Withdrawn paper questions LLM understanding and coherence

A withdrawn arXiv paper by Camilo Chacón Sartori explored the concept of the Bidirectional Coherence Paradox in large-language models (LLMs). The paper argued that LLMs can appear competent and provide coherent explanations, yet these explanations may not accurately reflect the underlying mechanisms of their success or lead to effective interventions. The research proposed an 'Epistemic Triangle' model to analyze how priors, signals, and domain knowledge interact, suggesting that neither behavioral success nor explanatory accuracy alone is sufficient to attribute understanding to AI agents. AI

IMPACT Challenges current evaluation practices for AI agents and suggests a need for more robust frameworks to assess understanding.

RANK_REASON The item is a withdrawn academic paper discussing AI concepts. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Bidirectional Coherence Paradox
• Camilo Chacon-Sartori
• Epistemic Triangle
• large-language models

• paper
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