PHOTON: Hierarchical Autoregressive Modeling for Lightspeed and Memory-Efficient Language Generation

• URL: http://arxiv.org/abs/2512.20687v1
• Date: Mon, 22 Dec 2025 19:26:59 GMT
• Title: PHOTON: Hierarchical Autoregressive Modeling for Lightspeed and Memory-Efficient Language Generation
• Authors: Yuma Ichikawa, Naoya Takagi, Takumi Nakagawa, Yuzi Kanazawa, Akira Sakai,
• Abstract summary: We propose a hierarchical autoregressive model that replaces flat scanning with vertical, multi-resolution context access.<n> Experimental results show that PHOTON is superior to competitive Transformer-based language models regarding the throughput-quality trade-off.
• Score: 5.553946791700077
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Transformers operate as horizontal token-by-token scanners; at each generation step, the model attends to an ever-growing sequence of token-level states. This access pattern increases prefill latency and makes long-context decoding increasingly memory-bound, as KV-cache reads and writes dominate inference throughput rather than arithmetic computation. We propose Parallel Hierarchical Operation for Top-down Networks (PHOTON), a hierarchical autoregressive model that replaces flat scanning with vertical, multi-resolution context access. PHOTON maintains a hierarchy of latent streams: a bottom-up encoder progressively compresses tokens into low-rate contextual states, while lightweight top-down decoders reconstruct fine-grained token representations. Experimental results show that PHOTON is superior to competitive Transformer-based language models regarding the throughput-quality trade-off, offering significant advantages in long-context and multi-query tasks. This reduces decode-time KV-cache traffic, yielding up to $10^{3}\times$ higher throughput per unit memory.

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