Papers with geometric metrics
GeoLAN: Geometric Learning of Latent Explanatory Directions in Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models lack transparency and are often unable to explain causal relationships . |
| Approach: | They propose a training framework that treats token representations as geometric trajectories and applies stickiness conditions to the Kakeya Conjecture. |
| Outcome: | The proposed training framework maintains task accuracy while improving geometric metrics and reducing fairness biases. |
How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Prior work on activation steering has focused on shaping reasoning traces, but it remains unclear how answer tokens actually read and integrate the reasoning to produce reliable outcomes. |
| Approach: | They propose a training-free steering method that uses self-reading quality scores to guide inference toward benign self-readiness and away from uncertain and disorganized reading. |
| Outcome: | The proposed method yields consistent accuracy gains in the reasoning traces generated by thinking LLMs. |