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arXiv:2510.04398v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in high-risk domains. However, state-of-the-art LLMs often produce...
arXiv:2510.04400v1 Announce Type: new Abstract: In this work, we explore the relevance of textual semantics to Large Language Models (LLMs),...
arXiv:2510.04391v1 Announce Type: cross Abstract: What is the computational objective of imagination? While classical interpretations suggest imagination is useful for...
arXiv:2509.08825v2 Announce Type: replace Abstract: Large language models are rapidly transforming social science research by enabling the automation of labor-intensive...
arXiv:2510.02493v1 Announce Type: cross Abstract: Conventionally, supervised fine-tuning (SFT) is treated as a simple imitation learning process that only trains...
arXiv:2510.02359v1 Announce Type: new Abstract: Improving air quality and addressing climate change relies on accurate understanding and analysis of air...
arXiv:2510.01252v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly trained on massive, uncurated corpora, understanding both model...
arXiv:2503.04697v2 Announce Type: replace Abstract: Reasoning language models have shown an uncanny ability to improve performance at test-time by “thinking...

Why treat LLM inference as batched kernels to DRAM when a dataflow compiler can pipe tiles through on-chip FIFOs and...

