arXiv:2607.00397v1 Announce Type: cross Abstract: Understanding how complex cognitive functions are organized within artificial systems is central to interpreting large language models (LLMs) and relating them to biological cognition. Yet although LLMs exhibit broad cognitive-like behaviours, it remains unclear whether their internal representations form reproducible functional systems that explain behaviour, failure and links to human cognition. Here we present NeuroCogMap, a cognitive neuroscience-inspired framework that organizes internal features of LLMs into functional parcels and links t
The accelerating development of increasingly complex LLMs necessitates new methods for understanding their internal workings to ensure reliability and responsible deployment.
Understanding the internal cognitive organization of LLMs is critical for improving their interpretability, trustworthiness, and developing more advanced and biologically inspired artificial intelligence.
We now have a neuroscience-inspired framework, NeuroCogMap, that promises to systematically map and interpret the functional structures within large language models, moving beyond purely black-box analysis.
- · AI researchers
- · Developers of transparent AI
- · Cognitive science in AI
- · Ethical AI advocates
- · Black-box AI development
- · AI systems lacking interpretability
Increased understanding of how LLMs generate behavior, potentially leading to more targeted improvements and debugging.
Development of LLMs that are inherently more interpretable and aligned with human cognitive processes.
New insights into biological cognition derived from systematic analysis and comparison with artificial intelligence systems.
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Read at arXiv cs.CL