Entropic-Time Inference: Self-Organizing Large Language Model Decoding Beyond Attention

• URL: http://arxiv.org/abs/2603.03310v1
• Date: Sun, 08 Feb 2026 19:13:26 GMT
• Title: Entropic-Time Inference: Self-Organizing Large Language Model Decoding Beyond Attention
• Abstract summary: We introduce a selforganizing inference architecture that jointly couples scheduling, attention sparsification, and sampling temperature.<n>Our method extends vLLM with entropy-aware scheduling, entropic pruning of paged attention blocks, and adaptive temperature control that stabilizes generation near a target entropy regime.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern large language model (LLM) inference engines optimize throughput and latency under fixed decoding rules, treating generation as a linear progression in token time. We propose a fundamentally different paradigm: entropic\-time inference, where decoding is governed by the flow of uncertainty rather than token index. We introduce a self\-organizing inference architecture that jointly couples scheduling, attention sparsification, and sampling temperature under a unified entropy control objective. Our method extends vLLM with entropy-aware scheduling, entropic pruning of paged attention blocks, and adaptive temperature control that stabilizes generation near a target entropy regime. This transforms inference into a resource\-intelligent thermodynamic process that allocates computation where uncertainty reduction is maximized. We present a concrete systems design, pseudocode, and integration plan, demonstrating how entropy can serve as a first\-class control signal for scalable LLM inference.

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