OATML

Vision-language models exhibit many surprisingly simple failures, but why these failures occur remains unclear. We conjecture that their source is representational misalignment in the backbone's vision and language representations. We demonstrate a new generalization failure that would not occur if the representations were easily alignable, followed by a set of theory-grounded experiments further showing that the representations cannot be aligned using any linear transform. The representations are not expected to be better aligned with sufficient scale due to each modality containing inherently different information. Modern paradigms, such as reasoning or in-context learning, do not alleviate existing failures either. These results suggest that existing paradigms are incapable of preventing these failures and falsify a strong version of the Platonic Representation Hypothesis -- that sufficiently powerful models trained in different modalities should converge to equivalent represent... [full abstract]

Code evolution is a family of techniques that rely on large language models to search through possible computer programs by evolving or mutating existing code. Many proposed code evolution pipelines show impressive performance but are often not compared to simpler baselines. We test how well two simple baselines do over three domains: finding better mathematical bounds, designing agentic scaffolds, and machine learning competitions. We find that simple baselines match or exceed much more sophisticated methods in all three. By analyzing these results we find various shortcomings in how code evolution is both developed and used. For the mathematical bounds, a problem's search space and domain knowledge in the prompt are chiefly what dictate a search's performance ceiling and efficiency, with the code evolution pipeline being secondary. Thus, the primary challenge in finding improved bounds is designing good search spaces, which is done by domain experts, and not the search itself. Wh... [full abstract]

Vision-language models fail at some tasks that are simple for humans, but why? Many failures point to a mismatch between the models' representations. We test this by showcasing a new failure: a VLM can misclassify simple concepts, like a zebra, if they are not explicitly used to align its vision-language representations. This occurs although the vision and language models separately know of the concept and persists for both retrieval and generative models. Thus, alignment is hard due to the language part of the VLM's representations clustering based not on semantics but other features, e.g. the first word in a sentence. We propose a method that can match images and captions without directly translating between their representations and demonstrate that it achieves good performance on a benchmark where VLMs struggle due to representational misalignment, beating models with two orders of magnitude more parameters.

Code evolution is a family of techniques that rely on large language models to search through possible computer programs by evolving or mutating existing code. Many proposed code evolution pipelines show impressive performance but are often not compared to simpler baselines. We test how well two simple baselines do over three domains: finding better mathematical bounds, designing agentic scaffolds, and machine learning competitions. We find that simple baselines match or exceed much more sophisticated methods in all three. By analyzing these results we find various shortcomings in how code evolution is both developed and used. For the mathematical bounds, a problem’s search space and domain knowledge in the prompt are chiefly what dictate a search’s performance ceiling and efficiency, with the code evolution pipeline being secondary. Thus, the primary challenge in finding improved bounds is designing good search spaces, which is done by domain experts, and not the search itself. When designing agentic scaffolds we find that high variance in the scaffolds coupled with small datasets leads to suboptimal scaffolds being selected, resulting in hand-designed majority vote scaffolds performing best. We propose better evaluation methods that reduce evaluation stochasticity while keeping the code evolution economically feasible. We finish with a discussion of avenues and best practices to enable more rigorous code evolution in future work. …