Quality follows upgrading
Academic
arXiv:2603.20219v1 Announce Type: new Abstract: Autoregressive language models trained with next-token prediction generate text by sampling one discrete token at a time. Although very scalable, …
arXiv:2603.20276v1 Announce Type: new Abstract: A hallmark of human intelligence is Introspection-the ability to assess and reason about one's own cognitive processes. Introspection has emerged …
arXiv:2603.20510v1 Announce Type: new Abstract: Language models often lack grounded reasoning capabilities in specialized domains where training data is scarce but bespoke systems excel. We …
arXiv:2603.20212v1 Announce Type: new Abstract: Reward models (RMs) are critical for aligning Large Language Models via Reinforcement Learning from Human Feedback (RLHF). While Generative Reward …
arXiv:2603.21013v1 Announce Type: new Abstract: Despite recent advances in integrating Large Language Models (LLMs) into social robotics, two weaknesses persist. First, existing implementations on platforms …
arXiv:2603.20293v1 Announce Type: new Abstract: Text-attributed graphs, where nodes are enriched with textual attributes, have become a powerful tool for modeling real-world networks such as …
arXiv:2603.20255v1 Announce Type: new Abstract: Speech-based AI educational applications have gained significant interest in recent years, particularly for children. However, children speech research remains limited …
arXiv:2603.20216v1 Announce Type: new Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models, offering sub-linear generation latency and bidirectional …
arXiv:2603.20256v1 Announce Type: new Abstract: Autonomous science agents built on large language models (LLMs) are increasingly used to generate hypotheses, design experiments, and produce reports. …
arXiv:2603.20381v1 Announce Type: new Abstract: Understanding the fundamental mechanisms governing the production of meaning in the processing of natural language is critical for designing safe, …
arXiv:2603.20432v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable progress in scaling to access massive contexts. However, the access is via the …
arXiv:2603.20441v1 Announce Type: new Abstract: Verification-guided self-improvement has recently emerged as a promising approach to improving the accuracy of large language model (LLM) outputs. However, …