Memory as transformation: LETHE, a self-referential gan-inspired architecture

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On September 7, 2026, the LETHE model was published in the arXiv cs.CL domain. This research proposes a new architecture inspired by Generative Adversarial Networks (GANs), aimed at addressing the memory mechanism issues in Transformer models. The core innovation of LETHE lies in introducing a self-referential mechanism, defining the memory process as a specific transformation operation rather than traditional storage and retrieval. This design attempts to optimize the model’s long-term memory capabilities by mimicking the generative properties of GANs, representing a new direction in the design of memory modules for current large language models.

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Key entitiesKEY ENTITIES

SuperCollider

SignalsSIGNALS

Keyword heat

SuperCollider1

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A arXiv cs.CL en 2026-09-07 12:00

arXiv:2609.04289v1 presents the self-referential soundscape forgetting system named LETHE. This system is implemented based on SuperCollider and uses a generative adversarial network for vocabulary generation. Its core architecture consists of a 3x3 hybrid matrix composed of two delay lines and nine coefficients, with parameters evolving through the interaction between a linear discriminator and a random perturbation optimizer. In ablation control experiments with fixed and circular sessions, the active generator is crucial for parameter evolution (in all 15 ablation sessions, $\Delta c_{22}=0.000$). This system requires no external datasets or supervision, allowing independent mixing of circular, fixed, and on-site sources, and belongs to the tradition of self-referential electronic music.