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arXiv:2602.23668v1 Announce Type: new Abstract: Large language model (LLM) agents typically rely on reactive decision-making paradigms such as ReAct, selecting actions conditioned on growing execution …
arXiv:2602.23681v1 Announce Type: new Abstract: The paradigm of large language model (LLM) reasoning is shifting from parameter scaling to test-time compute scaling, yet many existing …
arXiv:2602.23701v1 Announce Type: new Abstract: LLM-powered Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in complex domains but suffer from inherent fragility and opaque failure mechanisms. …
arXiv:2602.23716v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents show promise for e-commerce conversational shopping, yet existing implementations lack the interaction depth and contextual …
arXiv:2602.23720v1 Announce Type: new Abstract: The field of Artificial Intelligence is undergoing a transition from Generative AI -- probabilistic generation of text and images -- …
arXiv:2602.23777v1 Announce Type: new Abstract: This paper addresses the domain generalization (DG) problem in deep learning. While most DG methods focus on enforcing visual feature …
arXiv:2602.23802v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual reasoning and understanding tasks but still struggle to capture …
arXiv:2602.23864v1 Announce Type: new Abstract: Multi-agent debate (MAD) systems leverage collective intelligence to enhance reasoning capabilities, yet existing approaches struggle to simultaneously optimize accuracy, consensus …
arXiv:2602.23876v1 Announce Type: new Abstract: Designing efficient reward functions for low-level control tasks is a challenging problem. Recent research aims to reduce reliance on expert …
arXiv:2602.23974v1 Announce Type: new Abstract: Offline reinforcement learning aims to learn an agent from pre-collected datasets, avoiding unsafe and inefficient real-time interaction. However, inevitable access …
arXiv:2602.24037v1 Announce Type: new Abstract: Market regime shifts induce distribution shifts that can degrade the performance of portfolio rebalancing policies. We propose macro-conditioned scenario-context rollout …
arXiv:2602.24080v1 Announce Type: new Abstract: The pursuit of human-like conversational agents has long been guided by the Turing test. For modern speech-to-speech (S2S) systems, a …