# SimSiam Naming Game: A Unified Approach for Emergent Communication and Representation Learning Source: [SimSiam Naming Game: A Unified Approach for Emergent Communication and Representation Learning]() Collection index: [llms.txt]() Data updated: 2026-06-17T05:00:14.085Z Authors: Nguyen Le Hoang, Tadahiro Taniguchi, Tianwei Fang, Akira Taniguchi, Masatoshi Nagano Published: 2024-10-29T07:16:20.000Z ## Abstract Emergent Communication (EmCom) investigates how agents develop symbolic communication through interaction without predefined language. Recent frameworks, such as the Metropolis--Hastings Naming Game (MHNG), formulate EmCom as the learning of shared external representations negotiated through interaction under joint attention, without explicit success or reward feedback. However, MHNG relies on sampling-based updates that suffer from high rejection rates in high-dimensional perceptual spaces, making the learning process sample-inefficient for complex visual datasets. In this work, we propose the SimSiam Naming Game (SSNG), a feedback-free EmCom framework that replaces sampling-based updates with a symmetric, self-supervised representation alignment objective between autonomous agents. Building on a variational inference--based probabilistic interpretation of self-supervised learning, SSNG formulates symbol emergence as an alignment process between agents' latent representations mediated by message exchange. To enable end-to-end gradient-based optimization, discrete symbolic messages are learned via a Gumbel--Softmax relaxation, preserving the discrete nature of communication while maintaining differentiability. Experiments on CIFAR-10 and ImageNet-100 show that the emergent messages learned by SSNG achieve substantially higher linear-probe classification accuracy than those produced by referential games, reconstruction games, and MHNG. These results indicate that self-supervised representation alignment provides an effective mechanism for feedback-free EmCom in multi-agent systems. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Emergent Communication Evaluation on ImageNet-100 20-class subset](): Emergent Communication Evaluation; ImageNet-100 20-class subset; Top-1 Accuracy: 44.6. - [Emergent Message Classification on CIFAR-10 standard (test)](): Emergent Message Classification; CIFAR-10 standard (test); Top-1 Accuracy: 51.12. ## Original sources and implementation [Original paper]() [PDF]()