{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/how-neural-networks-organize-concepts","title":"How Neural Networks Organize Concepts: Introducing Concept Trajectory Analysis for Deep Learning Interpretability","arxiv_id":null,"date":"2025-06-01","proceeding":"Independent Research 2025 6","authors":["Andrew Smigaj"],"abstract":"We present Concept Trajectory Analysis (CTA), an interpretability method that tracks how neural networks organize concepts by following their paths through clustered activation spaces across layers. Applying CTA to GPT-2 with 1,228 single-token words revealed that the model organizes language primarily by grammatical function rather than semantic meaning. We found that 48.5% of words converge to grammatical highways where nouns—whether animals, objects, or abstracts—travel together, while maintaining semantic distinctions at finer scales (χ2 = 95.90, p < 0.0001). \r\n    CTA combines geometric clustering with trajectory tracking to quantify how concepts flow through networks. Our method introduces windowed analysis to identify phase transitions (semantic→grammatical in GPT-2) and leverages LLMs to generate interpretable cluster labels. In medical AI, CTA exposed how a heart disease model stratifies patients through risk pathways, revealing demographic biases (male overprediction in Path 4, 83% male composition). \r\n   By making neural organization visible and quantifiable, CTA provides actionable insights for model debugging, bias detection, and scientific understanding of deep learning. Our open-source implementation enables researchers to apply CTA to any neural network, advancing interpretable AI across domains.","url_abs":"https://github.com/AndrewSmigaj/conceptual-trajectory-analysis-LLM-intereptability-framework/raw/refs/heads/main/abstract.md","url_pdf":"https://github.com/AndrewSmigaj/conceptual-trajectory-analysis-LLM-intereptability-framework/raw/main/view.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"how-neural-networks-organize-concepts","repo_url":"https://github.com/AndrewSmigaj/conceptual-trajectory-analysis-LLM-intereptability-framework","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bias-detection","task_name":"Bias Detection"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"cosine-annealing","method_name":"Cosine Annealing"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"discriminative-fine-tuning","method_name":"Discriminative Fine-Tuning"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gpt-2","method_name":"GPT-2"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-cosine-annealing","method_name":"Linear Warmup With Cosine Annealing"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}