Process Reward Models (PRMs) have emerged as a promising approach for guiding large language models (LLMs) through multi-step reasoning by providing step-level feedback during inference. However, our evaluation across 7 LLMs reveals a failure mode: while PRMs improve performance for instruct mathematical models, they fail to enhance and sometimes degrade reasoning model performance. Through systematic analysis with linear probes, we identify distinct reward prediction patterns that differentiate reasoning from non-reasoning model outputs. To understand this mechanism, we train Sparse Autoencoders on the Qwen2.5-Math-PRM and analyze reasoning features. Our analysis reveals that 80% of these features respond to formatting artifacts (whitespace patterns, Unicode tokens, punctuation) rather than mathematical content. Reasoning model outputs exhibit distinct metacognitive patterns absent from standard mathematical solutions. This explains why they lead to unreliable reward estimation. Our findings expose a fundamental limitation in applying existing reward models to reasoning systems and provide mechanistic insights into this failure mode. We release our trained SAEs to facilitate future research into reward model interpretability.

Fine-tuning LLMs introduces many important behaviors, such as instruction-following and safety alignment. This makes it crucial to study how fine-tuning changes models’ internal mechanisms. Sparse Autoencoders (SAEs) offer a powerful tool for interpreting neural networks by extracting concepts (features) represented in their activations. Previous work observed that SAEs trained on base models transfer effectively to instruction-tuned (chat) models, attributed to activation similarity. In this work, we propose *feature drift* as an alternative explanation: the feature space remains relevant, but the distribution of feature activations changes. In other words, fine-tuning recombines existing concepts rather than learning new ones. We validate this by showing base SAEs reconstruct both base and chat activations comparably despite systematic differences, with individual features exhibiting clear drift patterns. In a refusal behavior case study, we identify base SAE features that drift to activate on harmful instructions in chat models. Causal interventions using these features confirm that they mediate refusal. Our findings suggest that monitoring how existing features drift, rather than searching for entirely new features, may provide a more complete explanation of how fine-tuning changes model capabilities.

Machine Unlearning (MU) is critical for removing private or hazardous information from deep learning models. While MU has advanced significantly in unimodal (text or vision) settings, multimodal unlearning (MMU) remains underexplored due to the lack of open benchmarks for evaluating cross-modal data removal. To address this gap, we introduce CLEAR, the first open-source benchmark designed specifically for MMU. CLEAR contains 200 fictitious individuals and 3,700 images linked with corresponding question-answer pairs, enabling a thorough evaluation across modalities. We conduct a comprehensive analysis of 11 MU methods (e.g., SCRUB, gradient ascent, DPO) across four evaluation sets, demonstrating that jointly unlearning both modalities outperforms single-modality approaches. The dataset is available at [link](https://huggingface.co/datasets/therem/CLEAR)

This paper presents a system developed for the Clinical NLP 2024 Shared Task, focusing on reliable text-to-SQL modeling on Electronic Health Records (EHRs). The goal is to create a model that accurately generates SQL queries for answerable questions while avoiding incorrect responses and handling unanswerable queries. Our approach comprises three main components: a query correspondence model, a text-to-SQL model, and an SQL verifier.For the query correspondence model, we trained a logistic regression model using hand-crafted features to distinguish between answerable and unanswerable queries. As for the text-to-SQL model, we utilized T5-3B as a pretrained language model, further fine-tuned on pairs of natural language questions and corresponding SQL queries. Finally, we applied the SQL verifier to inspect the resulting SQL queries.During the evaluation stage of the shared task, our system achieved an accuracy of 68.9 % (metric version without penalty), positioning it at the fifth-place ranking. While our approach did not surpass solutions based on large language models (LMMs) like ChatGPT, it demonstrates the promising potential of domain-specific specialized models that are more resource-efficient. The code is publicly available at https://github.com/runnerup96/EHRSQL-text2sql-solution.