This paper addresses the challenge of handling unseen modalities and dynamic modality combinations at test time with our proposed text-centric alignment method. This training-free alignment approach unifies different input modalities into a single semantic text representation by leveraging in-context learning with Large Language Models and uni-modal foundation models. Our method significantly enhances the ability to manage unseen, diverse, and unpredictable modality combinations, making it suitable for both generative and discriminative models to adopt on top. Our extensive experiments primarily evaluate on discriminative tasks, demonstrating that our approach is essential for LLMs to achieve strong modality alignment performance. It also surpasses the limitations of traditional fixed-modality frameworks in embedding representations. This study contributes to the field by offering a flexible and effective solution for real-world applications where modality availability is dynamic and uncertain.

LLM search methods, such as Chain of Thought (CoT) and Tree of Thought (ToT), enhance LLM reasoning by exploring multiple reasoning paths. When combined with search algorithms like MCTS and Bandit methods, their effectiveness relies heavily on uncertainty estimation to prioritize paths that align with specific search objectives. However, it remains unclear whether existing LLM uncertainty metrics adequately capture the diverse types of uncertainty required to guide different search objectives. In this work, we introduce a framework for uncertainty benchmarking, identifying four distinct uncertainty types: Answer, Correctness, Aleatoric, and Epistemic Uncertainty. Each type serves different optimization goals in search. Our experiments demonstrate that current metrics often align with only a subset of these uncertainty types, limiting their effectiveness for objective-aligned search in some cases. These findings highlight the need for additional target-aware uncertainty estimators that can adapt to various optimization goals in LLM search.