arXiv:2606.03739v1 Announce Type: new Abstract: LLM pipelines waste substantial token budgets on low-information content: repeated context, verbose responses, and redundant boilerplate. We introduce Entropy Gate, a token compression framework applying entropy quenching $-$ a thermodynamic process that progressively freezes out low-energy tokens while preserving semantic fidelity. Each token receives a multi-factor information energy $E(t)$ combining statistical, structural, and positional components. An adaptive quenching schedule $T(\tau) = T_0 / (1 + \alpha \tau)$ removes tokens whose Boltzm
The proliferation of increasingly complex LLMs and their high operational costs necessitate novel solutions for efficiency, making compression techniques like Entropy Gate timely.
This development addresses a critical bottleneck in large language model pipelines by significantly reducing token consumption, which directly impacts computational expense and environmental footprint.
LLM pipelines can now process information more efficiently and cost-effectively, potentially enabling larger context windows and more complex reasoning with existing compute resources.
- · LLM operators and developers
- · Cloud providers offering LLM services
- · AI-driven software companies
- · Consumers of AI services
- · High-latency networking providers
- · Companies relying on inefficient token generation
Reduced operational costs and increased throughput for large language models.
Acceleration of LLM adoption in new applications due to lower barriers to entry and improved performance.
Increased demand for specialized hardware optimized for compressed data, potentially leading to new chip architectures or advancements in memory management.
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Read at arXiv cs.CL