For An Exciting Tomorrow
arXiv:2510.19685v1 Announce Type: cross Abstract: Feedback is one of the most powerful influences on student learning, with extensive research examining…
arXiv:2510.18279v2 Announce Type: replace-cross Abstract: Large language models (LLMs) and their multimodal variants can now process visual inputs, including images…
arXiv:2510.18982v1 Announce Type: new Abstract: While test-time scaling with verification has shown promise in improving the performance of large language…
arXiv:2510.19687v1 Announce Type: cross Abstract: Human communication is motivated: people speak, write, and create content with a particular communicative intent…
arXiv:2510.18876v2 Announce Type: replace-cross Abstract: While Multimodal Large Language Models (MLLMs) excel at holistic understanding, they struggle in capturing the…
arXiv:2510.17884v1 Announce Type: cross Abstract: The remarkable capabilities of Large Language Models (LLMs) in natural language understanding and generation have…
arXiv:2510.18559v1 Announce Type: cross Abstract: As AI systems enter high-stakes domains, evaluation must extend beyond predictive accuracy to include explainability,…
arXiv:2510.17901v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative decentralized training across multiple parties (nodes) while keeping raw data…
arXiv:2506.11097v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are transforming search engines into Conversational Search Engines (CSE). Consequently, Search…
arXiv:2510.18406v1 Announce Type: cross Abstract: Weakly supervised learning often operates with coarse aggregate signals rather than instance labels. We study…