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Paper

CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomics

by Independent / Community 026a67521f4c27296ae717837a40d7830942b9a1

Free2AITools Nexus Index

65.1

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 76

P: Popularity 51

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Integrating cognitive ergonomics with LLMs is crucial for improving safety, reliability, and user satisfaction in human-AI interactions. Current LLM designs often lack this integration, resulting in systems that may not fully align with human cognitive capabilities and limitations. This oversight exacerbates biases in LLM outputs and leads to suboptimal user experiences due to inconsistent application of user-centered design principles. Researchers are increasingly leveraging NLP, particularl...

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Paper Information Summary
Entity Passport
Registry ID	026a67521f4c27296ae717837a40d7830942b9a1
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{026a67521f4c27296ae717837a40d7830942b9a1,
  author = {Unknown},
  title = {CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomics Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/026a67521f4c27296ae717837a40d7830942b9a1}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomics [Paper]. Free2AITools. https://api.semanticscholar.org/026a67521f4c27296ae717837a40d7830942b9a1

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 76

Popularity (P) 51

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomics: Authority (A:76), Popularity (P:51), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Integrating cognitive ergonomics with LLMs is crucial for improving safety, reliability, and user satisfaction in human-AI interactions. Current LLM designs often lack this integration, resulting in systems that may not fully align with human cognitive capabilities and limitations. This oversight exacerbates biases in LLM outputs and leads to suboptimal user experiences due to inconsistent application of user-centered design principles. Researchers are increasingly leveraging NLP, particularl..."

❝ Cite Node

@article{Unknown2026CogErgLLM:,
  title={CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomics},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈10CitationsSemantic Scholar

🏛️76AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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🏷️ Research Topics

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📦Data Source: semantic_scholar

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 10

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