Quality follows upgrading
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arXiv:2602.18171v1 Announce Type: new Abstract: Clickbait headlines degrade the quality of online information and undermine user trust. We present a hybrid approach to clickbait detection …
arXiv:2602.18176v1 Announce Type: new Abstract: Masked Diffusion Models (MDMs) offer greater flexibility in decoding order than autoregressive models but require careful planning to achieve high-quality …
arXiv:2602.18217v1 Announce Type: new Abstract: Real-time sentence comprehension imposes a significant load on working memory, as comprehenders must maintain contextual information to anticipate future input. …
arXiv:2602.18232v1 Announce Type: new Abstract: Recent work on test-time scaling for large language model (LLM) reasoning typically assumes that allocating more inference-time computation uniformly improves …
arXiv:2602.18262v1 Announce Type: new Abstract: While mechanistic interpretability has developed powerful tools to analyze the internal workings of Large Language Models (LLMs), their complexity has …
arXiv:2602.18324v1 Announce Type: new Abstract: Psychological corpora in NLP are collections of texts used to analyze human psychology, emotions, and mental health. These texts allow …
arXiv:2602.18326v1 Announce Type: new Abstract: We describe a modern deep learning system that automatically identifies informative contextual examples (\qu{contexts}) for first language vocabulary instruction for …
arXiv:2602.18346v1 Announce Type: new Abstract: In jurisdictions like India, where courts face an extensive backlog of cases, artificial intelligence offers transformative potential for legal judgment …
arXiv:2602.18351v1 Announce Type: new Abstract: Real-world knowledge representation often requires capturing subjective, continuous attributes -- such as political positions -- that conflict with pairwise validation, …
arXiv:2602.18420v1 Announce Type: new Abstract: This study presents an ensemble technique, SPQ (SVD-Pruning-Quantization), for large language model (LLM) compression that combines variance-retained singular value decomposition …
arXiv:2602.18425v1 Announce Type: new Abstract: Comprehensively retrieving diverse documents is crucial to address queries that admit a wide range of valid answers. We introduce retrieve-verify-retrieve …
arXiv:2602.18429v1 Announce Type: new Abstract: Large Language Models (LLMs) have made significant progress in reasoning tasks across various domains such as mathematics and coding. However, …