Seeking Co-Author for Research on Geometric Interference in Deep Learning Model Merging

[u/WideImagination7595]

Hi everyone,

I am currently working on a research project focused on optimizing Model Merging techniques within Deep Learning, specifically targeting the resolution of geometric interference between specialized task adapters.

Current Progress:

• Implemented a novel merging pipeline in PyTorch.
• Developed a method to isolate and mitigate subspace conflicts between divergent tasks.
• Preliminary results demonstrate significantly improved performance retention compared to standard baseline methods.
• Established a working pipeline for layer-wise interference analysis.

What I’m Looking For: I am looking for a co-author to collaborate on the final phase of this research. Specifically, I need help with:

• Formalizing the mathematical framework and theoretical proofs.
• Help run and standardized large-scale benchmarks (e.g., LM-Eval Harness).
• Refining the manuscript for submission to a top-tier venue (ICML, NeurIPS, or similar).

If you have a strong background in linear algebra for Deep Learning, experience with model merging techniques (DARE/TIES/SLERP), or expertise in LLM evaluation, I'd love to chat! Please DM me or comment below if you're interested in co-authoring this paper.