Samuel Moor-Smith | Searcharxiv

arXiv2026

Testing the validity of multiple opinion dynamics models

While opinion dynamics models have been extensively studied as stylized models, there has been growing attention to the possibility of combining these models with empirical data. This attention seems to be driven by the many social issues that strongly depend on people's opinions (such as climate change and vaccination) and the need for empirically valid models to design related policy interventions. While different models have been combined in various ways with empirical data, standardised comparison of models against empirical data is still lacking. In this article, we test the validity of multiple opinion dynamics models--including both stylized and more realistic models. Our approach follows a "data science-like" validation procedure, where we first calibrate the model's free parameters using an initial range of years (e.g. 2010-2015), and then use data from one wave (e.g. 2016) to predict data in the following wave (e.g. 2017). We initially tested such a procedure using simulated data and then tested different models on various topics from the European Social Survey. Both toy models and empirical models perform well on the simulated data, but fail to predict future years in the empirical data. Furthermore, during the calibration phase on the empirical data, most models learned to "freeze"--meaning that their predictions for the following year are just a copy of the data from the previous year. This work advances the literature by offering a benchmark for comparing different opinion dynamics models. Furthermore, our tests show that real-world dynamics appear to be completely incompatible with the dynamics of the tested models. This calls for more effort in exploring what are the features that would improve validity and applications for opinion dynamics models.

physics.soc-ph↗

arXiv2025

Soft-Masked Diffusion Language Models

Diffusion models have demonstrated strong potential in language modeling, offering various advantages over traditional autoregressive approaches. Their ability to generate and revise entire responses in parallel enables faster generation and built-in self-correction mechanisms. Most modern diffusion-based language models employ masked diffusion, where decoding involves iteratively processing masked tokens based on a binary decision: either retaining the mask or replacing it with the predicted token. However, this binary choice discards valuable predictive information when the mask is retained. To address this limitation, we introduce soft-masking (SM), a novel method that dynamically blends the embedding of the mask token with the embeddings of the top-k predicted tokens from the previous decoding step, for each retained mask. This provides the model with a more informative prior, preserving context from earlier computations and allowing partial information about masked tokens to propagate beyond a single step. We propose a training methodology that efficiently adapts masked diffusion language models to incorporate SM. We demonstrate that training a 169M parameter model from scratch with SM yields superior perplexity and MAUVE scores compared to binary masking baselines. Similarly, a pretrained model can be enhanced with SM through continued pretraining. Finally, we finetune two state-of-the-art diffusion models, Dream-7B and Dream-Coder-7B, with SM. SM consistently improves performance across multiple coding benchmarks, particularly in high-throughput settings. The code is available at https://github.com/IBM/soft-masked-diffusion-language-models.

cs.LG↗