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arXiv:2603.00546v1 Announce Type: new Abstract: Using Multimodal Large Language Models (MLLMs) as judges to achieve precise and consistent evaluations has gradually become an emerging paradigm …
arXiv:2603.00552v1 Announce Type: new Abstract: Evaluating persona-aligned empathy in LLM-based dialogue agents remains challenging. User states are latent, feedback is sparse and difficult to verify …
arXiv:2603.00575v1 Announce Type: new Abstract: Progress in software-engineering agents is increasingly constrained by the scarcity of executable, scalable, and realistic data for training and evaluation. …
arXiv:2603.00578v1 Announce Type: new Abstract: Long chain-of-thought~(CoT) has become a dominant paradigm for enhancing the reasoning capability of large reasoning models~(LRMs); however, the performance gains …
arXiv:2603.00585v1 Announce Type: new Abstract: Recent advances in video generation have opened new avenues for macroscopic simulation of complex dynamic systems, but their application to …
arXiv:2603.00590v1 Announce Type: new Abstract: As artificial intelligence (AI) is increasingly deployed across domains, ensuring fairness has become a core challenge. However, the field faces …
arXiv:2603.00599v1 Announce Type: new Abstract: Hypergraphs are the natural description of higher-order interactions among objects, widely applied in social network analysis, cross-modal retrieval, etc. Hypergraph …
arXiv:2603.00608v1 Announce Type: new Abstract: Student repetition in secondary education imposes significant resource burdens, particularly in resource-constrained contexts. Addressing this challenge, this study introduces a …
arXiv:2603.00623v1 Announce Type: new Abstract: Agentic systems augment large language models with external tools and iterative decision making, enabling complex tasks such as deep research, …
arXiv:2603.00631v1 Announce Type: new Abstract: LiTS is a modular Python framework for LLM reasoning via tree search. It decomposes tree search into three reusable components …
arXiv:2603.00656v1 Announce Type: new Abstract: Real-world user requests to LLM agents are often underspecified. Agents must interact to acquire missing information and make correct downstream …
arXiv:2603.00676v1 Announce Type: new Abstract: Existing mobile device control agents often perform poorly when solving complex tasks requiring long-horizon planning and precise operations, typically due …