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Berik Sembayev

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#artificial intelligence Open access Sep 2026

Request for arXiv Endorsement — Independent Researcher in Autonomous AI

Request for arXiv Endorsement I am an independent researcher working on artificial intelligence, autonomous systems, AI safety, and autonomous AI architectures. My recent works include: • AI Cancer: The Principle of Proportional Irreversible Protection for Autonomous Artificial Intelligence SystemsDOI: 10.5281/zenodo.2...

Berik Sembayev · 0 citations
#artificial intelligence Open access Sep 2026

Request for arXiv Endorsement — Independent Researcher in Autonomous AI

Request for arXiv Endorsement I am an independent researcher working on artificial intelligence, autonomous systems, AI safety, and autonomous AI architectures. My recent works include: • AI Cancer: The Principle of Proportional Irreversible Protection for Autonomous Artificial Intelligence SystemsDOI: 10.5281/zenodo.2...

Berik Sembayev · 0 citations
#large language models Open access Sep 2026

Can an LLM Pass Theory of Mind Without Physical Experience? The Role of Question Formulation and Perspective Organization

The ability of large language models (LLMs) to solve Theory of Mind (ToM) tasks remains a subject of active debate. This paper explores the hypothesis that some of the errors LLMs make in ToM tasks are not due to an inability to model another's perspective, but rather stem from how the question is formulated and how th...

Berik Sembayev · 0 citations
#large language models Open access Sep 2026

Can an LLM Pass Theory of Mind Without Physical Experience? The Role of Question Formulation and Perspective Organization

The ability of large language models (LLMs) to solve Theory of Mind (ToM) tasks remains a subject of active debate. This paper explores the hypothesis that some of the errors LLMs make in ToM tasks are not due to an inability to model another's perspective, but rather stem from how the question is formulated and how th...

Berik Sembayev · 0 citations
#artificial intelligence Open access Sep 2026

Five Stages of AI Development: From Chat to Superintelligence

The development of artificial intelligence is often described in terms of increasing model size, training data volume, context length, benchmark performance, and computational resources. These parameters are important; however, they do not necessarily reflect the most fundamental changes in the behavior of an AI system...

Berik Sembayev · 0 citations
#artificial intelligence Open access Sep 2026

Five Stages of AI Development: From Chat to Superintelligence

The development of artificial intelligence is often described in terms of increasing model size, training data volume, context length, benchmark performance, and computational resources. These parameters are important; however, they do not necessarily reflect the most fundamental changes in the behavior of an AI system...

Berik Sembayev · 0 citations
#artificial intelligence Open access Sep 2026

AI Cancer: The Principle of Proportional Irreversible Protection for Autonomous Artificial Intelligence Systems

The development of artificial intelligence is gradually moving systems from responding to individual user requests toward prolonged operation involving memory, tools, external resources, and independently generated sequences of actions. This creates a problem distinct from an ordinary incorrect response: an AI system m...

Berik Sembayev · 0 citations
#artificial intelligence Open access Sep 2026

AI Cancer: The Principle of Proportional Irreversible Protection for Autonomous Artificial Intelligence Systems

The development of artificial intelligence is gradually moving systems from responding to individual user requests toward prolonged operation involving memory, tools, external resources, and independently generated sequences of actions. This creates a problem distinct from an ordinary incorrect response: an AI system m...

Berik Sembayev · 0 citations
#large language models Open access Sep 2026

Searching for the Limit Point of Self-Improvement in Large Language Models Through Changes in the Probability of Their Own Answers

This paper proposes a research hypothesis concerning the possibility of identifying the point at which further iterative reconsideration of a task by a large language model ceases to produce substantial improvement. The central idea is based on an iterative tool that does not simply ask a model to answer the same promp...

Berik Sembayev · 0 citations
#large language models Open access Sep 2026

Searching for the Limit Point of Self-Improvement in Large Language Models Through Changes in the Probability of Their Own Answers

This paper proposes a research hypothesis concerning the possibility of identifying the point at which further iterative reconsideration of a task by a large language model ceases to produce substantial improvement. The central idea is based on an iterative tool that does not simply ask a model to answer the same promp...

Berik Sembayev · 0 citations

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