AI RESEARCH
Context Steering: A New Paradigm for Compression-based Embeddings by Synthesizing Relevant Information Features
arXiv CS.LG
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ArXi:2508.14780v2 Announce Type: replace Compression-based dissimilarities (CD) offer a flexible and domain-agnostic means of measuring similarity by identifying implicit information through redundancies between data objects. However, as similarity features are derived from the data, rather than defined as an input, it often proves difficult to align with the task at hand, particularly in complex clustering or classification settings. To address this issue, we