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arXiv:2511.12518v2 Announce Type: replace Abstract: In large-scale industrial recommendation systems, retrieval must produce high-quality candidates from massive corpora under strict latency. Recently, Generative Retrieval (GR)...
arXiv:2511.13095v2 Announce Type: replace Abstract: We introduce BeDiscovER (Benchmark of Discourse Understanding in the Era of Reasoning Language Models), an up-to-date, comprehensive suite for evaluating...
arXiv:2511.14702v2 Announce Type: replace Abstract: Accurate segmentation of myocardial scar from late gadolinium enhanced (LGE) cardiac MRI is essential for evaluating tissue viability, yet remains...
arXiv:2511.15169v3 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have significantly improved problem-solving through explicit Chain-of-Thought (CoT) reasoning. However, this capability creates a Safety-Helpfulness Paradox:...
arXiv:2511.15331v2 Announce Type: replace Abstract: Recent large language models (LLMs) show promise in design tasks, yet a fundamental misalignment persists: design thinking requires iterative intent...
arXiv:2511.16629v2 Announce Type: replace Abstract: Policy gradient methods, which have been extensively studied in the last decade, offer an effective and efficient framework for reinforcement...
arXiv:2511.16943v2 Announce Type: replace Abstract: Generative recommendation systems typically leverage Semantic Identifiers (SIDs), which represent each item as a sequence of tokens that encode semantic...
arXiv:2511.17044v2 Announce Type: replace Abstract: Parametric Retrieval-Augmented Generation (PRAG) is a RAG approach that integrates external knowledge directly into model parameters using a LoRA adapter,...
arXiv:2511.18247v2 Announce Type: replace Abstract: We derive instance-dependent tail bounds for the regret of optimism-based reinforcement learning in finite-horizon tabular Markov decision processes with unknown...
arXiv:2511.18267v2 Announce Type: replace Abstract: Residential buildings are increasingly integrating large devices that run natively on direct current (DC), such as solar photovoltaics, electric vehicles,...
arXiv:2511.18413v3 Announce Type: replace Abstract: Agentic recommendations cast recommenders as large language model (LLM) agents that can plan, reason, use tools, and interact with users...
arXiv:2511.18894v3 Announce Type: replace Abstract: Medical image segmentation is crucial for clinical applications, but it is frequently disrupted by noisy annotations and ambiguous anatomical boundaries,...
arXiv:2511.19233v2 Announce Type: replace Abstract: Localization is increasingly becoming an integral component of wireless cellular networks. The advent of artificial intelligence (AI) and machine learning...
arXiv:2511.21448v3 Announce Type: replace Abstract: Phishing and spam emails remain a major cybersecurity threat, with attackers increasingly leveraging Large Language Models (LLMs) to craft highly...
arXiv:2511.22690v2 Announce Type: replace Abstract: Despite recent advances in text-to-image generation, existing models consistently fail to produce reliable multi-human scenes, often duplicating faces, merging identities,...
arXiv:2512.01556v2 Announce Type: replace Abstract: Foundation models often generate unreliable answers, while heuristic uncertainty estimators fail to fully distinguish correct from incorrect outputs, causing users...
arXiv:2512.01557v2 Announce Type: replace Abstract: This study examines the digital representation of African languages and the challenges this presents for current language detection tools. We...
arXiv:2512.01628v2 Announce Type: replace Abstract: Existing two-stage fourth-order (TSFO) methods are confined to explicit frameworks and can only be applied to homogeneous hydrodynamic problems describing...
arXiv:2512.03470v4 Announce Type: replace Abstract: Infrared small target detection (ISTD) faces two major challenges: a lack of discernible target texture and severe background clutter, which...
arXiv:2512.04552v2 Announce Type: replace Abstract: Differentiable reinforcement learning (RL) frameworks like DiffRO offer a powerful approach for controllable text-to-speech (TTS), but are vulnerable to reward...
arXiv:2512.05742v2 Announce Type: replace Abstract: This memorandum analyzes and stress-tests arguments in favor and against the inclusion of internal deployment within the scope of the...
arXiv:2512.05790v3 Announce Type: replace Abstract: We develop a theoretical framework that explains how gating mechanisms determine the learnability window $\mathcal{H}_N$ of recurrent neural networks, defined...
arXiv:2512.07076v3 Announce Type: replace Abstract: Camouflage is primarily context-dependent yet current metrics for camouflaged scenarios overlook this critical factor. Instead, these metrics are originally designed...
arXiv:2512.09101v2 Announce Type: replace Abstract: We present Masked Generative Policy (MGP), a novel framework for visuomotor imitation learning. We represent actions as discrete tokens, and...