SMILES has to go : Representation of Molecules via Algebraic Data Types

This paper proposes a novel representation of molecules through Algebraic Data Types (ADTs). The representation has useful properties primarily by including type information. The representation uses the Dietz representation enabling representation of organometallics with multi-centre, multi-atom bonding and delocalised electrons, resonant structures and co-ordinate data of atoms. Furthermore, this representation goes further than any other in the literature, providing a natural data structure to represent shells, subshells and orbitals. Perks of the representation include it's natural inclusion in reaction descriptions and the ability to make molecules instances of algebraic groups. The representation is further motivated as providing guarantees for those wishing to do Bayesian machine learning (probabilistic programming) over molecular structures. A criticism of competing and commonly used representations such as SMILES and SELFIES is provided and solutions are proposed to the weaknesses of these along with an open source library, written in Haskell. An example of integrating the library with LazyPPL -- a lazy probabilistic programming library written in Haskell -- is provided, conceptually justifying the efficiency of the representation over string based representations and recent work such as SELFIES. This library distinguishes between the data and the type of data -- enabling a separation of concerns between interface and object. I solve three problems associated with the future of SELFIES, molecular programming language, 3D information, syntactic invalidity and Dietz representation.

Jailbreaking Large Language Models in Infinitely Many Ways

We discuss the "Infinitely Many Meanings" attacks (IMM), a category of jailbreaks that leverages the increasing capabilities of a model to handle paraphrases and encoded communications to bypass their defensive mechanisms. IMMs' viability pairs and grows with a model's capabilities to handle and bind the semantics of simple mappings between tokens and work extremely well in practice, posing a concrete threat to the users of the most powerful LLMs in commerce. We show how one can bypass the safeguards of the most powerful open- and closed-source LLMs and generate content that explicitly violates their safety policies. One can protect against IMMs by improving the guardrails and making them scale with the LLMs' capabilities. For two categories of attacks that are straightforward to implement, i.e., bijection and encoding, we discuss two defensive strategies, one in token and the other in embedding space. We conclude with some research questions we believe should be prioritised to enhance the defensive mechanisms of LLMs and our understanding of their safety.