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

arXiv:2603.03310v1 Announce Type: new 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

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Andrew Kiruluta · · 1 min read · 9 views
#cs.CL #cs.LG

arXiv:2603.03310v1 Announce Type: new 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.

Executive Summary

This article proposes a novel approach to large language model (LLM) inference, dubbed 'entropic-time inference.' It seeks to transform the traditional decoding process by leveraging the flow of uncertainty rather than token index. The authors introduce a self-organizing inference architecture that couples scheduling, attention sparsification, and sampling temperature under a unified entropy control objective. This approach is designed to optimize resource allocation and reduce uncertainty in LLM generation. The authors provide a concrete systems design, pseudocode, and integration plan, demonstrating the potential of entropy as a first-class control signal for scalable LLM inference. The proposed method has the potential to enhance the performance and efficiency of LLMs, but its practical feasibility and scalability require further investigation.

Key Points

  • ▸ Entropic-time inference: a novel paradigm for LLM decoding
  • ▸ Self-organizing inference architecture for joint entropy control
  • ▸ Unified entropy control objective: coupling scheduling, attention, and sampling temperature

Merits

Strength in adaptability

The proposed architecture adapts to changing uncertainty levels, allowing for more efficient resource allocation and reduced uncertainty in LLM generation.

Scalability potential

The use of entropy as a control signal has the potential to enhance the scalability of LLMs, making them more suitable for large-scale applications.

Demerits

Limited evaluation

The article lacks a thorough evaluation of the proposed method, including comparisons with existing decoding approaches and an assessment of its practical feasibility.

Computational complexity

The self-organizing inference architecture may introduce additional computational complexity, potentially offsetting the benefits of the proposed method.

Expert Commentary

While the proposed entropic-time inference paradigm shows promise, its practical feasibility and scalability require further investigation. The self-organizing inference architecture is a novel and intriguing approach, but its computational complexity and potential limitations in evaluation and comparison with existing methods are concerns. Nevertheless, the article's innovative ideas and potential implications make it a valuable contribution to the field of LLM research. As the field continues to evolve, it will be essential to address the challenges and limitations of the proposed method to realize its full potential.

Recommendations

  • ✓ Further evaluation and comparison with existing decoding approaches to assess the practical feasibility and scalability of the proposed method.
  • ✓ Investigation into the computational complexity of the self-organizing inference architecture and potential optimizations to mitigate its effects.

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