Wasserstein-Barycentric Interaction Fields for Spatial Factor Models: Evidence from Language-Model Representations
Summary
This paper introduces a method using Wasserstein barycenters to reconstruct language model embedding fields for predicting peer-misalignment penalties in spatial factor models, outperforming conventional weighting schemes.
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Paper page - Wasserstein-Barycentric Interaction Fields for Spatial Factor Models: Evidence from Language-Model Representations
Source: https://huggingface.co/papers/2608.29669
Abstract
A language-model embedding field reconstructed via Wasserstein barycenters predicts peer-misalignment penalties more accurately than conventional weighting schemes.
Spatial return models take the interaction matrix as given and leave feedback uninterpreted. We construct a bandwidth-free field from firms’ language-model article embedding distributions using target-anchoredWasserstein barycentric reconstruction. Aquadratic exposure-adjustmentproblem maps feedback into a peer-misalignment penalty ratio. For 52 firms, the field, frozen from 2018-2022 news, yields a 2023-2026 penalty ratio of 3.46 (95% interval [2.89, 4.17]) and higher conditional quasi-likelihood than equal-weighted peer support or RBF weighting of the same distances. Joint penalty ratios for the barycentric and news co-mention fields are 2.33 and 0.86 with boundary calibrated tests which reject both exclusions.
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