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arXiv:2602.24119v1 Announce Type: new Abstract: This study presents the first systematic, reference-free human evaluation of large language model (LLM) machine translation (MT) for Ancient Greek …
arXiv:2602.24142v1 Announce Type: new Abstract: Mobile Agents can autonomously execute user instructions, which requires hybrid-capabilities reasoning, including screen summary, subtask planning, action decision and action …
arXiv:2602.24172v1 Announce Type: new Abstract: Argumentative LLMs (ArgLLMs) are an existing approach leveraging Large Language Models (LLMs) and computational argumentation for decision-making, with the aim …
arXiv:2602.24174v1 Announce Type: new Abstract: Small language models (SLMs) have emerged as efficient alternatives to large language models for task-specific applications. However, they are often …
arXiv:2602.24188v1 Announce Type: new Abstract: We present a scalable methodology for evaluating language models in multi-turn interactions, using a suite of collaborative games that require …
arXiv:2602.24210v1 Announce Type: new Abstract: AI agents powered by reasoning models require access to sensitive user data. However, their reasoning traces are difficult to control, …
arXiv:2602.24287v1 Announce Type: new Abstract: Multi-turn interactions with large language models typically retain the assistant's own past responses in the conversation history. In this work, …
arXiv:2602.23365v1 Announce Type: cross Abstract: Organisations face polycrisis uncertainty yet overlook embedded knowledge. We show how generative AI can operate as a serendipity engine and …
arXiv:2602.23699v1 Announce Type: cross Abstract: The quadratic computational cost of processing vision tokens in Multimodal Large Language Models (MLLMs) hinders their widespread adoption. While progressive …
arXiv:2602.23734v1 Announce Type: cross Abstract: One-stream Transformer-based trackers achieve advanced performance in visual object tracking but suffer from significant computational overhead that hinders real-time deployment. …
arXiv:2602.23863v1 Announce Type: cross Abstract: With the aim of detecting AI-generated images and identifying the specific models responsible for their generation, we propose a multi-modal …
arXiv:2602.23866v1 Announce Type: cross Abstract: Software engineering agents (SWE) are improving rapidly, with recent gains largely driven by reinforcement learning (RL). However, RL training is …