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arXiv:2603.11266v1 Announce Type: new Abstract: Unlearning in Large Language Models (LLMs) aims to enhance safety, mitigate biases, and comply with legal mandates, such as the …
arXiv:2603.11623v1 Announce Type: new Abstract: Topological Data Analysis (TDA) provides powerful tools to explore the shape and structure of data through topological features such as …
arXiv:2603.11756v1 Announce Type: new Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing data likelihood. However, likelihood in observation …
arXiv:2603.11399v1 Announce Type: new Abstract: Users on e-commerce platforms can be uncertain about their preferences early in their search. Queries to recommendation systems are frequently …
arXiv:2603.11193v1 Announce Type: new Abstract: Reinforcement learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for eliciting reasoning capabilities in large language models, …
arXiv:2603.11594v1 Announce Type: new Abstract: Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of …
arXiv:2603.11415v1 Announce Type: new Abstract: Abstractive summarization requires models to generate summaries that convey information in the source document. While large language models can generate …
arXiv:2603.11679v1 Announce Type: new Abstract: As real-world datasets become increasingly complex and heterogeneous, supervised learning is often bottlenecked by input representation design. Modeling multimodal data …
arXiv:2603.11768v1 Announce Type: new Abstract: Long-term memory has emerged as a foundational component of autonomous Large Language Model (LLM) agents, enabling continuous adaptation, lifelong multimodal …
arXiv:2603.11535v1 Announce Type: new Abstract: Token-choice Mixture-of-Experts (TC-MoE) routes each token to a fixed number of experts, limiting dynamic computation allocation and requiring auxiliary losses …
arXiv:2603.11093v1 Announce Type: new Abstract: The development of high-level autonomous driving (AD) is shifting from perception-centric limitations to a more fundamental bottleneck, namely, a deficit …
arXiv:2603.11802v1 Announce Type: new Abstract: We introduce an expressive framework and algorithms for the semi-decentralized control of cooperative agents in environments with communication uncertainty. Whereas …