While finite-dimensional irreducible representations (IRs) are attractive building blocks for equivariance due to their computationally exploitable structure, theyoften don'texist fornon-compact groups, precluding generalizations to most non-linear symmetries, let alone those ill-modeled by groups.

Privacy is a central challenge for systems that learn from sensitive data sets, especially when a system's outputs must be continuously updated to reflect changing

A.2 Documentation and intended uses We include a datasheet [1] in Section B. Detailed documentation on the precise structure and content OpenProteinSet is made available under the CC BY 4.0 license. The authors bear all responsibility in case of violation of rights. OpenProteinSet will continue to be hosted on RODA for the foreseeable future. A.7 Alignment tool settings For JackHMMer, we used -N 1 -E 0.0001 -incE 0.0001 -F1 0.0005 -F2 0.00005 -F3 0.0000005 and then capped outputs at depth 5000. B.1 Motivation For what purpose was the dataset created?

Each row of an MSA is a protein sequence.

To mitigate this gap, we first refine and extend the definition of honesty inLLMs based onthedefinition proposed byAskelletal.[14],astheability

Work done during an internship at IDEA.