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-license: mit
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+<div align="center">
+
+# Geometry of Decision Making in Language Models
+
+**Abhinav Joshi · Divyanshu Bhatt · Ashutosh Modi**  
+*NeurIPS 2025*
+</div>
+
+
+<div align="center">
+<br/>
+<p align="center">
+<a href="https://arxiv.org/pdf/2511.20315">
+  <img src="https://img.shields.io/badge/arXiv-2511.20315-6A1B9A?style=for-the-badge&logo=arxiv&logoColor=white"/>
+</a>
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+<img src="https://img.shields.io/badge/Experimental Findings-Hugging%20Face-FFB000?style=for-the-badge&logo=huggingface&logoColor=black"/>
+</p>
+<a href="https://openreview.net/forum?id=Jj4NdJtXwp">
+  <img src="https://img.shields.io/badge/OpenReview-NeurIPS%202025-512DA8?style=for-the-badge"/>
+</a>
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+<br/><br/>
+</div>
+
+<div align="justify">
+
+> This repository contains the official implementation/release for the NeurIPS 2025 paper Geometry of Decision Making in Language Models. 
+> We study the internal decision-making processes of large language models through the lens of intrinsic dimension (ID), analyzing how hidden representations evolve across layers in a multiple-choice question answering (MCQA) setting.
+> This repository provides additional experimental results, intrinsic-dimension estimations, and layer-wise performance analysis, that we believe will be a valuable resource for researchers for further exploration in this area.
+</div>
+
+<div align="justify">
+
+![Teaser image](./thumbnail.png)
+**Picture:** *In the transformer-based architectures, a vector (latent features) of the same hidden dimensions $`d`$, is transformed by transformer blocks $`f_l`$. Though the extrinsic dimension remains the same, we find that the feature space lies on low-dimensional manifolds of different intrinsic dimensions. Intrinsically, there exists a mapping $`\phi_l`$ corresponding to each $`f_l`$. We study how these compressed manifolds align with the decision-making process in middle layers. We project the internal representations back to the vocabulary space to inspect the decisiveness. There is a sudden shift in performance that is aligned with the follow-up of a sharp peak observed in the residual-post ID estimates.*
+
+</div>
+
+## Abstract
+> Large Language Models (LLMs) show strong generalization across diverse tasks,
+yet the internal decision-making processes behind their predictions remain opaque.
+In this work, we study the geometry of hidden representations in LLMs through
+the lens of intrinsic dimension (ID), focusing specifically on decision-making
+dynamics in a multiple-choice question answering (MCQA) setting. We perform a
+large-scale study, with 28 open-weight transformer models and estimate ID across
+layers using multiple estimators, while also quantifying per-layer performance on
+MCQA tasks. Our findings reveal a consistent ID pattern across models: early
+layers operate on low-dimensional manifolds, middle layers expand this space,
+and later layers compress it again, converging to decision-relevant representations.
+Together, these results suggest LLMs implicitly learn to project linguistic inputs
+onto structured, low-dimensional manifolds aligned with task-specific decisions,
+providing new geometric insights into how generalization and reasoning emerge in
+language models.
+
+
+## Structure
+```
+results/
+  ├── dataset_name/
+  │   ├── fewshot_xx/
+  │   │   ├── model_id/
+  │   │       ├── last_token_relative_depth_xx/
+  │   │           ├── intrinsic/
+  │   │               ├── results.csv (global intrinsic estimates)
+  │   │               ├── mle_local_dims
+  │   │                   ├── layer_name.csv (local intrinsic estimates)
+  │   │           ├── llm_hook_args.json (arguments used for activation collection)
+  │   │           ├── llm_intrinsic_args.json (arguments used for ID estimation)
+  │   │           ├── accuracy_layer_name.json (accuracy per layer using logit lens)    
+  │   │           ├── accuracy_output.json (overall accuracy on the dataset)
+  │   │           ├── predictions.csv (predictions and corresponding ground truths)
+```
+
+## Citation
+
+```
+@inproceedings{
+joshi2025geometry,
+title={Geometry of Decision Making in Language Models},
+author={Abhinav Joshi and Divyanshu Bhatt and Ashutosh Modi},
+booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
+year={2025},
+url={https://openreview.net/forum?id=Jj4NdJtXwp}
+}
+```