Abstract
Counterfactual Contrast Embeddings (CCE) introduce a dual-structured semantic representation in which each encoded instance explicitly models both its affirmative meaning and its nearest plausible misinterpretations. Unlike conventional embeddings that optimize for proximity among semantically similar inputs, the disclosed architecture embeds inputs within a bidirectional semantic manifold consisting of an attraction component and a repulsion component. The repulsion component is not derived from arbitrary negatives but from systematically generated counterfactual variants that approximate high-risk confusion regions in latent space. This enables the embedding to encode not only what an input represents, but also the boundary conditions of what it must not be interpreted as. The resulting representation supports retrieval and reasoning systems that are intrinsically resistant to false-positive similarity, semantic drift, and hallucination propagation. The disclosure further specifies training dynamics, masking operators, and retrieval scoring functions that operationalize contrastive exclusion at scale, enabling robust deployment in high-stakes AI systems.
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Recommended Citation
Manuel-Devadoss, Johny, "Counterfactual Contrast Embeddings (CCE)", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9960