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arXiv:2603.09888v1 Announce Type: new Abstract: A fundamental requirement for intelligent systems is the ability to learn continuously under changing environments. However, models trained in this …
arXiv:2603.09677v1 Announce Type: new Abstract: Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the …
arXiv:2603.09313v1 Announce Type: new Abstract: Activation steering is a widely used approach for controlling large language model (LLM) behavior by intervening on internal representations. Existing …
arXiv:2603.09774v1 Announce Type: new Abstract: Achieving robust spatial reasoning remains a fundamental challenge for current Multimodal Foundation Models (MFMs). Existing methods either overfit statistical shortcuts …
arXiv:2603.09049v1 Announce Type: new Abstract: Autonomous agents are increasingly used to improve prompts, code, and machine learning systems through iterative execution and feedback. Yet existing …
arXiv:2603.09434v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed across diverse real-world applications and user communities. As such, it is crucial that …
arXiv:2505.13903v1 Announce Type: cross Abstract: Large Language Models (LLMs) have recently achieved remarkable progress in mathematical reasoning. To enable such capabilities, many existing works distill …
arXiv:2603.08964v1 Announce Type: new Abstract: Forward reachability analysis is a dominant approach for verifying reach-avoid specifications in neural feedback systems, i.e., dynamical systems controlled by …
arXiv:2603.09481v1 Announce Type: new Abstract: We present GenePlan (GENeralized Evolutionary Planner), a novel framework that leverages large language model (LLM) assisted evolutionary algorithms to generate …
arXiv:2603.08879v1 Announce Type: new Abstract: Accessing sensitive patient data for machine learning is challenging due to privacy concerns. Datasets with annotations of personally identifiable information …
arXiv:2603.08999v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong reasoning performance through chain-of-thought (CoT) reasoning, yet often generate unnecessarily long reasoning paths that …
arXiv:2603.09157v1 Announce Type: new Abstract: As large language models evolve from conversational assistants to autonomous agents, ensuring trustworthiness requires a fundamental shift from post-hoc evaluation …