...
arXiv:2602.10606v2 Announce Type: replace Abstract: Generative recommendation models sequence generation to produce items end-to-end, but training from behavioral logs often provides weak supervision on underlying...
arXiv:2602.10564v2 Announce Type: replace Abstract: Federated fine-tuning of on-device large language models (LLMs) mitigates privacy concerns by preventing raw data sharing. However, the intensive computational...
arXiv:2602.10520v2 Announce Type: replace Abstract: Looped Language Models (LoopLMs) perform multi-step latent reasoning prior to token generation and outperform conventional LLMs on reasoning benchmarks at...
arXiv:2602.10516v2 Announce Type: replace Abstract: Audio-driven 3D talking avatar generation is increasingly important in virtual communication, digital humans, and interactive media, where avatars must preserve...
arXiv:2602.10480v2 Announce Type: replace Abstract: Large language models (LLMs) exhibit strong general-purpose reasoning capabilities, yet they frequently hallucinate when used as world models (WMs), where...
arXiv:2602.10467v2 Announce Type: replace Abstract: Bargaining is often regarded as a logical arena rather than an art or a matter of intuition, yet Large Language...
arXiv:2602.10445v2 Announce Type: replace Abstract: Generative Recommendation (GR) has excelled by framing recommendation as next-token prediction. This paradigm relies on Semantic IDs (SIDs) to tokenize...
arXiv:2602.10437v2 Announce Type: replace Abstract: Sparse autoencoders (SAEs) decompose language model activations into interpretable features, but existing methods reveal only which features activate, not which...
arXiv:2602.10431v2 Announce Type: replace Abstract: Large language models (LLMs) demand substantial computational and memory resources, posing challenges for efficient deployment. Two complementary approaches have emerged...
arXiv:2602.10271v2 Announce Type: replace Abstract: Understanding multimodal long-context documents that comprise multimodal chunks such as paragraphs, figures, and tables is challenging due to (1) cross-modal...
arXiv:2602.10125v2 Announce Type: replace Abstract: Network segmentation is a popular security practice for limiting lateral movement, yet practitioners lack a metric to measure how segmented...
arXiv:2602.10117v2 Announce Type: replace Abstract: Large Language Models (LLMs) often provide chain-of-thought (CoT) reasoning traces that appear plausible, but may hide internal biases. We call...
arXiv:2602.10081v2 Announce Type: replace Abstract: In scientific research, analysis requires accurately interpreting complex multimodal knowledge, integrating evidence from different sources, and drawing inferences grounded in...
arXiv:2602.10067v2 Announce Type: replace Abstract: Language models trained on large-scale datasets have been shown to learn features that encode abstract concepts such as factuality or...
arXiv:2602.10053v2 Announce Type: replace Abstract: Standard models of bounded rationality typically assume agents either possess accurate knowledge of the population's reasoning abilities (Cognitive Hierarchy) or...
arXiv:2602.09829v2 Announce Type: replace Abstract: Large Language Models (LLMs) are reshaping recommender systems by leveraging extensive world knowledge and semantic reasoning to interpret user intent....
arXiv:2602.09744v2 Announce Type: replace Abstract: Latent reasoning has emerged as a promising paradigm for sequential recommendation, enabling models to capture complex user intent through multi-step...
arXiv:2602.09725v2 Announce Type: replace Abstract: Remote KV cache reuse fetches KV cache for identical contexts from remote storage, avoiding recomputation, accelerating LLM inference. While it...
arXiv:2602.09523v2 Announce Type: replace Abstract: We present Singpath-VL, a vision-language large model, to fill the vacancy of AI assistant in cervical cytology. Recent advances in...
arXiv:2602.09477v2 Announce Type: replace Abstract: Digital histopathology whole slide images (WSIs) provide gigapixel-scale high-resolution images that are highly useful for disease diagnosis. However, digital histopathology...