Update README.md

README.md

@@ -25,19 +25,6 @@ We concentrate on understanding the relationship between internal structure in n
 
 3. **Geometry of Program Synthesis (GPS)**: Applying SLT to study inductive biases, advancing our understanding of how to predict and measure alignment-relevant risks.
 
-## Notable Achievements
-
-- Established developmental interpretability as a concrete application of SLT to alignment
-- Developed scalable new measuring tools like the Local Learning Coefficient (LLC)
-- Validated that SLT can make accurate predictions about real-world AI systems
-- Popularized SLT within the AI safety community through conferences, workshops, and collaborations
-
-## Key Publications
-
-- Quantifying Degeneracy in Singular Models via the learning coefficient (Lau et al. 2023)
-- Estimating the Local Learning Coefficient at Scale (Furman and Lau 2024)
-- The Developmental Landscape of In-Context Learning (Hoogland et al. 2024)
-
 ## Resources
 
 - [DevInterp GitHub Repository](https://github.com/timaeus-research/devinterp)