Do Androids Question Electric Sheep? A Multi-Agent Cognitive Simulation of Philosophical Reflection on Hybrid Table Reasoning

While LLMs demonstrate remarkable reasoning capabilities and multi-agent applicability, their tendency to "overthink" and "groupthink" pose intriguing parallels to human cognitive limitations.Inspired by this observation, we conduct an exploratory simulation to investigate whether LLMs are wise enough to be thinkers of philosophical reflection.We design two frameworks, Philosopher and Symposium, which simulate self-and groupreflection for multi-persona in hybrid table reasoning tasks.Through experiments across four benchmarks, we discover that while introducing varied perspectives might help, LLMs tend to under-perform simpler end-to-end approaches.We reveal from close reading five emergent behaviors which strikingly resemble human cognitive closure-seeking behaviors, and identify a consistent pattern of "overthinking threshold" across all tasks, where collaborative reasoning often reaches a critical point of diminishing returns.This study sheds light on a fundamental challenge shared by both human and machine intelligence: the delicate balance between deliberation and decisiveness.

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Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.acl-srw.9
Primary Topic
Child and Animal Learning Development
Type
article
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article

Do Androids Question Electric Sheep? A Multi-Agent Cognitive Simulation of Philosophical Reflection on Hybrid Table Reasoning

Yiran Rex
Child and Animal Learning Development
article

Do Androids Question Electric Sheep? A Multi-Agent Cognitive Simulation of Philosophical Reflection on Hybrid Table Reasoning

Yiran Rex
article en

Abstract

While LLMs demonstrate remarkable reasoning capabilities and multi-agent applicability, their tendency to "overthink" and "groupthink" pose intriguing parallels to human cognitive limitations.Inspired by this observation, we conduct an exploratory simulation to investigate whether LLMs are wise enough to be thinkers of philosophical reflection.We design two frameworks, Philosopher and Symposium, which simulate self-and groupreflection for multi-persona in hybrid table reasoning tasks.Through experiments across four benchmarks, we discover that while introducing varied perspectives might help, LLMs tend to under-perform simpler end-to-end approaches.We reveal from close reading five emergent behaviors which strikingly resemble human cognitive closure-seeking behaviors, and identify a consistent pattern of "overthinking threshold" across all tasks, where collaborative reasoning often reaches a critical point of diminishing returns.This study sheds light on a fundamental challenge shared by both human and machine intelligence: the delicate balance between deliberation and decisiveness.

Beijing University of Posts and Telecommunications (CN)
Reduced inequalities
Openalex Percentile: Top 79%
Child and Animal Learning Development
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