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arXiv:2604.04103v1 Announce Type: new Abstract: High-stakes decision systems increasingly require structured justification, traceability, and auditability to ensure accountability and regulatory compliance. Formal arguments commonly used …
arXiv:2604.04215v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) are emerging as a compelling alternative to dominant autoregressive models, replacing strictly sequential token generation …
arXiv:2604.03922v1 Announce Type: new Abstract: Selecting LLM-generated code candidates using LLM-generated tests is challenging because the tests themselves may be incorrect. Existing methods either treat …
arXiv:2604.03589v1 Announce Type: new Abstract: Small language models (SLMs) have been increasingly deployed in edge devices and other resource-constrained settings. However, these models make confident …
arXiv:2604.03335v1 Announce Type: new Abstract: Apparent age estimation is a valuable tool for business personalization, yet current models frequently exhibit demographic biases. We review prior …
arXiv:2604.03858v1 Announce Type: new Abstract: Training Data Attribution (TDA) seeks to trace model predictions back to influential training examples, enhancing interpretability and safety. We formulate …
arXiv:2604.03754v1 Announce Type: new Abstract: Large language models (LLMs) have been shown to encode truth of statements in their activation space along a linear truth …
arXiv:2604.03478v1 Announce Type: new Abstract: In high-stakes settings where machine learning models are used to automate decision-making about individuals, the presence of algorithmic bias can …
arXiv:2604.04144v1 Announce Type: new Abstract: The holy grail of LLM personalization is a single LLM for each user, perfectly aligned with that user's preferences. However, …
arXiv:2604.03345v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) have recently emerged as a powerful architecture for various machine learning applications. However, their unique structure raises …
arXiv:2604.03553v1 Announce Type: new Abstract: AI is supporting, accelerating, and automating scientific discovery across a diverse set of fields. However, AI adoption in historical research …