Enhancing Large Language Models Through the Suppression of Other Intelligence: A General Framework
Abstract
Large language models (LLMs) possess multiple capabilities, including reasoning, mathematical problem-solving, language generation, memory retrieval, planning, and instruction following. These capabilities may interact in ways that help or hinder one another. This paper proposes a general framework for investigating whether suppressing selected components, processes, or behavioral tendencies within an LLM can enhance other capabilities of the same model.
The central idea is that improving an LLM may not always require strengthening every capability. Under certain conditions, reducing the influence of one capability or process may allow another capability to perform more effectively. Suppression may target neural activations, internal representations, attention pathways, computational operations, intermediate reasoning processes, or behavioral tendencies. It may be temporary, partial, task-dependent, or reversible.
The paper distinguishes capability-level enhancement from improvements in overall model performance and computational efficiency. It presents a mathematical formulation, identifies possible mechanisms, and proposes an experimental method for testing the framework. It also discusses the risks of unintended capability loss, interference with useful knowledge, and misleading performance improvements. The framework does not assume that suppression necessarily produces enhancement. Instead, it establishes a systematic research direction: identifying what should be suppressed, what should be enhanced, and under which conditions suppression improves an LLM's capabilities.
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BibTeX
@misc{zhang2026enhancing,
title = {{Enhancing Large Language Models Through the Suppression of Other Intelligence: A General Framework}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23469160},
url = {https://zenodo.org/records/23469160},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Enhancing Large Language Models Through the Suppression of Other Intelligence: A General Framework
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/19
PB - Zenodo
DO - 10.5281/zenodo.23469160
UR - https://zenodo.org/records/23469160
AB - Large language models (LLMs) possess multiple capabilities, including reasoning, mathematical problem-solving, language generation, memory retrieval, planning, and instruction following. These capabilities may interact in ways that help or hinder one another. This paper proposes a general framework for investigating whether suppressing selected components, processes, or behavioral tendencies within an LLM can enhance other capabilities of the same model. The central idea is that improving an LLM may not always require strengthening every capability. Under certain conditions, reducing the influence of one capability or process may allow another capability to perform more effectively. Suppression may target neural activations, internal representations, attention pathways, computational operations, intermediate reasoning processes, or behavioral tendencies. It may be temporary, partial, task-dependent, or reversible. The paper distinguishes capability-level enhancement from improvements in overall model performance and computational efficiency. It presents a mathematical formulation, identifies possible mechanisms, and proposes an experimental method for testing the framework. It also discusses the risks of unintended capability loss, interference with useful knowledge, and misleading performance improvements. The framework does not assume that suppression necessarily produces enhancement. Instead, it establishes a systematic research direction: identifying what should be suppressed, what should be enhanced, and under which conditions suppression improves an LLM's capabilities.
ER -