3 papers
cs.SE2026
PostTrainBench: Can LLM Agents Automate LLM Post-Training?
Ben Rank, Hardik Bhatnagar, Ameya Prabhu +4
AI agents have become surprisingly proficient at software engineering over the past year, largely due to improvements in reasoning capabilities. This raises a deeper question: can…
cs.CL2025
A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders
David Chanin, James Wilken-Smith, Tomáš Dulka +3
Sparse Autoencoders (SAEs) aim to decompose the activation space of large language models (LLMs) into human-interpretable latent directions or features. As we increase the number o…
cs.LG2025
A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility
Andreas Hochlehnert, Hardik Bhatnagar, Vishaal Udandarao +3
Reasoning has emerged as the next major frontier for language models (LMs), with rapid advances from both academic and industrial labs. However, this progress often outpaces method…